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This study aims to analyze the process flow of skilled consultants who utilize production improvement know-how and digital technology to enhance production systems in external companies. The concept of a Digital Triplet (D3), which expands the authors’ Digital Twin framework to include the intelligent activity world, is adopted as it aligns with this study’s objective. Given the complexity of the problems faced by production system consulting and the resulting inadequacy of reusing decision-making processes of skilled engineers based on the Generalized Process Model (GPM) using D3, a production system consulting modeling method is proposed. This method incorporates the Generalized Production System Consulting Process Model (GCPM) to generalize the production system consulting process. Using the proposed method, a case study focusing on energy-saving improvements was conducted to describe and analyze the consulting process of skilled consultants. The results show that the proposed method effectively captures the process flow of skilled consultants while considering the iterative structure of the GCPM. Additionally, utilizing the GCPM enables a comprehensive view of the entire process, facilitating an understanding of how knowledge and tools are utilized in various contexts.

Manufacturing processes can be cited as significant research areas when examining infrastructure systems and infrastructure, as they are inextricably linked to both. Examples include automobile manufacturing, the production of traffic signs, etc. Connecting and utilizing Industry 4.0 technologies and processing simulation solutions to address industry challenges, such as process optimization and fault detection, are gaining in popularity. Cyber-physical systems and digital twins connect the physical and cyber worlds to enable intelligent manufacturing capabilities, increased system flexibility, decreased manufacturing-cycle times, and improved quality. This paper presents a solution that improves the synchronization between the real (physical) and simulation (digital) layers, using discrete-event-driven simulations to create more efficient and accurate digital-twin environments. Using a combination of inexpensive commercial microcontrollers and an inertial-measurement-unit sensor to enhance a standard programmable logic controller process, a discrete-event-simulation-based digital layer is updated in real time to produce a live digital twin. The system can accurately identify and track products throughout the production cycle while simultaneously updating the digital twin in real time. Even independently, the algorithm running on the microcontroller can be used to gather the input parameters required for the simulation of production processes. The implemented environment can serve as a suitable testing ground for investigating the practical applicability of digital-twin solutions.

Big Data (BD)-driven business intelligence (BI) is considered to be a new development stage within industrial engineering management to achieve higher business value creation. The article provides extensive research of BD-driven approaches to identify strategic knowledge and value-creating components within Internet of Things (IoT) manufacturing. Due to previous research studies focused mostly on partial solutions and lack a holistic view, this article topic is still far from being sufficiently explored and resolved. The article presents the results of an extensive research study of contemporary professional literature with a specific aim to provide a more holistic and systematical view on the current state and future trends in this issue. To solve this high complex research problem, both quantitative and qualitative research methodology approaches are used. The article is primarily based on a systematic literature review covering 187 essential and 36 additional current research papers within the Web of Science and Scopus database from 2012 to the present. Research findings identify the seven key strategic elements determining that Big Data analytics (BDA) can become a real and tremendous business value creation driver within IoT intelligent manufacturing. These research results provide important conclusions and implications for challenging future investigations into BDA and BI systems in the context of advancing the Industry 4.0 revolution. In conclusion, we propose a final suggestion in the form of a strategic future research chain from IoT through BDA to value creation based on BD intelligence within IoT manufacturing, whereas seven strategic elements might play a crucial. Author, Internal Grant Agency of Tomas Bata University in Zlin [IGA/FaME/2020/009-Process]

Publisher Copyright: © 2013 IEEE. This work was supported in part by the European Union (EU) Project Advanced Manufacturing Solutions Tightly Aligned With Business Needs (AVANGARD), and in part by the European Union's Horizon 2020 Research and Innovation Program under Grant 869986. The scheduling applied to manufacturing represents a huge opportunity for companies to stand out in a world of fast and big changes. Having a reliable scheduling system will allow factories to deal with the significant demand for highly customized products. Although manufacturing scheduling has been deeply studied for decades, there is still a gap between academia and industry, namely because the lack of flexibility and homogeneity among scheduling solutions, which makes them very use case-oriented. Furthermore, the absence of standardization is also making it difficult to implement smart scheduling solutions in industrial scenarios. Thus, this work presents a set of requirements and design principles based on axiomatic design concept, to make the first steps to standardize the designing and development of manufacturing scheduling solutions in the context of Industry 4.0. At the end, is presented a scheduling generic framework targeting smart manufacturing and evaluated in a practical use case. publishersversion published

Nowadays, global resources management intersects with collaboration and Industry 4.0 paradigms, namely for collaboratively managing cyber-physical systems. Only organizations that cooperate with their business partners, along with their suppliers and remaining stakeholders, including their clients, will be able to permit and promote the much-needed endowing of agility, effectiveness, and efficiency in their management processes. For that, suitable decision-making paradigms, along with underlying approaches, will be needed, in order to properly fulfil current companies’ decision requirements and practices. The main purpose of this paper is to show that this can be achieved by applying combined global resources management paradigms and approaches, to reach collaboration further supported by recent technology made available through Industry 4.0. In doing so, the interaction of companies and stakeholders, supported by appropriate networks, along with varying kind of other communication and problem-solving technology, will enable them to promote and reinforce interoperation to reach the best-suited management decisions, by considering each ones’ objectives and priorities, along with common goals. To this end, in this paper, a systematic literature review methodology is used to synthetize the main contributions about the relation of these domains. The study carried out and the results obtained permitted us to realize that dynamic, integrated, distributed, parallel, intelligent, predictive, and real-time-based decision paradigms are of the upmost importance currently, but are still just scarcely being combined, which is suggested though its encompassing through a proposed collaborative management framework that is recommended to be applied, either in industry or academia, to improve global resources management processes and practices., The project is funded by the FCT—Fundação para a Ciência e Tecnologia through the R&D Units Project Scope: UIDB/00319/2020, and EXPL/EME-SIS/1224/2021.

Cyber-Physical Production Systems (CPPSs), z. B. automatisierte Autofabriken, sind flexible Systeme, die aus intelligenten Maschinen und Software bestehen und sich selbstständig an wechselnde Anforderungen anpassen können. Ein CPPS produziert verwandte Produkte mit einer gemeinsamen Plattform, aber unterschiedlichen Eigenschaften, z. B. Autos in verschiedenen Konfigurationen, und bildet so eine Produktfamilie. Bei der Planung von CPPSs entwerfen die Ingenieure das CPPS unter Berücksichtigung von Produktfamilien. Daher modellieren sie die Produktionsprozesse für jedes Produkt und ermitteln deren Gemeinsamkeiten und produktspezifische Variabilität für eine integrierte Sicht auf die CPPS. Die unzureichende Wissensdarstellung der Produktionsprozesse mit ihrer Variabilität macht diese Identifizierung zeitaufwändig, fehleranfällig und schwer reproduzierbar. Wenn sich die Produktfamilie weiterentwickelt, d. h. wenn sich die Anforderungen an das Produkt oder das CPPS ändern, führen die Ingenieure die Identifizierung der Variabilität in der Regel von Grund auf neu durch. Darüber hinaus erschwert eine begrenzte Werkzeug unterstützung mit unklarer Semantik und ohne maschinenlesbare Struktur die Analyse und das Testen und verhindert effiziente Prozessoptimierungen und Qualitätssicherung. Diese Arbeit verwendet die Design-Science-Methodik, um den Model Variant Analysis (MVA)-Ansatz zu entwickeln, der aus (i) dem MVA-Metamodell zur Verbesserung der formalen Darstellung von Produktionsprozessen, (ii) einer Methode zur Identifizierung und Verbesserung von Merkmalen und (iii) einer Variabilitätsanalyse auf der Grundlage des Product-Process-Resource (PPR)-Ansatzes besteht. In dieser Arbeit wird der MVA-Ansatz qualitativ evaluiert, wobei die identifizierten Anforderungen mit den bereitgestellten Fähigkeiten verglichen werden. Dazu werden typische Anwendungsfälle aus dem CPPS-Engineering verwendet und es wird untersucht, wie diese vom MVA-Ansatz profitieren können. Die Auswertung zeigt, dass der MVA-Ansatz Fähigkeiten bietet, die CPPS-Ingenieure bei ihren typischen Planungsaktivitäten unterstützen. Die Evaluierung zeigt auch, dass das MVA-Metamodell sowie die Identifizierungs- und Analyseansätze für andere Modelltypen geeignet sind. Die Ergebnisse dieser Arbeit sollen ein wohldefiniertes Metamodell und strukturierte Modellanalyseansätze für Ingenieure bereitstellen. Allerdings ist eine empirische Evaluierung mit Branchenexperten und einer breiteren Palette von Anwendungsfällen erforderlich, um die Erkenntnisse zu erhärten., Cyber-Physical Production Systems (CPPSs), such as automated car manufacturing plants, are flexible systems, combined of smart machines and software, that can adapt to changing requirements autonomously. A CPPS produces related products with a shared platform but different characteristics, e.g., cars in different configurations, building a product family. In CPPSs planning, engineers design the CPPS considering product families. Therefore, they model production processes for each product and identify their commonalities and product-specific variability for an integrated view on the CPPS.Insufficient knowledge representation of production processes with their variability makes this identification time-consuming, error-prone, and hard to reproduce. Furthermore, if the product family evolves, i.e., product or CPPS requirements change, engineers typically conduct the variability identification task from scratch. On top, limited tool support with unclear semantics and without machine-readable structure makes analysis and testing difficult, preventing efficient process optimizations and quality assurance.This thesis uses the Design Science methodology to develop the Model Variant Analysis (MVA) approach consisting of (i) the MVA metamodel to improve the formal representation of production processes, (ii) a feature identification and improvement method, and (iii) a variability analysis based on the Product-Process-Resource (PPR) approach. The thesis evaluates the MVA approach in a qualitative evaluation, comparing identified requirements with the provided capabilities. Therefore, this thesis utilizes typical use cases from CPPS engineering and investigates how they can benefit from the MVA approach. The evaluation indicates that the MVA approach provides capabilities that aid CPPS engineers in their typical planning activities. The evaluation also shows that the MVA metamodel as well as the identification and analysis approaches are open for other types of models. The results of this thesis are envisioned to provide a well-defined metamodel and structured model analysis approaches for CPPS engineers. However, an empirical evaluation with industry experts and a broader range of use cases is required to harden the evidence.

