Search

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. [ABSTRACT FROM AUTHOR]

In the vision of smart manufacturing and Industry 4.0, it is vital to automate production processes. There is a significant gap in current practices, where the derivation of production processes from product data still heavily relies on human expertise, leading to inefficiencies and a shortage of skilled labor. This paper proposes a universal framework for skill-based cyber–physical production systems (CPPS) that formalizes production knowledge into machine-processable formats. Key contributions include a novel conceptual model for skill-based production processes and an automated method to derive production plans from high-level CPPS skills for production planning and execution. This framework aims to enhance smart manufacturing by enabling more efficient, transparent, and automated production planning, thereby addressing the critical gap in current manufacturing practices. The framework's benefits include making production processes explainable, optimizing multi-criteria systems, and eliminating human biases in process selection. A case study illustrates the framework's application, demonstrating its current capabilities and potential for modern manufacturing. [ABSTRACT FROM AUTHOR]

This study aims to experimentally confirm whether knowledge that has been challenging to transfer through traditional on-the-job training (OJT) can be effectively transferred by introducing a formalized OJT approach that describes the improvement process knowledge of skilled production systems consultants, facilitating imitation by unskilled consultants. We adopted the Digital Triplet (D3) concept, an extension of the authors' digital twin framework to intelligent activities, aligning with our study objectives. Recognizing the difficulty and inadequacy of knowledge transfer in production systems consulting OJT, we propose an OJT support method integrating a decision-making modeling approach for skilled consultants' processes based on the Generalized Production Systems Consulting Process Model (GCPM) from prior literature into traditional OJT methods involving self-learning and direct instruction. This method enables the construction of a domain-specific GCPM, formalizing the improvement process flow implemented by skilled consultants and linking it to production improvement expertise and tools. In a case study focused on energy-saving improvement, we constructed and tested a domain-specific GCPM's efficacy in facilitating the transfer of difficult-to-transfer knowledge. The results indicate that domain-specific GCPM facilitates such knowledge transfer, including specialized improvement, knowledge utilization, rationale, and adaptation to specific cases. [ABSTRACT FROM AUTHOR]

Many intelligent distributed production control architectures have been developed for cyber-physical production systems (CPPSs), but the difficulty in predicting performance has hindered acceptance by industry. Performance predictions for systems with conventional control can be made by simulating product flow using discrete-event simulation (DES) software. However, DES is inadequate for capturing the intricacies of intelligent distributed production control architectures. Alternatively, agent-based simulation (ABS) software is more effective for capturing distributed intelligence. A hybrid discrete-event and agent-based simulation tool combines the strengths of both approaches, making it effective for capturing the intertwined physical and cyber layers of a CPPS. In this paper, a review is carried out to determine which off-the-shelf simulation tools are capable of using hybrid discrete-event and agent-based simulation for performance predictions in the conceptual design phase of a CPPS. This review is carried out using the following structured steps. First, the scope and evaluation criteria are identified. Next, a selection of simulation tools is collected. The selected tools are then evaluated and classified. Finally, the most promising simulation tool according to this evaluation – Anylogic – is subjected to a case study to assess if hybrid simulation can be used to predict the performance of a CPPS. [ABSTRACT FROM AUTHOR]

In manufacturing, biologicalisation defines the analysis of biological patterns as a source of inspiration to model intelligent manufacturing systems. This analysis is highly desirable as an answer to the increasing complexity of modeling current engineering solutions which are required to be self-organized, cooperative, and autonomous. Building on this line of research, our paper introduces a framework inspired by the notion of “reciprocal altruism” observed in species like vampire bats. The goal of this framework is twofold; first, to showcase the bio-inspired methodological guidelines of altruism in a manufacturing context. Second, to foster cooperative behaviors among its constitutive resources. The core idea revolves around the assignment of two roles: altruistic/donors and recipients. In this context, altruistic/donors are individuals willing to share their resources or capabilities with those in need, even at the potential cost of their own fitness. We believe that this concept has versatile applications in various manufacturing scenarios, ranging from peer-to-peer energy sharing among mobile robots to load sharing and even tool sharing. In our work, we instantiate the control logic, functionalities, and proof of concept of this altruistic approach, focusing on tool-sharing as an illustrative application. Our preliminary results demonstrate an improvement when comparing the altruistic approach with a manual one. This improvement is particularly evident when considering potential production downtime and production rate fluctuations caused by tool defects. These findings underscore the tangible benefits that bio-inspired solutions can offer in addressing the ongoing challenges of smart manufacturing, especially in terms of engineering design.

