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The mass customisation strategy is needed by manufacturing companies to face the increasing variety and unpredictability of products required by customers. However, mass customisation may increase the complexity of managing manufacturing and production logistics activities, for example due to reduced product batch sizes. The synchronisation of material flows within the factory is emerging as a way to address this complexity, as it enables an effective and efficient implementation of mass customisation. Indeed, the fourth Industrial Revolution introduces new digital levers, which can be combined with traditional managerial levers to achieve the synchronisation of material flows within the factory. This study contributes to the rising stream of research on this topic. A systematic literature review was conducted, leading to the development of a classification framework of the levers supporting the synchronisation of material flows. The identified managerial levers are: storage of materials, feeding policy, and scheduling. The digital levers are: materials tracking, process tracking, data analytics, and assistance systems. The developed framework was operationalised in four industrial cases and applied as a tool to map their levers related to the synchronisation of material flows. [ABSTRACT FROM AUTHOR]

The continuous manufacturing of biologics offers significant advantages in terms of reducing manufacturing costs and increasing capacity, but it is not yet widely implemented by the industry due to major challenges in the automation, scheduling, process monitoring, continued process verification, and real-time control of multiple interconnected processing steps, which must be tightly controlled to produce a safe and efficacious product. The process produces a large amount of data from different sensors, analytical instruments, and offline analyses, requiring organization, storage, and analyses for process monitoring and control without compromising accuracy. We present a case study of a cyber–physical production system (CPPS) for the continuous manufacturing of mAbs that provides an automation infrastructure for data collection and storage in a data historian, along with data management tools that enable real-time analysis of the ongoing process using multivariate algorithms. The CPPS also facilitates process control and provides support in handling deviations at the process level by allowing the continuous train to re-adjust itself via a series of interconnected surge tanks and by recommending corrective actions to the operator. Successful steady-state operation is demonstrated for 55 h with end-to-end process automation and data collection via a range of in-line and at-line sensors. Following this, a series of deviations in the downstream unit operations, including affinity capture chromatography, cation exchange chromatography, and ultrafiltration, are monitored and tracked using multivariate approaches and in-process controls. The system is in line with Industry 4.0 and smart manufacturing concepts and is the first end-to-end CPPS for the continuous manufacturing of mAbs. [ABSTRACT FROM AUTHOR]

This study synthesizes a model of the main engineering and production chains in the Smart factory of Industry 4.0. This study has three main stages: an overview of the main definitions of the concepts "engineering and production chain" and "model", the generation of working definitions of these concepts for the purposes of this article, and synthesis of a model of the main engineering and production chains in the Smart factory of Industry 4.0. It was concluded in the course of the analysis that the defined six main engineering and production chains in the Smart factory of Industry 4.0 are part of its cyber–physical production system. The tools that support the Model of the main engineering and production chains in the Smart factory of Industry 4.0 are also synthesized in this article. The essential added value for science and production engineering of the Model in the main engineering and production chains in the Smart factory of Industry 4.0 are defined for the first time in the literature as an option to deepen the processes of re-structuring the real traditional physical production into a cyber–physical production process by integrating specialized software products in the operation of each of the defined chains and the entire cyber–physical production system. [ABSTRACT FROM AUTHOR]