The challenges for industrial companies in the area of factory planning and operation are characterised on the one hand by permanently shortening product life cycles and increasing product diversity. Furthermore, the demand for ecologically sustainable processes is growing and the complexity of production systems is increasing due to higher product complexity. This results in a complex decision-making space for companies within factory planning and factory operation which is difficult to plan. The advancing digitalisation can bring a great opportunity here. Modelling and simulation can create greater transparency in the context of planning and operation, and processes can be designed to be ecologically sustainable and efficient. Currently, research approaches in the context of factory planning and operation are focussing on the application and use of digital methods and tools of the Digital Factory (DF). However, the application is limited to individual areas in factory planning or factory operation. For this reason, this paper focuses on the design of a framework that addresses both factory planning and factory operation aspects and links them through modelling and simulation. Cyber-physical production systems (CPPS) can help here by mapping the individual modules within planning and operation using individual agents in agent-based simulation (AB). By linking planning and real data, the processes from planning and operation can be taken into account. From this, insights gained from planning can be simulated in an early phase and subjected to optimisation during operation. The cycle-oriented CPPS can be used on an ongoing basis by preparing the generic building blocks on the planning and operational sides through structured data acquisition and implementing them in the real world with the help of decision support from the virtual world.

Publisher Copyright: © 2013 IEEE. This research is supported by the Digital Manufacturing and Design Training Network (DiManD) project funded by the European Union through the Marie Skłodowska-Curie Innovative Training Networks (H2020-MSCA-ITN-2018) under grant agreement no. 814078 The concept of smart manufacturing has attracted huge attention in the last years as an answer to the increasing complexity, heterogeneity, and dynamism of manufacturing ecosystems. This vision embraces the notion of autonomous and self-organized elements, capable of self-management and self-decision-making under a context-aware and intelligent infrastructure. While dealing with dynamic and uncertain environments, these solutions are also contributing to generating social impact and introducing sustainability into the industrial equation thanks to the development of task-specific resources that can be easily adapted, re-used, and shared. A lot of research under the context of self-organization in smart manufacturing has been produced in the last decade considering different methodologies and developed under different contexts. Most of these works are still in the conceptual or experimental stage and have been developed under different application scenarios. Thus, it is necessary to evaluate their design principles and potentiate their results. The objective of this paper is threefold. First, to introduce the main ideas behind self-organization in smart manufacturing. Then, through a systematic literature review, describe the current status in terms of technological and implementation details, mechanisms used, and some of the potential future research directions. Finally, the presentation of an outlook that summarizes the main results of this work and their interrelation to facilitate the development of self-organized manufacturing solutions. By providing a holistic overview of the field, we expect that this work can be used by academics and practitioners as a guide to generate awareness of possible requirements, industrial challenges, and opportunities that future self-organizing solutions can have towards a smart manufacturing transition. publishersversion published

Cyber-physical production systems are constituted of various sub-systems in a production environment, from machines to logistics networks, that are connected and exchange data in real-time. Every sub-system consumes and generates data. This data has the potential to support decision making and optimization of production processes. To extract valuable information from this data, however, different data sources must be consolidated and analyzed. A Knowledge Graph (KG), also known as a semantic network, represents a net of real-world entities, i.e., machines, sensors, processes, or concepts, and illustrates their relationship. KG allows us to encode the knowledge and data context into a human interpretable form and is amenable to automated analysis and inference. This paper presents the potential of KG in manufacturing and proposes a framework for its implementation. The proposed framework should assist practitioners in integrating raw data from multiple data sources in production, developing a suitable data model, creating the knowledge graph, and using it in a graph application. Although the framework is applicable for different purposes, this work illustrates its use for supporting the quality assessment of products in a discrete manufacturing production line.

Continuous improvement of the fleet of technological equipment, in the conditions of existing production, is a difficult task, the solution of which must take into account many parameters, such as mechanization, automation and application of new technologies within the concept of Industry 4.0. One of the effective methods of improving the technical and economic indicators and reliability of production is the modernization of existing technological equipment, which is based on the need to use modern methods of development and implementation of information technology, such as Industrial Internet of Things, cyber-physical production systems. The subject of this study is the testing of methods for developing additive cyber design for automation of complex industrial equipment. The goal of this article is to develop a cyber-physical production control system for the modernization of the DA2238B hydraulic press. To achieve this goal it is necessary to solve the following tasks: to analyze the technical characteristics and control system of the hydraulic press DA2238B; to carry out research of the scheme of hydraulic basic and, taking it into account, to choose sensors and executive mechanisms, and also operating modes; to develop the scheme of inclusion of the hydro equipment; to develop an automated control system based on a modern single-board computer and to develop an intuitive additive cyber-design of the operator interface; to conduct experimental research. Conclusions: as a result of research, a block diagram was developed and an automated control system based on LattePanda was implemented. Selected pressure and temperature sensors for control of pressing processes, which allowed to ensure accurate compliance with the requirements of the technological process. An automated operator's workplace has been developed, in which touch control based on additive cyber-design has been implemented. The developed control system provides: collection and analysis of production data on the basis of which it is possible to improve production technology and to provide forecasting of achievement "Lean Manufacturing"., Постоянное совершенствование парка технологического оборудования, в условиях существующего производства, является сложной задачей, при решении которой необходимо учитывать много параметров, таких как механизация, автоматизация и применение новых технологий в рамках концепции Industry 4.0. Одним из эффективных методов повышения технико-экономических показателей и надежности производства является модернизация существующего технологического оснащения, которая основывается на необходимости использования современных методов разработки и внедрения информационных технологий, таких как Industrial Internet of Things, кибер-физических производственных систем. Предметом данного исследования является апробация методов разработки аддитивного кибер-дизайна для автоматизации сложного промышленного оборудования. Целью данной статьи является разработка кибер-физической производственной системы управления, для модернизации пресса гидравлического ДА2238Б. Для достижения поставленной цели необходимо решить следующие задачи: провести анализ технических характеристик и системы управления пресса гидравлического ДА2238Б; провести исследование схемы гидравлической принципиальной на базе которой выбрать датчики и исполнительные механизмы, а также режимы работы; разработать схему включения гидро- оборудования; разработать автоматизированную систему управления на базе современного одноплатного компьютера и разработать интуитивно понятный аддитивный кибер-дизайн интерфейса оператора; провести экспериментальные исследования. Выводы: в результате исследований была разработана структурная схема и реализована автоматизированная система управления на базе LattePanda. Выбраны датчики давления и температуры для контроля процессов прессования, что позволило обеспечить точное соответствие требованиям технологического процесса. Разработано автоматизированное рабочее место оператора, в котором реализовано сенсорное управление на базе аддитивного кибер-дизайна. Разработанная система управления обеспечивает: сбор и анализ производственных данных, на базе которых можно усовершенствовать технологию производства и обеспечить прогнозирование достижения "Lean Manufacturing"., Постійне вдосконалення парку технологічного обладнання, в умовах існуючого виробництва, є складним завданням, при вирішенні якого необхідно враховувати багато параметрів, таких як механізація, автоматизація і застосування нових технологій в рамках концепції Industry 4.0. Одним з ефективних методів підвищення техніко-економічних показників та надійності виробництва є модернізація існуючого технологічного оснащення, яка ґрунтується на необхідності використання сучасних методів розробки і впровадження інформаційних технологій, таких як Industrial Internet of Things, кібер-фізичних виробничих систем. Предметом даного дослідження є апробація методів розробки адитивного кібер-дизайну для автоматизації складного промислового обладнання. Метою даної статті є розробка кібер-фізичної виробничої системи керування, для модернізації преса гідравлічного ДА2238Б. Для досягнення поставленої мети необхідно вирішити такі завдання: провести аналіз технічних характеристик і системи керування преса гідравлічного ДА2238Б; провести дослідження схеми гідравлічної принципової на базі якої обрати датчики і виконавчі механізми, а також режими роботи; розробити схему включення гідро обладнання; розробити автоматизовану систему керування на базі сучасного одноплатного комп'ютера і розробити інтуїтивно зрозумілий адитивний кібер-дизайн інтерфейсу оператора; провести експериментальні дослідження. Висновки: в результаті досліджень було розроблено структурну схему та реалізовано автоматизовану систему керування на базі LattePanda. Обрані датчики тиску і температури для контролю процесів пресування, що дозволило забезпечити точне дотримання вимог технологічного процесу. Розроблено автоматизоване робоче місце оператора, в якому реалізовано сенсорне управління на базі адитивного кібер-дизайну. Розроблена система керування забезпечує: збір та аналіз виробничих даних, на базі яких можна вдосконалити технологію виробництва і забезпечити прогнозування досягнення "Lean Manufacturing".

With the concepts of Industry 4.0 and smart manufacturing gaining popularity, there is a growing notion that conventional manufacturing will witness a transition toward a new paradigm, targeting innovation, automation, better response to customer needs, and intelligent systems. Within this context, this review focuses on the concept of cyber–physical production system (CPPS) and presents a holistic perspective on the role of the CPPS in three key and essential drivers of this transformation: data-driven manufacturing, decentralized manufacturing, and integrated blockchains for data security. The paper aims to connect these three aspects of smart manufacturing and proposes that through the application of data-driven modeling, CPPS will aid in transforming manufacturing to become more intuitive and automated. In turn, automated manufacturing will pave the way for the decentralization of manufacturing. Layering blockchain technologies on top of CPPS will ensure the reliability and security of data sharing and integration across decentralized systems. Each of these claims is supported by relevant case studies recently published in the literature and from the industry; a brief on existing challenges and the way forward is also provided.