In the vision of smart manufacturing and Industry 4.0, it is vital to automate production processes. There is a significant gap in current practices, where the derivation of production processes from product data still heavily relies on human expertise, leading to inefficiencies and a shortage of skilled labor. This paper proposes a universal framework for skill-based cyber–physical production systems (CPPS) that formalizes production knowledge into machine-processable formats. Key contributions include a novel conceptual model for skill-based production processes and an automated method to derive production plans from high-level CPPS skills for production planning and execution. This framework aims to enhance smart manufacturing by enabling more efficient, transparent, and automated production planning, thereby addressing the critical gap in current manufacturing practices. The framework’s benefits include making production processes explainable, optimizing multi-criteria systems, and eliminating human biases in process selection. A case study illustrates the framework’s application, demonstrating its current capabilities and potential for modern manufacturing.

Anomaly detection is essential for realising state-of-the-art and safe cyber-physical production systems. With the early detection of possible anomalies, the rise and the carryover of a failure throughout the entire manufacture can be prevented. Current anomaly detection approaches use sensor data from the live system only. Therefore, many important characteristics, interactions, and correlations within the system are neglected. Graphs open a particular way of modelling cyber-physical production systems, while at the same time, they allow the inclusion of a wider variation and variety of data about the system. In this study, we introduce an unsupervised correlation- and interaction-aware anomaly detection concept for cyber-physical production systems based on graph neural networks. We define a concept that allows modelling the entire system as a graph, including external knowledge. We introduce a reconstruction-based graph neural network to detect and analyse the anomalies in these graph structures. The concept is evaluated in a real industrial cyber-physical production system. The test results confirm that the presented concept can be applied to detect anomalies while concurrently considering the correlations and interactions of the system. [ABSTRACT FROM AUTHOR]

Copyright of HMD: Praxis der Wirtschaftsinformatik is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The demand for reconfigurations of production systems is increasing, driven by shorter innovation and product life cycles and economic volatility. Another trend in the domain of industrial automation is the emergence of cyber-physical production systems, which offer promising potentials, for example, self-organization capabilities. A suitable cyber-physical production system architecture that incorporates knowledge modelling and management concerns, plus a reconfiguration management methodology, is crucial for realizing self-organized reconfiguration management. In this paper, first reference architectures, architectural patterns, and basic principles, as well as knowledge modelling and management approaches, are discussed in general. Afterwards, these are examined concerning the reconfiguration management use case focusing on UML/XML-based and ontology-based approaches. A novel approach comprising a multi-agent system and the MAPE-K concept for reconfiguration management is presented. In addition, the approach contains a service-oriented architecture for a deterministic plant control within a layered architecture. The knowledge modelling is realized through a UML information model, which can be integrated into the system utilizing XML files. Furthermore, the provided tool support is described. It enables a user to describe system components in an effort-reduced manner and conform to the schema defined by the information model and its restrictions via a GUI. [ABSTRACT FROM AUTHOR]

The primary objective of this study is to evaluate the adaptability and inclination of industrial sectors of Pakistan with respect to Industry 4.0. A questionnaire with nine questions was developed and disseminated to 20 sampled industries. To analyze the variability in responses, a one-way analysis of variance test was used. The statistical analysis revealed that there is an awareness of the basic concept behind Industry 4.0 in Pakistani industries, but there is a reluctance to adopt digitization and to shift from conventional production systems. This study will be helpful and will provide a guide for new and already existing enterprises for achieving Industry 4.0 requisite attributes precisely. [ABSTRACT FROM AUTHOR]