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The material extrusion 3D printing process known as fused deposition modeling (FDM) has recently gained relevance in the additive manufacturing industry for large-scale part production. However, improving the real-time monitoring of the process in terms of its mechanical properties remains important to extend the lifespan of numerous critical applications. To enhance the monitoring of mechanical properties during printing, it is necessary to understand the relationship between temperature profiles and ultimate tensile strength (UTS). This study uses a cyber–physical production system (CPPS) to analyze the impact of four key thermal parameters on the tensile properties of polylactic acid (PLA). Layer thickness, printing speed, and extrusion temperature are the most influential factors, while bed temperature has less impact. The Taguchi L-9 array and the full factorial design of experiments were implemented along with the deposited line's local fused temperature profile analysis. Furthermore, correlations between temperature profiles with the bonding strength during layer adhesion and part solidification can be stated. The results showed that layer thickness is the most important factor, followed by printing speed and extrusion temperature, with very close influence between each other. The lowest impact is attributed to bed temperature. In the experiments, the UTS values varied from 46.38 MPa to 56.19 MPa. This represents an increase in the UTS of around 17% from the same material and printing design conditions but different temperature profiles. Additionally, it was possible to observe that the influence of the parameter variations was not linear in terms of the UTS value or temperature profiles. For example, the increase in the UTS at the 0.6 mm layer thickness was around four times greater than the increase at 0.4 mm. Finally, even when it was found that an increase in the layer temperature led to an increase in the value of the UTS, for some of the parameters, it could be observed that it was not the main factor that caused the UTS to increase. From the monitoring conditions analyzed, it was concluded that the material requires an optimal thermal transition between deposition, adhesion, and layer solidification in order to result in part components with good mechanical properties. A tracking or monitoring system, such as the one designed, can serve as a potential tool for reducing the anisotropy in part production in 3D printing systems. [ABSTRACT FROM AUTHOR]

To reduce global greenhouse gas emissions, numerous new renewable power plants are installed and integrated in the power grid. Due to the volatile generation of renewable power plants large storage capacity has to be installed and electrical consumer must adapt to periods with more or less electrical generation. Industry, as one of the largest global consumers of electrical energy, can help by adjusting its electricity consumption to renewable production (demand response). Industrial aqueous parts cleaning machines offer a great potential for demand response as they often have inherent energy storage potential and their process can be adapted for energy-flexible operation. Therefore, this paper presents a method for implementing demand response measures to aqueous parts cleaning machines. We first determine the potential for shifting electrical consumption. Then, we adapt the automation program of the machine so that submodules and process steps with high potential can be energy-flexibly controlled. We apply the method to an aqueous parts cleaning machine in batch process at the ETA Research Factory. [ABSTRACT FROM AUTHOR]

In the modern world cyber-physical production systems are increasingly used. They allow you to control the flow of the technological process in production in real time. But the use of such an approach is greatly complicated by the fact that the equipment of many enterprises is old and cannot support the necessary functions. This is primarily due to the lack of the necessary sensors, as well as the corresponding software. Since the complete replacement of production equipment is very expensive, the task is to create separate monitoring systems. They must be able to integrate into the necessary parts of the production process. And they should also be cheap. In this work, we propose to build a model of such a monitoring and visualization system. The main attention in the work is focused on the hardware implementation of the proposed system and the relationship of its individual elements. [ABSTRACT FROM AUTHOR]

Exponentially growing technologies such as intelligent robots in the context of Industry 4.0 are radically changing traditional manufacturing to intelligent manufacturing. Workspaces are transformed into fully shared spaces for performing tasks during human–robot collaboration (HRC), increasing the possibility of accidents as compared to the fully restricted and partially shared workspaces. The interactions are quite demanding in terms of safety, employing both cognitive and physical resources. In this work, we have proposed a four-layered connective framework that can quickly respond to changing physical and psychological safety situations. The first layer performs the desired operation of the cyber-physical production system (CPPS) and gets itself aware of anomalies. The second layer assesses, categorizes and quantifies the developed situations as anxiety factor. The third layer mitigates the arising anxiety through the optimal allocation of resources. The fourth layer makes decisions based on the historical knowledge, the current state of anxiety, and the suggested optimization using logic. The results demonstrated that the proposed method improves decision-making of a CPPS, eventually increasing the productivity. The case study shows that the method is less time-intensive as the maximum time to decide on a situation was found 0.03 s. The survey highlighted the technique leads to enhanced fluency, collaboration, comfort, safety and legibility during collaboration. The proposed system was found 16.85% more accurate than the standard system. The significance test of the results highlighted the p-value < 0.01. The proposed framework can be applied to any industrial scenario where HRC is involved like manufacturing, assembling, packaging, etc. [ABSTRACT FROM AUTHOR]