Cyber-physical systems become more complex, therewith production lines become more complex in the smart factory. Every employed system produces high amounts of data with unknown dependencies and relationships, making incident reasoning difficult. Context-aware fault diagnosis can unveil such relationships on different levels. A fault diagnosis application becomes context-aware when the current production situation is used in the reasoning process. We have already published TAOISM, a visual analytics model defining the context-aware fault diagnosis process for the Industry 4.0 domain. In this article, we propose the Flourish dashboard for context-aware fault diagnosis. The eponymous visualization Flourish is a first implementation of a context-displaying visualization for context-aware fault diagnosis in an Industry 4.0 setting. We conducted a questionnaire and interview-based bilingual evaluation with two user groups based on contextual faults recorded in a production-equal smart factory. Both groups provided qualitative feedback after using the Flourish dashboard. We positively evaluate the Flourish dashboard as an essential part of the context-aware fault diagnosis and discuss our findings, open gaps, and future research directions.

Anomaly detection is essential for realizing modern and secure cyber-physical production systems. By detecting anomalies, there is the possibility to recognize, react early, and in the best case, fix the anomaly to prevent the rise or the carryover of a failure throughout the entire manufacture. While current centralized methods demonstrate good detection abilities, they do not consider the limitations of industrial setups. To address all these constraints, in this study, we introduce an unsupervised, decentralized, and real-time process anomaly detection concept for cyber-physical production systems. We employ several 1D convolutional autoencoders in a sliding window approach to achieve adequate prediction performance and fulfill real-time requirements. To increase the flexibility and meet communication interface and processing constraints in typical cyber-physical production systems, we decentralize the execution of the anomaly detection into each separate cyber-physical system. The installation is fully automated, and no expert knowledge is needed to tackle data-driven limitations. The concept is evaluated in a real industrial cyber-physical production system. The test result confirms that the presented concept can be successfully applied to detect anomalies in all separate processes of each cyber-physical system. Therefore, the concept is promising for decentralized anomaly detection in cyber-physical production systems.

Production scheduling can be affected by many disturbances in the manufacturing system, and consequently, the feasible schedules previously defined became obsolete. Emerging of new technologies associated with Industry 4.0, such as Cyber-Physical Production Systems, as a paradigm of implementation of control and support in decision making, should embed the capacity to simulate different environment scenarios based on the data collected by the manufacturing systems. This paper presents the evaluation of environment dynamics effect on production scheduling, considering three scheduling models and three environment scenarios, through a case study. Results show that environment dynamics affect production schedules, and a very strong or strong positive correlation between environment dynamics scenarios and total completion time with delay, over three scheduling paradigms. Based on these results, the requirement for mandatory inclusion of a module for different environment dynamics scenarios generation and the corresponded simulations, of a Cyber-Physical Production Systems architecture, is confirmed., This work has been supported by FCT -Fundacao para a Ciencia e Tecnologia within the R&D Units Project Scope: UIDB/00319/2020.

Integration of additive manufacturing (AM) into series production demands for comprehensive methods and tools. An agent-based process chain simulation framework is proposed to embed AM into a cyber-physical production system. A multi-dimensional evaluation supports the planning of AM process chains as well as their day-to-day operation. The detailed evaluation possibilities down to product and single simulation event level are illustrated by a case study, in which an automotive exterior part is produced using the multi-jet fusion technology. Multiple possible process chain configurations are assessed based on their modeled key performance indicators, summarized in a energy value stream map.

Designing meat processing systems can be a complex process since this type of cyber-physical production system has two distinguishing properties: divergent product routings due to the cutting up of meat (known as co-production), and variability in the outcome of production processes (known as random yield). In this paper, we present a model-based design framework for meat processing systems, which can be used for performance prediction and system validation of a designed system. We show how an agent-based production control layer can be integrated into a model-based design architecture for meat processing systems. Finally, this framework describes how to route products through a system with co-production and random yield.

International audience; Despite the development and application of new digital solutions in the production industry, the human operator is still essential in the production chain monitoring and control processes. In this context, some activities can be crucial for the human operator like, for example, drift diagnosis in production control process. It requires attention and experience and can be assisted by Decision Support System (DSS) to guide operators indecision-making in industrial production process control. Drift diagnosis process is a challenging problem in this context and artificial intelligence technologies are promising to tackle this issue. In this paper, we propose a new approach of DSS for drift diagnosis. The proposed approach is built upon a literature review on drift concept, drift detection methods and failure diagnosis approaches. This multi-model approach is designedto address all the diagnostics tasks of production systems and is based on Machine Learning (ML) algorithms to model the behavior of production systems, a knowledge-based model to integrate human experiences and a data-driven model to combine historical data from sensors. When the drift occurs, the proposed DSS can help human operator to determine drift causes and to suggest corrective actions. This article also provides guidelines about the design of a decision support system to support human operators in complex decision activities.

In recent years, the introduction of Industry 4.0 technologies in the manufacturing landscape promoted the development of smart factories characterised by relevant socio-technical interactions between humans and machines. In this context, understanding and modelling the role of humans turns out to be crucial to develop efficient manufacturing systems of the future. Grounding on previous researches in the field of Human-in-the-Loop and Human Cyber-Physical Systems, the paper aims at contributing to a deep reflection about human-machine interaction in the wider perspective of Social Human-in-the-Loop Cyber-Physical Production Systems, in which more agents collaborate and are socially connected. After presenting an evolution of manufacturing control organisations, an architecture to depict social interactions in smart factories is proposed. The proposed architecture contributes to the representation of different human roles in the smart factory and the exploration of both hierarchical and heterarchical data-driven decision-making processes in manufacturing.

With the inception of high-speed internet data services and ever-growing technical advancement in manufacturing technology, the integration of production systems and the internet of things to produce different types of jobs via cloud computing has become possible. The internet-enabled advanced automatic production systems can be referred to as the cyber-physical production systems (CPPS). The use of CPPS via cloud computing and the internet of things (IoT) can offer high productivity and high flexibility for the production of jobs in a dynamic production environment with varying specifications. The aim of this paper is to present a generalized synergic framework between different production facilities locating at different geographical locations to realize an energy-saving and efficient cyber-physical production system for the production of different types of jobs in the context of the industry 4.0 and beyond. In addition to the above, the study also identifies a need to address large scale multi-objective optimization issues to make the best decisions for different combinatorial production scenarios by using CPPS that are functioning in smart production facilities at different geographical locations.

Modelling and reconfiguration of production processes require knowledge across different domains. This in-depth knowledge is necessary to avoid possible side effects that could threaten the production plant, the workpiece or the worker. Therefore, process modelling approaches allow adding additional data to the steps of a process. Such additions can be constraints, which need to be fulfilled before a step can be executed. Upon reconfiguration of production processes, these constraints need to be validated to ensure that the objective of the process is still met. However, this task demands expertise in the field of process modelling as well as in the domain of the production process and the production plant. To the best of our knowledge, state-of-the-art production process modelling approaches are unable to determine the semantic validity of a reconfigured production process. In this paper, we introduce a domain-specific modelling language dedicated to model and validate constraints between production steps.With this approach, we aim to assist the operator in reconfiguring production processes. We evaluate this approach in three case studies and show that our approach can detect violated constraints in production processes.

This workshop focuses on Software Engineering in Cyber-Physical Production Systems (SECPPS). SECPPS is an interactive workshop opened by keynotes and lightning talks, followed by project showcase presentations, and concluded by extensive discussions in break-out groups. The main output of SECPPS 2022 is an updated research roadmap as well as concrete networking activities to further grow the community in this interdisciplinary field.

Volume 2022, Issue 06, How can a decentralized manufacturing control concept be implemented and bring about success? To answer this question, a cyber-physical test field is under development at the University of Applied Sciences Hamburg, Germany. This article gives insight into the test field for decentral manufacturing control which can be represented as a “swimming pool” model. This analogy focuses on the freedom of the production orders to decentrally negotiate among each other on scarce resources such as the machines and the transport capacity. The transport means are automated guided vehicles (AGV) which allow the production orders to move freely within the limits of the pool, namely the production itself. Production orders should cross the pool from order release on the left side to order completion on the right side of the pool using the AGVs. Though, production orders may negotiate independently about the scarce machining capacities. Equipped with given customer priorities, a production order may want to bargain for a swimming lane with expensive but fast CNC machining capacity, while a different order may prefer a parallel swimming lane offering an inexpensive but slow conventional machining service. – Our research in this field has shown that a well-organized communication process between all entities in the system is crucial to implementing such a decentrally organized swimming pool model. Therefore, this article focuses on describing a negotiation mechanism to support decision making between those entities. Technical enablers such as an MQTT communication broker and a suitable simulation environment are supporting the overall concept. It is to be learned to what extent all decentralized entities of the cyber-physical production system act in a resource-conserving and value-adding manner., Wie ein dezentrales Fertigungssteuerungskonzept realisiert werden und Erfolg herbeiführen kann, wirft noch immer Fragen auf. Daher wird an der Hochschule für Angewandte Wissenschaften Hamburg, Deutschland ein cyber-physikalisches Testfeld unter Verwendung von fahrerlosen Transportfahrzeugen (Automated Guided Vehicles AGV) entwickelt. Das Testfeld kann als ein "Swimmingpool"-Modell dargestellt werden. Diese Analogie beschreibt die Freiheit der Produktionsaufträge, sich mit Hilfe der AGVs dezentral innerhalb der Grenzen des "Pools", also der Produktion selbst, zu bewegen. Die Produktionsaufträge sollen den Pool von der Auftragsfreigabe auf der linken Seite bis zur Auftragsfertigstellung auf der rechten Seite des Pools durchqueren. Dabei können die Fertigungsaufträge unabhängig voneinander über die knappen Bearbeitungskapazitäten verhandeln. Ausgestattet mit bestimmten Kundenprioritäten kann ein Produktionsauftrag um eine Schwimmbahn mit teurer, aber schneller CNC-Bearbeitungskapazität verhandeln, während ein anderer Auftrag eine parallele Schwimmbahn mit preiswerter, aber langsamer konventioneller Bearbeitung bevorzugt. – Unsere Forschung auf diesem Gebiet hat gezeigt, dass ein gut organisierter Kommunikationsprozess zwischen allen Entitäten im System entscheidend für die Umsetzung eines solch dezentral organisierten Schwimmbadmodells ist. Daher konzentriert sich dieser Artikel auf die Beschreibung eines Verhandlungsmechanismus zur Unterstützung der Entscheidungsfindung zwischen diesen Entitäten. Technische Enabler wie ein MQTT-Kommunikationsbroker und eine geeignete Simulationsumgebung unterstützen das Gesamtkonzept. Es soll herausgefunden werden, inwieweit alle dezentralen Entitäten des cyber-physischen Produktionssystems (CPS) ressourcen-schonend und wertschöpfend agieren.