Cyber-Physical Systems (CPS) play an essential role in today's production processes, leveraging Artificial Intelligence (AI) to enhance operations such as optimization, anomaly detection, and predictive maintenance. This article reviews a cognitive architecture for Artificial Intelligence, which has been developed to establish a standard framework for integrating AI solutions into existing production processes. Given that machines in these processes continuously generate large streams of data, Online Machine Learning (OML) is identified as a crucial extension to the existing architecture. To substantiate this claim, real-world experiments using a slitting machine are conducted, to compare the performance of OML to traditional Batch Machine Learning. The assessment of contemporary OML algorithms using a real production system is a fundamental innovation in this research. The evaluations clearly indicate that OML adds significant value to CPS, and it is strongly recommended as an extension of related architectures, such as the cognitive architecture for AI discussed in this article. Additionally, surrogate-model-based optimization is employed, to determine the optimal hyperparameter settings for the corresponding OML algorithms, aiming to achieve peak performance in their respective tasks. [ABSTRACT FROM AUTHOR]

This study aims to experimentally confirm whether knowledge that has been challenging to transfer through traditional on-the-job training (OJT) can be effectively transferred by introducing a formalized OJT approach that describes the improvement process knowledge of skilled production systems consultants, facilitating imitation by unskilled consultants. We adopted the Digital Triplet (D3) concept, an extension of the authors’ digital twin framework to intelligent activities, aligning with our study objectives. Recognizing the difficulty and inadequacy of knowledge transfer in production systems consulting OJT, we propose an OJT support method integrating a decision-making modeling approach for skilled consultants’ processes based on the Generalized Production Systems Consulting Process Model (GCPM) from prior literature into traditional OJT methods involving self-learning and direct instruction. This method enables the construction of a domain-specific GCPM, formalizing the improvement process flow implemented by skilled consultants and linking it to production improvement expertise and tools. In a case study focused on energy-saving improvement, we constructed and tested a domain-specific GCPM’s efficacy in facilitating the transfer of difficult-to-transfer knowledge. The results indicate that domain-specific GCPM facilitates such knowledge transfer, including specialized improvement, knowledge utilization, rationale, and adaptation to specific cases.

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. [ABSTRACT FROM AUTHOR]

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.

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. [ABSTRACT FROM AUTHOR]

Copyright of Automatisierungstechnik is the property of De Gruyter and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The requirements for cyber-physical production systems (CPPS) grow steadily, leading to increased complexity of such systems. To address issues due to reconfiguration or failures, we present an approach to creating a context model of the CPPS components during runtime and to detect deviations from the planned production processes. Furthermore, we present a concept for modelling production processes that allows for dynamic process adaption during runtime. We then utilize this process adaption and the context model to cope with unforeseen failures and increase the resilience of the system. Finally, we evaluate the feasibility of our approach by applying it to an Industry 4.0 production system. [ABSTRACT FROM AUTHOR]

Customized production requires highly flexible and adaptable production systems, which can be enabled by Cyber-physical production systems (CPPS). For this purpose, it is necessary to implement appropriate manufacturing cells with certain capabilities into the CPPS network. The cells have to contain changeable characteristics to meet varying requirements along their lifecycle. The paper presents a novel and already validated methodology to enable the potential of CPPS by planning modular and scalable manufacturing cells. The developed methodology consists of a systematic procedure using models for the generation of alternative manufacturing cells, with a focus on customizable automation. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