The subject of the study is the process of Information and Measuring System (IMS) designing as a component of the Cyber-Physical System (CPS) in the paradigm of Industry 4.0 (I4.0). The aim of the study is to develop methodological support for the design of IMS and Automatic Control Systems (ACS) as components of production CPS (CPPS), in particular, for Digital Factory. Objectives: to determine the conceptual model of IMS; choose the IoT model for structural synthesis, select the appropriate regulatory (sets of standards and implementation models) and hardware; perform R&D of IMS based on NB-IoT sensors; formalize the procedure for integrating components into the CPPS, develop the Asset Administration Shell. The methods used are: heuristic synthesis methods, experimental planning theory. The following results were obtained. The key role of optimally designed IMS level 4.0 in increased decision-making accuracy in CPPS management and control processes is demonstrated. The quality of control is improved both by quickly obtaining accurate information for updating models in cyber add-ons, and at the physical level, in ACS. The universal model of IMS implementation in CPPS was proposed. The stages of choosing the concept, structure, hardware and communication protocols of the IIoT ecosystem IMS + ACS have been performed. The methodology was tested during the development of the NB-IoT Tech remote monitoring system, which has a decentralized structure for collection data on resources consumed. The integration of the ecosystem as a component of CPPS at the appropriate levels of the architectural model RAMI 4.0 has been performed. Regulatory support has been formed and the functional aspect of the Asset Administrative Shell for CPPS integration has been developed. Conclusions. Scientific novelty: it is proposed to design the IMS as a component of the AAS of the cyber-physical system, according to the implementation methodology of its subsystems at the corresponding levels of RAMI4.0 and the selected IoT model. The new approach, called "soft digitalization", combines the approaches of Industry 3.0 and 4.0, it is designed for the sustainable development of automated systems to the level of cyber-physical systems and is relevant for the recovery of the economy of Ukraine. Practical significance of the results: the IoT-Tech system based on Smart sensors has been developed and tested. This information and measurement system is non-volatile and adapted to measure any parameters in automated systems of various levels of digitization.

To remain competitive in a highly dynamic environment, manufacturing companies have to quickly react to disturbances or changing customer requirements. To enable manufacturing systems to cover these dynamics, the concept of reconfigurable manufacturing systems was introduced. From a technical point of view, this concept has been exploited for the past 20 years, revealing several different design solutions. However, industrial application is still an exception. Our analysis led to the assumption that this is due to a lack of operator support for reconfiguration management. In addition, mostly individual aspects of reconfiguration are considered instead of exploiting the entire reconfiguration space at the system and machine level. Therefore, in this paper, we present a digital twin framework for reconfiguration management considering reconfiguration as a holistic problem. We evaluate the framework by conducting a case study and challenging it by evaluating the completeness based on a systematic literature review, and analyze if it follows good practice based on 32 requirements for digital twin frameworks.

Sustainable manufacturing is a global front-burner issue oriented to the sustainable development of humanity and society. In this context, this paper takes the human-robot collaborative disassembly (HRCD) as the topic on its contribution to economic, environmental and social sustainability. In addition, a detailed enabling systematic implementation for HRCD is presented, combined with a set of advanced technologies such as cyber-physical production system (CPPS) and artificial intelligence (AI), and it involves five aspects which including perception, cognition, decision, execution and evolution aiming at the dynamics, uncertainties and complexities in disassembly. Deep reinforcement learning, incremental learning and transfer learning are also investigated in the systematic approaches for HRCD. The demonstration in the case study contains experiment results of multi-modal perception for robot system and human body in hybrid human-robot collaborative disassembly cell, sequence planning for an HRCD task, distance based safety strategy and motion driven control method, and it manifests high feasibility and effectiveness of the proposed approaches for HRCD and verifies the functionalities of the systematic framework. [ABSTRACT FROM AUTHOR]