Kontext und Motivation. In “Multi-disciplinary Engineering” (MDE) Umgebungen, ist das Wissen über unterschiedliche Aspekte eines System, wie “Product, Process and Resources” (PPR) Spezifikationen, als auch Umweltfaktoren und deren Einflüsse auf das Produkt und den Prozess häufig auf Experten unterschiedlicher Disziplinen verteilt, die divergierende Blickwinkel auf das System haben. Dadurch fällt es oft schwer, dieses Wissen an einem Ort zu konzentrieren. Testen ist eine Methode, um beabsichtigte Effekte zu erforschen, wie zum Beispiel ob das Produkt die erwarteten Anforderungen erfüllt, ob der Produktionsprozess die notwendige Durchsatzleistung erbringt, oder Risiken, die zu unerwünschten Effekten führen, zu überprüfen. Traditionelle Testmethoden betrachten of nur Effekte, die einer bestimmten Disziplin zuzuordnen sind. In MDE’s, etwa bei “Cyber-physical Production Systems” (CPPS) ist die Formulierung und Validierung von Ursachen-Wirkung-Hypothesen eine komplexe Aufgabe. Dadurch bleiben Risiken, auch bei der Verwendung von systematischen Testmethoden, oft unentdeckt. Ziele. Das primäre Ziel dieser Studie ist der Entwurf eines Ansatzes, der die systematische Testplanung, Messung und Analyse in MDE Umgebungen, auf Basis der Ursachen-Wirkung-Analyse, ermöglicht. Methoden. Basierend auf dem Ziel wurden die folgenden Forschungsfragen formuliert:(1) Welche Risikotreiber motivieren Multi-Aspekt Risikobasiertes Testen von Ursachen-Wirkung-Hypothesen zwischen Eigenschaften des Produktionsprozesses und Parametern/Einflüsse der Produktionsressourcen? Das Subset der Risikotreiber wurde durch den Forschungsüberblick und Workshop-Veranstaltungen mit Fachexperten abgeleitet.(2) Welches Wissensmodell kann Multi-Aspekt Ursachen-Wirkung-Hypothesen zwischen Eigenschaften des Produktionsprozesses und Parametern/Einflüsse der Produktionsressourcen repräsentieren? Aufbauend auf der “Failure Modes and Effects Analysis” (FMEA) zur Unterstützung von systematischem und effizientem Risikomanagement, “PPR Asset Network” (PAN) Metamodell zur Beschreibung einer CPPS Struktur und “CPPS Risk Assessment (CPPS-RA) Metamodell und Methode die FMEA, PAN und Ursachen-Wirkung-Analyse Aspekte verbindet, erweitert um testrelevante Aspekte, wurde das “Multi-aspect Test Case Specification” (MATCS) Metamodell erstellt und fachspezifische Wissensmodelle abgeleitet. (3) Welche Prozessschritte sind zur Unterstützung von Multi-Aspekt-Testung in “CPPS” und Softwaretechnik erforderlich? Basierend auf dem “Test Driven Development” (TDD) Ansatz zur Unterstützung von Software Design, der “Equivalence Class Partitioning” (ECP) Technik zur Aufteilung des Testraumes in Datenklassen und Gherkin Notation zur einfachen repräsentation von automatisieren Tests, wurde MATCS konzipiert. Die Risikoteiber, das MATCS Metamodell und die MATCS Methode repräsentieren in Kombination den MATCS Ansatz. Um die Forschungsfragen zu beantworten, wurde der “Design Science Research” Ansatz angewandt. Ergebnisse. MATCS führt das Subset an fachspezifischen Risikotreibern, das MATCS Metamodel und Methoden ein. Die Evaluierung des Ansatzes zeigt aussichtsreiche Resultate im Kontext von zwei Fallstudien in den Bereichen von CPPS und Softwaretechnik.Der Ansatz unterstützt die Fachexperten bei der Spezifikation von effizienten Testfällen für die Validierung der Ursachen-Wirkung-Hypothesen, der Definition des Umfangs und Limitationen der Testautomatisierung und macht das Experimentieren für Systemverbesserungen systematischer im Vergleich zu traditionellen Ansätzen. Ein PAN ist die Voraussetzung für eine effiziente Anwendung des Ansatzes. Fazit. MATCS hilft dabei, das implizite Wissen der multi-disziplinären Fachexperten explizit zu machen und schlägt weitere Schritte für die Qualitätsexperten vor, um Multi-Aspekt Testfälle zu spezifizieren und sowohl Umfang als auch Limitierungen für die Testautomatisierung zu definieren. Die initiale PAN Definition erfordert zwar Mehraufwand, macht sich aber in zukünftigen Iterationen im Kontext einer kontinuierlichen Verbesserungsstrategie bezahlt., Context and Motivation. In Multi-disciplinary Engineering (MDE) environments, knowledge on different aspects of the system, such as product, process and resources (PPR) specifications, as well as environmental factors, and their influence on the product and process quality is often distributed among experts coming from multiple disciplines and having different views on the system, and has been found hard to concentrate in one place. Testing is a method to explore intended effects, such as whether a product matches expected requirements, a production process meets performance goals, as well as to check for risks, which could cause undesired effects. Traditional testing methods often consider causes responsible for the effects coming from one discipline. However, in MDEs, such as Cyber-physical Production Systems (CPPS) engineering, due to their multi-disciplinary nature, systematic formulation and validation of cause-effect hypotheses is a complex task, therefore risks often remain undiscovered by systematic testing methods. Objectives. The primary objective of this study was to design an approach to enable systematic test planning, measurement and analysis in MDE environments based on cause-effect analysis. Methods. Based on the goal, the following research questions were formulated: (1) Which risk drivers motivate multi-aspect risk-based testing of cause-effect hypothesesbetween production process characteristics and production resource parameters/influences? The sub-set of risk drivers was derived from the literature review and workshops with domain experts. (2) What knowledge model can represent multi-aspect cause-effect hypotheses betweenproduction process characteristics and production resource parameters/influences? Built on the Failure Modes and Effects Analysis (FMEA) to support systematic and efficient risk management, PPR Asset Network (PAN) meta-model to describe a CPPS structure, and CPPS Risk Assessment (CPPS-RA) meta-model and method which connects FMEA, PAN and cause-effect analysis aspects, extended with test related aspects, Multi-aspect Test Case Specification (MATCS) meta-model was designed and domain specific knowledge models were derived. (3) What are the process steps to support multi-aspect testing in CPPS and softwareengineering? Based on the Test Driven Development (TDD) approach to support system design, Equivalence Class Partitioning (ECP) technique to divide the test space into classes of data, and Gherkin notation to allow easy representation of automated tests, MATCS was designed. Risk drivers, MATCS meta-model and MATCS method combined represent the MATCS approach. In order to answer the research questions, the Design Science Research approach, supported by a literature review was followed. MATCS was evaluated in CPPS and software engineering industrial case studies, which included workshops and experiments. Results. MATCS introduced the sub-sets of domain specific risk drivers, MATCS meta-model and method. The evaluation of the approach has shown promising results in context of two case studies, in CPPS and software engineering domains. The approach supports the domain experts in efficient test case specification for cause-effect hypotheses validation, definition of scope and limitations for test automation and makes experimentation for system improvements more systematic in comparison to the traditional approach. High quality PAN is the prerequisite for efficient application of the approach. Conclusion. MATCS helps to make the implicit multi-disciplinary expert knowledge explicit and suggests steps to the quality experts allowing them to specify multi-aspect test cases and define the scope and limitations for test automation. The initial PAN definition requires considerable effort, however, it is expected to pay off in future iterations in context of continuous integration strategy.