We draw on a substantial body of theoretical and empirical research on industrial 5G networks of virtual machines, factories, and workspaces. In this research, prior findings were cumulated indicating that Industrial Internet of Things and smart manufacturing and product-service systems shape remote monitoring and control across workspace digital twins. We carried out a quantitative literature review of ProQuest, Scopus, and the Web of Science throughout July 2022, with search terms including “the interactive industrial metaverse” + “virtual clones of cyber-physical production systems,” “workspace digital twins,” and “multi-sensor fusion technologies.” As we analyzed research published in 2022, only 159 papers met the eligibility criteria. By removing controversial or unclear findings (scanty/ unimportant data), results unsupported by replication, undetailed content, or papers having quite similar titles, we decided on 26, chiefly empirical, sources. Data visual- ization tools: Dimensions (bibliometric mapping) and VOSviewer (layout algorithms). Reporting quality assessment tool: PRISMA. Methodological quality assessment tools include: AMSTAR, Distiller SR, ROBIS, and SRDR. [ABSTRACT FROM AUTHOR]

Abstract Designing and developing distributed cyber-physical production systems (CPPS) is a time-consuming, complex, and error-prone process. These systems are typically heterogeneous, i.e., they consist of multiple components implemented with different languages and development tools. One of the main problems nowadays in CPPS implementation is enabling security mechanisms by design while reducing the complexity and increasing the system’s maintainability. Adopting the IEC 61499 standard is an excellent approach to tackle these challenges by enabling the design, deployment, and management of CPPS in a model-based engineering methodology. We propose a method for CPPS design based on the IEC 61499 standard. The method allows designers to embed a bio-inspired anomaly-based host intrusion detection system (A-HIDS) in Edge devices. This A-HIDS is based on the incremental Dendritic Cell Algorithm (iDCA) and can analyze OPC UA network data exchanged between the Edge devices and detect attacks that target the CPPS’ Edge layer. This study’s findings have practical implications on the industrial security community by making novel contributions to the intrusion detection problem in CPPS considering immune-inspired solutions, and cost-effective security by design system implementation. According to the experimental data, the proposed solution can dramatically reduce design and code complexity while improving application maintainability and successfully detecting network attacks without negatively impacting the performance of the CPPS Edge devices.

Cyber-Physical Systems (CPS) play an essential role in today’s production processes, leveraging Artificial Intelligence (AI) to enhance operations such as optimization, anomaly detection, and predictive maintenance. This article reviews a cognitive architecture for Artificial Intelligence, which has been developed to establish a standard framework for integrating AI solutions into existing production processes. Given that machines in these processes continuously generate large streams of data, Online Machine Learning (OML) is identified as a crucial extension to the existing architecture. To substantiate this claim, real-world experiments using a slitting machine are conducted, to compare the performance of OML to traditional Batch Machine Learning. The assessment of contemporary OML algorithms using a real production system is a fundamental innovation in this research. The evaluations clearly indicate that OML adds significant value to CPS, and it is strongly recommended as an extension of related architectures, such as the cognitive architecture for AI discussed in this article. Additionally, surrogate-model-based optimization is employed, to determine the optimal hyperparameter settings for the corresponding OML algorithms, aiming to achieve peak performance in their respective tasks.

The primary objective of this study is to evaluate the adaptability and inclination of industrial sectors of Pakistan with respect to Industry 4.0. A questionnaire with nine questions was developed and disseminated to 20 sampled industries. To analyze the variability in responses, a one-way analysis of variance test was used. The statistical analysis revealed that there is an awareness of the basic concept behind Industry 4.0 in Pakistani industries, but there is a reluctance to adopt digitization and to shift from conventional production systems. This study will be helpful and will provide a guide for new and already existing enterprises for achieving Industry 4.0 requisite attributes precisely.

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.

Modern machining systems use horizontal and vertical data integration for the automatic monitoring and quality control of the machining operations. There are currently available some comprehensive papers focused on high-level system integration. Independently of that, there are also multiple publications focused on the automatic monitoring of specific machining operations. There is lack of presented connections between the high-level proposals and the operation-level methods. This paper proposes a framework for milling process monitoring that covers both mentioned levels, i.e., the system complexity as well as particular operation details. The data model consisting of five key objects (cutting tool management; machine tool; workpiece; machining operation and user) is presented within the framework. [ABSTRACT FROM AUTHOR]

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.

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.