Volume 2022, Issue 18, The trend towards heterogeneous, decentral systems in intralogistics results in the need for a concept to describe and virtualize assets to enable their interaction. The multi-layer concept of Cyber-Physical Intralogistics Systems (CPIS) is introduced. The system description (descriptive layer) defines the structure of the digital twins and the communication (virtual layer) of physical (robots, periphery) and logical assets (control systems, simulations). To implement this concept, an experimental environment was developed at the Institute for Material Handling and Logistics and the Karlsruhe Institute of Technology. It consists of physical components, such as models of mobile robots or manipulators, and further periphery, such as racks and charging stations. The environment is supplemented by simulations and control software. Use cases for CPIS are to be implemented and tested in this environment. Due to the easily accessible hardware components and the possible scaling of the systems in the simulation, implementation cycles can be reduced, and results can be achieved quickly without requiring a real-world intralogistics system. CPIS can be used to initialize an automated charging process or to exchange perceived position data of system participants. The primary goal is to enable a modular system, add new participants through plug-and-play, and make systems easily changeable., Der Trend zu heterogenen, dezentralen Systemen in der Intralogistik erfordert ein Konzept zur Beschreibung und Virtualisierung von Assets, um deren Interaktion zu ermöglichen. Das mehrschichtige Konzept der Cyber-Physical Intralogistics Systems wird eingeführt. Die Systembeschreibung (Descriptive Layer) definiert die Struktur der digitalen Zwillinge und der Kommunikation (Virtual Layer) von physischen (Roboter, Peripherie) und logischen Assets (Steuerungssysteme, Simulationen). Zur Umsetzung dieses Konzepts wurde am Institut für Fördertechnik und Logistiksysteme des Karlsruher Instituts für Technologie eine Versuchsumgebung entwickelt. Sie besteht aus physischen Komponenten, wie den Modellen mobiler Roboter oder Manipulatoren, und anderen intralogistischen Komponenten, wie Regalen und Ladestationen. Ergänzt wird die Umgebung durch Simulationen und Steuerungssoftware. In dieser Umgebung sollen Anwendungsfälle für CPIS implementiert und getestet werden. Durch die leicht zugänglichen Hardwarekomponenten und die mögliche Skalierung der Systeme in der Simulation können Implementierungszyklen verkürzt und Ergebnisse schnell erzielt werden, ohne dass eine reales Intralogistiksystem benötigt wird. CPIS können zur Initialisierung eines automatisierten Ladevorgangs oder zum Austausch von Positionsdaten aufgenommen durch Systemteilnehmer genutzt werden. Das primäre Ziel ist es, ein modulares System zu ermöglichen, neue Teilnehmer durch Plug-and-Play hinzuzufügen und Systeme wandelbar zu machen.

Volume 2022, Issue 18, Services and devices in a Cyber-Physical Production System (CPPS) can be provided and requested by multiple parties. Therefore, CPPS face challenges such as cross-company interactions, data security, and robustness against failure. Blockchain Technology (BCT) appears to be a suitable solution for these challenges, since it ensures immutable, trust-building, partly automatable, and transparent data handling and storage. In particular, BCT-based tokens enable the digital representation of objects such as products, tools and machinery or values and permissions and offer new possibilities for CPPS. Thus, this contribution focuses on the application of tokens in CPPS. Multiple use cases for tokens such as asset-backed tokens or utility tokens are presented. Based on this, a concept for an asset-backed token, representing material in a CPPS, is developed and demonstrated in a simulation model., Die Dienste und Geräte in einem Cyber-Physischen Produktionssystem (CPPS) können von mehreren Parteien bereitgestellt und nachgefragt werden. Daher stehen CPPS vor Herausforderungen wie unternehmensübergreifenden Interaktionen, Datensicherheit und Robustheit gegenüber Ausfällen. Die Blockchain-Technologie (BCT) scheint eine geeignete Lösung für diese Herausforderungen darzustellen, da sie eine unveränderbare, vertrauensbildende, teilweise automatisierbare und transparente Datenverarbeitung und -speicherung gewährleistet. Insbesondere BCT-basierte Tokens ermöglichen die digitale Darstellung von Objekten wie Produkten, Werkzeugen und Maschinen oder Werten und Berechtigungen und bieten neue Möglichkeiten für CPPS. Daher konzentriert sich dieser Beitrag auf die Anwendung von Tokens in CPPS. Es werden verschiedene Anwendungsfälle für Tokens vorgestellt, wie etwa Asset-Backed Tokens oder Utility Tokens. Darauf aufbauend wird ein Konzept für einen Asset-Backed Token entwickelt, der Material innerhalb eines CPPS repräsentiert, und in einem Simulationsmodell demonstriert wird.

Cyber-physical production systems are constituted of various sub-systems in a production environment, from machines to logistics networks, that are connected and exchange data in real-time. Every sub-system consumes and generates data. This data has the potential to support decision making and optimization of production processes. To extract valuable information from this data, however, different data sources must be consolidated and analyzed. A Knowledge Graph (KG), also known as a semantic network, represents a net of real-world entities, i.e., machines, sensors, processes, or concepts, and illustrates their relationship. KG allows us to encode the knowledge and data context into a human interpretable form and is amenable to automated analysis and inference. This paper presents the potential of KG in manufacturing and proposes a framework for its implementation. The proposed framework should assist practitioners in integrating raw data from multiple data sources in production, developing a suitable data model, creating the knowledge graph, and using it in a graph application. Although the framework is applicable for different purposes, this work illustrates its use for supporting the quality assessment of products in a discrete manufacturing production line.

Der Grad der Datenvollständigkeit ist eine der Hauptdimensionen von Datenqualität. Wenn daher Datenqualität als Ganzes behandelt werden soll, kann die Performance in Form der gewählten Kennzahlen durch die Betrachtung verschiedener Arten die Datenqualität zu steigern und mit fehlenden Werten umzugehen im positiven Sinne beeinflusst werden. Da eine gewisse Datenqualität die Voraussetzung für die erfolgreiche Umsetzung von industriellen Anwendungen beispielsweise in CPPS-Engineering ist, können durch das Feststellen der vielversprechendsten Methode im Umgang mit fehlenden Werten wesentlich bessere Ergebnisse erzielt werden. Demnach strebt diese Masterarbeit an, Einblicke sowohl von der theoretische Seite in Form von wissenschaftlichen Publikationen als auch von praktischer Seite, d.h. Personen, die im beruflichen Alltag mit unvollständigen Datensätzen konfrontiert sind, zu vergleichen. Das Ziel des Literature Review und der Onlineumfrage unter Praktikern war es, Informationen zu den jeweiligen Präferenzen im Umgang mit unvollständigen Daten zu sammeln. Die darauffolgenden statistischen Experimente wurden im Rahmen von jeweils einer Open-Source-Fallstudie und einer Industriefallstudie, der Sensordaten eines Produktionsprozesses zugrunde liegen, durchgeführt. Im Zuge dessen wurden sechs Methoden zur Handhabung fehlender Werte in Kombination mit vier Anteilen fehlender Daten anhand von Ausführungszeit der Algorithmen, direkter Bewertung sowie indirekter Bewertung, letztere in Bezug auf RMSE und MAE, evaluiert. Hierbei zeigte sich, das jene Methoden mit der jeweils besten und schlechtesten Performance auch über die Fallstudien hinweg sehr ähnlich waren, aber wesentlich von der gewählten Kennzahl abhängen., Data completeness is one of the main data quality dimensions. Thus, to tackle data quality as a whole, considering different ways to enhance data quality in incomplete datasets can illustrate the performance impact on the chosen metrics. As sufficiently high data quality is a prerequisite for industrial applications for instance in CPPS engineering, determining the most promising method to handle missing values can essentially contribute to obtain better results. Therefore, this thesis aimed to bring together insights from both the theoretical side of research publications and the missing handling methods suggested in comparison to the perspective of practitioners in jobs confronted with incomplete data. Both the literature review of research publications and the online survey among practitioners as to handling missing data were intended to obtain information on the respective preferences. The subsequent statistical experiments were carried out in line with an open-source case study (in the natural resources sector) and an industry case study (using sensor data from a manufacturing process) to evaluate six missing value handling methods in combination with four different missing data rates in the underlying datasets according to the execution time of the imputation methods, direct evaluation as well as indirect evaluation, the latter with respect to RMSE and MAE. It turned out that the best and worst performing methods are similar across the case studies, but depend on the target performance metric.

Preface of the SE���22 Workshop Proceedings

In den letzten Jahren gibt es ein schnell wachsendes Interesse an Automatisierung und KI-basierten Lösungen in der Fertigungsindustrie, was von einigen als die vierte industrielle Revolution beschrieben wird. Da die Komplexität der Produkte zunimmt und viele verschiedene Varianten von Produkten hergestellt werden, wird schnell ersichtlich, dass die gesamte Planung der Prozesse nicht effizient von menschlichen Arbeitskräften durchgeführt werden kann. Cyber-physische Produktionssysteme (CPPS) haben sich als eine mögliche Lösung für dieses Problem erwiesen. Diese Systeme werden als Systeme mit komplexen Interaktionen zwischen menschlichen Arbeitskräften (z. B. DesignerInnen, IngenieurInnen, HandwerkerInnen) und Maschinen konzipiert. CPPS, wie sie in der Literatur definiert wurden, sind jedoch noch immer nicht gut etabliert, und einer der Hauptgründe, der dafür genannt wird, ist der Mangel an existierender Software-Infrastruktur, um sie zu unterstützen. Ein Bereich, in dem noch nicht viele praktische Lösungen existieren, ist der Planungsschritt. Wie soll man ausgehend von der Spezifikation eines Endprodukts und einer Liste der verfügbaren Materialien und Akteure einen realisierbaren Plan für die Fertigung des Endprodukts erstellen? Diese Frage bleibt weitgehend unbeantwortet. In dieser Arbeit untersuchen wir den derzeitigen Forschungsstand zu CPPS und die von verschiedenen AutorInnen vorgeschlagene Forschungsagenda. Basierend auf diesen Erkenntnissen schlagen wir eine Planungslösung vor, die gut für reale Domänen geeignet ist, in denen Objekte, die anhand bestimmter Attribute beschrieben werden können, und im Laufe des Montageprozesses dynamisch erzeugt und verbraucht werden. Darüber hinaus erlauben wir dem/der EndbenutzerIn, das System mit benutzerdefinierten Funktionen zu erweitern. Wir konzipieren und implementieren zwei verschiedene Lösungsstrategien für unser Planungsproblem. Wir sind in erster Linie davon motiviert, die Lücke zwischen den in der Literatur vorgeschlagenen cyber-physischen Produktionssystemen und ihren realen Gegenstücken zu schließen und ein Gebiet zu erforschen, welches bislang nicht besonders stark erforscht war. In dieser Hinsicht sind wir uns auch bewusst, dass unsere Arbeit nicht die endgültige Lösung des Problems darstellt. Wir dokumentieren und diskutieren die Grenzen unseres derzeitigen Ansatzes, mit der Absicht, den Weg für zukünftige Forschung und Entwicklung im Bereich der automatischen Produktionsplanung zu ebnen., In recent years, there has been a rapidly growing interest in automation and AI-based solutions in the manufacturing industry, which is described by some as the 4$^{\text{th}}$ industrial revolution. As the complexity of products increases, and many different variations of products are produced, it quickly becomes apparent that the entire planning of operations cannot be efficiently done by human operators. Cyber-physical production systems (CPPS) have emerged as a potential solution to this problem. These systems are conceptualized as systems of complex interactions between human actors (e.g., designers, engineers, workers) and machines. However, CPPS as they have been defined in the literature are still not well-established, and one of the main reasons cited is the lack of existing software infrastructure to support them. In particular, one area where not many practical solutions exist is the planning step. How should one go from the specification of a finished product and a list of available materials and actors, and create a viable plan for assembling the final product? This question remains largely unanswered. In this work, we study the existing state of CPPS and the research agenda proposed by different authors. Based on this, we propose and develop a planning solution that is well-suited for real world domains, in which objects that can be described in terms of certain attributes, and are dynamically created and consumed throughout the assembly process. Furthermore, we allow the end-user to extend the system with custom functions. We conceptualize and implement two different solution strategies for our planning problem. We are primarily driven by the motivation to bridge the gap between the the cyber-physical production systems proposed in literature and their real-world counterparts, and to start exploring what was previously not a very-well researched area. As such, we also recognize that our work is not the definite solution to the problem. We document and discuss limitations of our current approach, with the intent of paving the way for future research and development in the field of automated production planning.

Customer demands force manufacturing companies to offer individualised products to remain competitive. This results in a demand for flexibility and adaptability in product development and production, which causes a rise in complexity. Simultaneously, manufacturers strive to further automatise production and assembly lines to increase cost efficiency and optimise output. This leads to a conflict of interest between two different approaches, ‘streamlined and cost-efficient’ or ‘individualised and costly’. Software-Defined Manufacturing (SDM) is a new approach that seeks to reconcile these diametrical goals by increasing the adaptability of automated manufacturing. This is achieved by virtualising key functionalities of production machines, which permit the configuration of system functionality via software. With current production equipment, this is difficult, because it was designed for a specific process and because there is no meaningful separation between the executing physical part of the machine and the controlling software. This article presents a new method for the development of SDM-ready production machines and processes. The application of the method on a production use-case demonstrates its usefulness in the improvement of existing production facilities and processes. It further addresses the wider trend of Software-Defined Anything (SDx) and outlines the role SDM could play on the pathway towards autonomous manufacturing.

Data-driven methods based on artificial intelligence (AI) are powerful yet flexible tools for gathering knowledge and automating complex tasks in many areas of science and practice. Despite the rapid development of the field, the existing potential of AI methods to solve recent industrial, corporate and social challenges has not yet been fully exploited. Research shows the insufficient practicality of AI in domain-specific contexts as one of the main application hurdles. Focusing on industrial demands, this publication introduces a new paradigm in terms of applicability of AI methods, called Usable AI (UAI). Aspects of easily accessible, domain-specific AI methods are derived, which address essential user-oriented AI services within the UAI paradigm: usability, suitability, integrability and interoperability. The relevance of UAI is clarified by describing challenges, hurdles and peculiarities of AI applications in the production area, whereby the following user roles have been abstracted: developers of cyber–physical production systems (CPPS), developers of processes and operators of processes. The analysis shows that target artifacts, motivation, knowledge horizon and challenges differ for the user roles. Therefore, UAI shall enable domain- and user-role-specific adaptation of affordances accompanied by adaptive support of vertical and horizontal integration across the domains and user roles.

Knowledge Graphs (KGs) have a large potential to support analytics and AI applications in the manufacturing domain, which in the age of Industry 4.0 is increasingly driven by data. Compared to other domains, however, KG techniques have seen limited adoption in this field so far. We argue that the construction of KGs in the context of Cyber-physical Production Systems (CPPSs) requires a systematic methodology grounded in domain-specific abstractions. Consequently, we introduce a KG modularization framework based on the well-established RAMI 4.0 architecture model. A key benefit of the proposed approach is that the resulting KGs support navigation across abstraction hierarchies, enabling bottom-up contextualization of raw data on the one hand, and top-down explanations by linking to lower levels of granularity on the other hand. We motivate the proposed approach and illustrate its application with a real-world use case from the automotive sector.

Industry 4.0 architecture has been studied in a large number of publications in the fields of Industrial Internet of Things, Cyber Physical Production Systems, Enterprise Architectures, Enterprise Integration and Cloud Manufacturing. A large number of architectures have been proposed, but none of them has been adopted by a large number of research groups. Two major Industry 4.0 reference architectures have been developed by industry-driven initiatives, namely the German Industry 4.0 and the US-led Industrial Internet Consortium. These are the Reference Architecture Model Industry 4.0 and Industrial Internet Reference Architecture, which are being standardized by the International Electrotechnical Commission and the Object Management Group, respectively. The first research goal of this article is to survey the literature on Industry 4.0 architectures in a factory context and assess awareness and compatibility with Reference Architecture Model Industry 4.0 and Industrial Internet Reference Architecture. The second research goal is to adapt a previously proposed advanced manufacturing concept to Reference Architecture Model Industry 4.0. With respect to the first research goal, it was discovered that only a minority of researchers were aware of the said reference architectures and that in general authors offered no discussion about the compatibility of their proposals with any internationally standardized reference architecture for Industry 4.0. With respect to the second research goal, it was discovered that Reference Architecture Model Industry 4.0 was mature with respect to communication and information sharing in the scope of the connected world, that further standardization enabling interoperability of different vendors’ technology is still under development and that technology standardization enabling executable business processes between networked enterprises was lacking.

In traditional production plants, current technologies do not provide sufficient context to support information integration and interpretation. Digital transformation technologies have the potential to support contextualization, but it is unclear how this can be achieved. The present article presents a selection of the psychological literature in four areas relevant to contextualization: information sampling, information integration, categorization, and causal reasoning. Characteristic biases and limitations of human information processing are discussed. Based on this literature, we derive functional requirements for digital transformation technologies, focusing on the cognitive activities they should support. We then present a selection of technologies that have the potential to foster contextualization. These technologies enable the modelling of system relations, the integration of data from different sources, and the connection of the present situation with historical data. We illustrate how these technologies can support contextual reasoning, and highlight challenges that should be addressed when designing human–machine cooperation in cyber-physical production systems.

Due to increased competitive pressure, modern organizations tend to rely on knowledge and its exploitation to sustain a long-term advantage. This calls for a precise understanding of knowledge management (KM) processes and, specifically, how knowledge is created, shared/transferred, acquired, stored/retrieved, and applied throughout an organizational system. However, since the beginning of the new millennium, such KM processes have been deeply affected and molded by the advent of the fourth industrial revolution, also called Industry 4.0, which involves the inter contentedness of machines and their ability to learn and share data autonomously. For this reason, the present article investigates the intellectual structure and trends of KM in Industry 4.0. Bibliometric analysis and a systematic literature review are conducted on a total of 90 relevant articles. The results reveal six clusters of keywords, subsequently explored via a systematic literature review to identify potential stream of this emergent field and future research avenues capable of producing meaningful advances in managerial knowledge of Industry 4.0 and its consequences.

Condition monitoring of industrial equipment, combined with machine learning algorithms, may significantly improve maintenance activities on modern cyber-physical production systems. However, data of proper quality and of adequate quantity, modeling both good operational conditions as well as abnormal situations throughout the operational lifecycle, are required. Nevertheless, this is difficult to acquire in a non-destructive approach. In this context, this study investigates an approach to enable a transition from preventive maintenance activities, that are scheduled at predetermined time intervals, into predictive ones. In order to enable such approaches in a cyber-physical production system, a deep learning algorithm is used, allowing for maintenance activities to be planned according to the actual operational status of the machine and not in advance. An autoencoder-based methodology is employed for classifying real-world machine and sensor data, into a set of condition-related labels. Real-world data collected from manufacturing operations are used for training and testing a prototype implementation of Long Short-Term Memory autoencoders for estimating the remaining useful life of the monitored equipment. Finally, the proposed approach is evaluated in a use case related to a steel industry production process.

Die europäische Fertigungsindustrie ist von der zunehmenden Konkurrenz produzierender Unternehmen aus dem Ausland unter Druck gesetzt und versucht mit Initiativen wie Industrie 4.0 (I4.0) durch die Vernetzung ihrer Betriebsinfrastruktur neue Geschäftsmodelle und flexiblere Prozesse zu schaffen, um variantenreiche Produkte schneller und zu geringeren Kosten zu produzieren. Einen Kernaspekt dazu stellt Agilität dar, mit der Unternehmen schnell auf Marktänderungen reagieren können. Schon zu Beginn des 21. Jahrhundert wurden hierzu Fertigungskonzepte entwickelt, mit denen agile, rekonfigurierbare oder holonische Fertigungssysteme entstehen sollten, die auf eine schnelle Wandlungsfähigkeit abzielen. Mit I4.0 sollen nun Cyber-physische Produktionssysteme (CPPS) entstehen, in denen die Agilität durch Internettechnologien zur Kommunikation und Interaktion zwischen den Maschinen und Komponenten eines Fertigungssystems unterstützt werden soll. Im Speziellen sollen mit dem Ziel der agilen Automatisierung, das mit dieser Arbeit verfolgt wird, Inbetriebnahme- und Rekonfigurationsaufwände von Maschinen in heterogenen, komplexen Fertigungssystemen reduziert werden. Dazu wird im Zusammenhang mit I4.0 häufig leichtsinnig von semantischer Interoperabilität gesprochen, wenn heterogene Maschinen miteinander kommunizieren und interagieren sollen. Dabei beruht der Grundgedanke hinter semantischer Interoperabilität grundsätzlich darauf, eine für eine Domäne einheitliche Sprache mit eindeutige Begriffen und Zusammenhängen zwischen diesen zu definieren, um damit letztendlich das altbekannte Standardisierungsproblem zu lösen. Damit ist aber das grundlegende Interoperabilitätsproblem für Fertigungssysteme noch nicht gelöst. Denn Steuerungsansätze basierend auf semantischer Interoperabilität können selbst nur auf einer Menge vordefinierter statischer Regeln basieren, mit denen auf Grund der bekannten Maschinen und Komponenten und deren statischen Verhalten Entscheidungen getroffen werden können. Hierbei wird oft vernachlässigt, dass die Fähigkeiten eines gesamten Fertigungssystems in einer komplexen Abhängigkeit zueinander stehen, die durch diese vordefinierten Regeln abgebildet werden muss. Des Weiteren können in diesem Zusammenhang nur semantisch gleichartig beschriebene Maschinen verwendet werden, jedoch sind bei technischen Systemen oft sehr spezifische Anwendungen notwendig, deren Verhalten nicht problemlos auf eine abstrakte Semantik reduziert werden kann. Aus diesen Gründen wird in dieser Arbeit mit der Pragmatik auch die Beschreibung des maschinenspezifischen Verhaltens im Kontext der Anwendung berücksichtigt. Das hierin entwickelte Konzept beruht auf der Unterteilung der Semiotik (die in der Philosophie die Darstellung und Interpretation von Zeichen behandelt) in die Aspekte der Syntax, Semantik und Pragmatik, durch den Philosophen Charles W. Morris. Dabei wird das Wissen zum Verhalten der Maschinen, das bereits bei der Entwicklung und Programmierung in diese einfließt, in Form von orthogonalen Zustandsautomaten für die einzelnen Komponenten beschrieben und Abhängigkeiten zwischen diesen Komponenten in den Zustandsübergängen hinterlegt. Bei der Inbetriebnahme kann durch Auflösen der orthogonalen Zustandsautomaten ein einzelner Graph erzeugt werden, in dem Automatisierungsschritte zum Herstellen bestimmter Systemzustände dynamisch abgeleitet werden können. Dies erlaubt die Betrachtung einzelner Maschinen, wie auch von Fertigungszellen und -systeme, als Menge orthogonaler Zustandsautomaten. Auf dieser Ebene müssen jedoch auch die Abhängigkeiten zwischen den Maschinen und deren Komponenten berücksichtigt werden. Zu diesem Zweck werden Abhängigkeiten, sowie Schnittstellen zu anderen Maschinen, als externe Platzhalter ebenfalls in Form von Zustandsautomaten modelliert. Das vorgestellte Konzept wird anhand der Modellierung einer Werkzeugmaschine im Kontext einer Fertigungszelle überprüft und hinsichtlich der Ableitung der Automatisierungsschritte evaluiert., The European manufacturing industry is under pressure from increasing competition from manufacturing companies abroad and, through initiatives such as Industry 4.0 (I4.0), is trying to create new business models and more flexible processes through the interconenction of manufacturing equipment in order to produce variant-rich products faster and at lower cost. A core aspect of this is agility, which enables companies to respond quickly to market changes. Already at the beginning of the 21st century, manufacturing concepts were developed for this purpose, with which agile, reconfigurable or holonic manufacturing systems were to be created, which aim at rapid changeability. With I4.0, cyber-pysical production systems (CPPS) are now to be created in which agility is to be supported by Internet technologies for communication and interaction between the machines and components of a manufacturing system. Specifically, the goal of agile automation pursued in this thesis is to reduce commissioning and reconfiguration efforts of machines in heterogeneous, complex manufacturing systems. To this end, semantic interoperability is often carelessly referred to in connection with I4.0, when heterogeneous machines are supposed to communicate and interact with each other. The basic idea behind semantic interoperability is to define a uniform language for a domain with unambiguous terms and relationships between them in order to ultimately solve the well-known standardization problem. However, this does not solve the fundamental interoperability problem for manufacturing systems. This is because control approaches based on semantic interoperability can themselves only be based on a set of predefined static rules with which decisions can be made on the basis of the known machines and components and their static behavior. Here, it is often neglected that the capabilities of an entire manufacturing system posses complex dependencies among each other, which have to be represented within these predefined rules. Furthermore, only semantically identically described machines can be used in this context, but technical systems often require very specific applications whose behavior cannot be easily reduced to abstract semantics. For these reasons, pragmatics is used in this thesis to include the description of machine-specific behavior in the context of application. The concept developed herein is based on the division of semiotics (which in philosophy deals with the representation and interpretation of signs) into the aspects of syntax, semantics, and pragmatics, by the philosopher Charles W. Morris. Here, the knowledge about the behavior of the machines, which is already embedded into them during development and programming, is described in the form of orthogonal state machines for the individual components, while dependencies between these components are stored in the state transitions. During commissioning, a single graph can be generated by resolving the orthogonal state machines, in which automation steps for establishing specific system states can be derived dynamically. This allows for individual machines, as well as for manufacturing cells and systems, to be viewed as a set of orthogonal state machines. At this level, however, the dependencies between the machines and their components must also be considered. For this purpose, dependencies, as well as interfaces to other machines, are modeled as external placeholders also in the form of state machines. The presented concept is tested by modeling a machine tool in the context of a manufacturing cell and evaluated with respect to the derivation of the automation steps.

Software-intensive Cyber-Physische Produktionssysteme (SiCPPS) sind hochkomplexe vernetzte Systeme, die autonom miteinander und mit ihrer Umgebung interagieren, um flexibel eine Vielzahl an Produkten zu produzieren. Solche Systeme haben typischerweise eine lange Lebensdauer, über die eine Vielzahl von Varianten entwickelt und gewartet werden muss. Diese Systeme bestehen aus vielen heterogenen Hard- und Softwarekomponenten, welche auf verschiedene Arten konfiguriert und kombiniert werden können. Variabilität betrifft dabei nicht nur diese Komponenten, sondern auch Entwicklungsprozesse, Anwendungsdomänen sowie Methoden und Werkzeuge. In der Industrie hängt der Umgang mit Variabilität jedoch aktuell stark von Expertenwissen als auch von selbst entwickelten Werkzeugen ab. Diese funktionieren oft nur mit ganz bestimmten Artefakten bzw. Hard- und Softwareplattformen. Existierende Forschungsansätze aus dem Gebiet der SiCPPS beschäftigen sich nicht explizit mit der Beherrschung dieser Variabilität. In diesem Beitrag definieren wir eine Forschungsagenda zur Beherrschung der Variabilität in SiCPPS. Wir konzentrieren uns dabei auf die Entwicklung von Techniken (i) zur Erhebung und Modellierung von Variabilität und (ii) zur Automatisierung sowohl der Generierung von Artefakten in SiCPPS als auch der Generierung und Wartung von Modellen auf Basis bestehender Artefakte. Software-intensive Cyber-Physical Production Systems (SiCPPS) are highly complex networked systems that autonomously interact with each other and their environment to flexibly produce a variety of products. Such systems typically have long lifetimes over which a large number of variants must be developed and maintained. These systems consist of many heterogeneous hardware and software components, which can be configured and combined in various ways. Variability affects not only these components, but also the development processes, application domains, and methods and tools. In industry, however, the handling of variability currently depends heavily on expert knowledge as well as on tools developed in-house. These often only work with very specific artifacts or hardware and software platforms. Existing research approaches in the field of SiCPPS do not explicitly address the management of this variability. In this paper, we define a research agenda for mastering variability in SiCPPS. We focus on developing techniques (i) for eliciting and modeling variability and (ii) for automating both the generation of artifacts in SiCPPS and the generation and maintenance of models based on existing artifacts. Version of record

In this paper, we propose a multi-perspective model for smart products as a basis to implement advanced web-based services in the smart factory production chain. The model relies on three perspectives that are: the product, the production process and the work centers involved in the production chain. For each perspective, the physical world is connected with the cyber world, where collected sensor data is properly organised to enable data analysis and exploration according to the three perspectives in an interleaved way. A portfolio of web-based services at production chain level is also described according to the model. Among them, a data access dashboard has been designed in order to enable controlled exploration of production chain data for different actors involved in the production activities, ranging from the suppliers to the producer of the final product. The approach is devoted to the production of costly and complex products, where the conceptualisation of a Smart Product as the integration of product, process and infrastructure sensor data aims at ensuring high product quality levels, long lasting operativity, less frequent and efficient maintenance activities and constant performances over time.

Volume 2021, Issue 17, This contribution evaluates the feasibility of blockchain technology for processes of a cyber-physical production system (CPPS) using a decision framework. The evaluation is based on an implemented CPPS use case. Opportunities for the use of smart contracts are identified and two exemplary smart contracts are designed. To integrate the heterogeneous and resource-constrained IoT devices of a CPPS, three levels of connection to the blockchain are identified and applied to the use case. This work contributes to the implementation of an extensive blockchain-based CPPS., Dieser Beitrag evaluiert den Einsatz der Blockchain-Technologie f��r Prozesse eines cyber-physischen Produktionssystems (CPPS) anhand eines Entscheidungsrahmens. Die Evaluation basiert auf einem bestehenden CPPS Anwendungsfall. Es werden M��glichkeiten f��r den Einsatz von Smart Contracts identifiziert und zwei beispielhafte Smart Contracts entworfen. Um die heterogenen und ressourcen-beschr��nkten IoT-Ger��te eines CPPS zu integrieren, werden drei Ebenen der Anbindung an die Blockchain identifiziert und auf den Anwendungsfall angewendet. Diese Arbeit tr��gt zur Implementierung eines umfang-reichen blockchain-basierten CPPS bei.

Growing complexity in Industry 4.0 environments goes hand in hand with an increasing number of vulnerabilities. Due to internal and external influences, these vulnerabilities can cause a negative effect on the production process as well as the finished product. These influences include e. g., defective machine tools, deviations in quality or configuration changes of an assembly line, which in turn require to adjust the model and the workflow of the respective facility to prevent risks to human health or minimise costs due to production stops. To evaluate the necessity of a novel approach to handle these vulnerabilities and influences along with their subsequent system adaptation, three emerging problems are identified and provide the basis for the discussion of a distributed architecture that aims to facilitate the evolution of context models, workflows and configurations while allowing for reasonable involvement of human operators.

Plating process chains are characterized by a high specific energy and resource demand as well as a high complexity due to dynamic interdependencies between and within processing steps. Planning and operating plating process chains should focus on aspects from cleaner production such as a high energy and resource efficiency, low impacts on the environment as well as on economic aspects. A high process transparency is required to meet these objectives and to evaluate the effects of improvement measures. An energy and resource flow simulation can support this by providing a fully parameterizable digital twin of the physical plating line. This simulation is integrated into a cyber-physical production system approach and connected to the IT environment of the plating company for the simulation of real scenarios. For the parameterization of the energy model, continuous and temporal measurements are combined systematically while the resource flow model is parameterized through information from the manufacturing IT systems. The successful implementation at a job plating company with an industrial acid zinc-nickel plating line indicated reachable electricity and resource savings of up to 10% in four scenarios. The electricity and plating metal demand was allocated to single carriers and products as basis for a product-based environmental and economic analysis. Especially in case of different carrier load levels, the energy and resource demand per product varies significant. The developed approach and its successful implementation emphasizes the need of a high process transparency for planning and operating plating process chains to accelerate the shift towards cleaner production.

Reliability is one of the most important performance indicators in contemporary production facilities. Increasing reliability of manufacturing systems results in their prolonged lifetimes, and reduced maintenance and repair costs. Reliability modeling is a common technique for deriving reliability measurements and illustrating relevant fault-dependencies. There is a significant body of research focusing on hardware- and software reliability models, such as Fault Trees, Petri Nets and Markov Chains. Up until now, development of reliability models has been a labor-intensive and expert-knowledge-driven process. To remedy that, through the prevalence of data stemming from the new and technologically advanced manufacturing systems, we propose that data generated in modern manufacturing lines could be used to either automate or at least to support development of reliability models. In this paper, we elaborate on the details of our proposed framework for data-driven reliability assessment of cyber-physical production systems. We, furthermore, introduce a case study that will aid the development and testing of the proposed novel data-driven approach.

Facility management is an evolving discipline that has received attention from both professionals and researchers in recent years. Modern facility management considers various interests related to material resources, and among others, social and environmental interests. An important opportunity for the improvement of this discipline derives from the introduction of Industry 4.0 technologies for the management of material resources. In this paper, we shall study the problem of industrial facility management, an area with important economic implications. Starting from a facility management model for the maintenance of industrial assets, we develop a general approach to maintenance based on the Internet of Things and Cyber-Physical Systems, which allows us to reason about the implementation of an effective Organisational Facility Management Unit. The objective is the continuous improvement of maintenance activities, from which also derives the improvement of the production process performance.

This workshop focuses on Software Engineering in Cyber-Physical Production Systems. It is an interactive workshop opened by keynotes and statements by participants, followed by extensive discussions in break-out groups. The output of the workshop is a research roadmap as well as concrete networking activities to further establish a community in this interdisciplinary field.

In cyber-physical production systems, material flows show complexity due to varying physical aspects of transported work pieces and autonomously selected transport routes. As a result, physically induced disturbances that may lead to delays or damages are hard to predict. The on-line usage of a physics engine offers potential to derive material flow parameters that enable safe transports with optimized accelerations. Previous work showed the feasibility of this approach and potential operational benefits through faster material flows. In consequence, the scope of this paper is to apply discrete-event simulation to investigate whether physics simulation of material flows leads to positive impacts on production system performance indicators such as throughput times and capacity utilization. The results indicate that increased velocity and acceleration of material flows can positively influence these indicators. In consequence, applying physics simulation to ensure safe transports with such high velocities and accelerations can improve the overall performance of a production system., OASIcs, Vol. 89, 2nd International Conference of the DFG International Research Training Group 2057 – Physical Modeling for Virtual Manufacturing (iPMVM 2020), pages 15:1-15:26

The subject of research in the article is production management processes based on cyber-physical systems. The purpose of the work is to automate the management of production processes using the cybernetic component and analyze the results. To achieve this goal, it is necessary to solve the following tasks: develop an analytical and logical model of the CPPS development management process taking into account the requirements specified in the terms of reference for the development CPPS; to present the analytical and logical structure of connections in the form of a model of interaction of the main windows and all the necessary graphical elements for the implementation of the full-featured interface of the developed CPPS; to carry out experimental researches of efficiency and practical approbation of the received theoretical results, by comparison of classical methods and the developed ones. Results: The targets, physical and cybernetic components of the CPPS development management process were combined in a single information space from the beginning of development to its implementation. The process of checking the achievement of the main goal of CPPS development has been automated, which makes it possible to make changes and manage the CPPS development process at any level and stage of the proposed technology. The process of managing the development of the cybernetic component is automated on the basis of synthesized algorithms of functioning using the GUI elements of object-oriented programming languages, which made it possible to increase the flexibility of the proposed architecture and technologies for automating the CPPS development process and to reduce the development time of the cybernetic component. Conclusions: the proposed models and methods will not only automate the process of managing the development of CPPS from scratch, but will also make it possible to automate the process of managing the development of the cybernetic component for the modernization and improvement of existing CPPS., Предметом исследования в статье являются процессы управления производством на базе кибер-физических систем. Цель работы – автоматизация управления производственными процессами с помощью кибернетической составляющей и анализ полученных результатов. Для достижения поставленной цели необходимо решение следующих задач: разработать аналитически-логическую модель процесса управления разработкой CPPS с учетом требований указанных в техническом задании на разрабатываемый CPPS; представить аналитически-логическую структуру связей в виде модели взаимодействия основных окон и всех необходимых графических элементов для реализации полнофункционального интерфейса разрабатываемой CPPS; провести экспериментальные исследования эффективности и практической апробации полученных теоретических результатов, путем сравнения классических методов и разработанных. Результаты: Объединены целевые, физические и кибернетические составляющие процесса управления разработки CPPS в едином информационном пространстве от начала разработки до его внедрения. Автоматизирован процесс проверки достижения главной цели разработки CPPS, который дает возможность вносить изменения и управлять процессом разработки CPPS на любом уровне и этапе предлагаемой технологии. Автоматизирован процесс управления разработки кибернетической составляющей на базе синтезированных алгоритмов функционирования с использованием GUI элементов объектно-ориентированных языков программирования, что позволило увеличить гибкость предложенной архитектуры и технологий автоматизации процесса разработки CPPS и сократить время разработки кибернетической составляющей. Выводы: предложенные модели и методы позволят не только автоматизировать процесс управления разработкой CPPS с "нуля", но и даст возможность автоматизировать процесс управления разработкой кибернетической составляющей для модернизации и усовершенствования уже существующих CPPS., Предметом дослідження в статті є процеси керування виробництвом на базі кібер-фізичних систем. Мета роботи – автоматизація керування виробничими процесами за допомогою кібернетичної складової і аналіз отриманих результатів. Для досягнення поставленої мети необхідне вирішення наступних завдань: розробити аналітично-логічну модель процесу управління розробкою CPPS з урахуванням вимог зазначених в технічному завданні на розроблювальний CPPS; представити аналітично-логічну структуру зв'язків у вигляді моделі взаємодії основних вікон і всіх необхідних графічних елементів для реалізації повнофункціонального інтерфейсу розроблюваної CPPS; провести експериментальні дослідження ефективності та практичну апробацію отриманих теоретичних результатів, шляхом порівняння класичних методів та розроблених. Результати: Об'єднано цільові, фізичні та кібернетичні складові процесу управління розробки CPPS в єдиному інформаційному просторі від початку розробки до його впровадження. Автоматизовано процес перевірки досягнення головної мети розробки CPPS, який дає можливість вносити зміни і керувати процесом розробки CPPS на будь-якому рівні і етапі запропонованої технології. Автоматизовано процес управління розробки кібернетичної складової на базі синтезованих алгоритмів функціонування з використанням GUI елементів об'єктно-орієнтованих мов програмування, що дало можливість збільшити гнучкість запропонованої архітектури і технологій автоматизації процесу розробки CPPS та скоротити час розробки кібернетичної складової. Результати наукових і теоретичних досліджень впроваджено в освітній процесс, у виробничий процес ряду державних і приватних підприємств, також викладені рішення захищені авторськими свідоцтвами. Висновки: запропоновані моделі та методи дозволять не тільки автоматизувати процес управління розробкою CPPS з "нуля", а й дасть можливість автоматизувати процес управління розробкою кібернетичної складової для модернізації і удосконалення вже існуючих CPPS.