Currently, the manufacturing management board applies technologies in line with the concept of Industry 4.0. Cyber-physical production systems (CPSs) mean integrating computational processes with the corresponding physical ones, i.e., allowing work at the operational level and at the strategic level to run side by side. This paper proposes a framework to collect data and information from a production process, namely, the burnishing one, in order to monitor real-time deviations from the correct course of the process and thus reduce the number of defective products within the manufacturing process. The proposed new solutions consist of (i) the data and information of the production process, acquired from sensors, (ii) a predictive model, based on the Hellwig method for errors in the production process, relying on indications of a machine status, and (iii) an information layer system, integrating the process data acquired in real time with the model for predicting errors within the production process in an enterprise resource planning (ERP) system, that is, the business intelligence module. The possibilities of using the results of research in managerial practice are demonstrated through the application of an actual burnishing process. This new framework can be treated as a solution which will help managers to monitor the production flow and respond, in real time, to interruptions.

In order to use tools, e.g. in machining, efficiently until the end of their life, predictions for the remaining tool lifespan are essential. These can only be made based on comprehensive life cycle data of the individual tools originating on different machines. Current approaches usually pursue a machine-related monitoring of the process data; a tool-related evaluation is thus not possible. The concept of the Asset Administration Shell (AAS), defined by Plattform Industrie 4.0, allows not only storing information but also describing it semantically so that interpretation is possible without a priori knowledge. They are thus suitable for making systems interoperable and can be used in the scenario described to manage the lifecycle data of tools. The AAS of a tool can move from one host to another together with the described tool and collect or provide data at the machine. A central server can be used to manage tools that are in storage and are used on any machine without a corresponding management capability. When a tool is removed from the tool magazine, the AAS is transferred to a central server where the tool information can be retrieved and from which the administration shell can be requested the next time the tool is used. While an exchange format is defined for the AAS, the necessary communication mechanisms for exchange between the central server and machines or machine-related devices must be defined. A registry in which all AAS are listed allows applications, such as an overarching user interface or an AI-based forecasting algorithm, to find the administration shells of all tools and access the individual data. The complete use case has been implemented exemplarily utilizing a central server and Sinumerik edge devices close to the machine tool. [ABSTRACT FROM AUTHOR]

This study demonstrates the result of a systematic literature review using the preferred reporting items for systematic review and meta-analysis (PRISMA) method. The main objective is to find issues that still need research on heterogeneous communication, especially 5G cellular and localization networks for small and medium-sized enterprises (SMEs) in the context of Industry 4.0. The research query is formulated based on the research questions and implemented in three selected databases. Then, the query results are categorized into six sections. Consequently, open issues addressed by the assessed papers in five general categories are analyzed and discussed. [ABSTRACT FROM AUTHOR]

Rechargeable battery production should yield highly diversified batteries, overcoming performance degradation caused by the complexity of production processes, dynamic disturbances, and uncertainties. Resilience must be achieved to overcome these limitations while satisfying the core technical requirements. This study developed an architectural framework for a cyber-physical production system (CPPS) using a digital twin (DT) to achieve resilience. Activities for resilience, operational characteristics, and CPPS were analysed to determine the core requirements. This analysis presents a novel model of activities for resilience. Moreover, the DT-based CPPS architecture, service composition procedures, and the asset description for providing inputs to the elements in the CPPS were designed according to these requirements. The proposed architectural framework applies the asset administration shell principles for efficient interoperability. The service composition procedures are classified into the type and instance phases to ensure static and dynamic technical functionalities. Moreover, the asset description is suitable to indicate the required information elements of rechargeable battery production. The DT-based CPPS was applied in a rechargeable battery production for an industrial case study to verify and validate the proposed method. The average accuracy of the DT application was 95.24%, indicating that it can provide technical functions with high accuracy. As a result, these technical functions can be executed within a sufficient action time, and the high simulation accuracy prevents performance degradation during production. Additionally, the DT is suitable for event diagnosis and provides a dynamic response. Furthermore, the proposed method can eliminate the data, analysis, and decision latencies. [ABSTRACT FROM AUTHOR]

This study synthesizes a model of the main engineering and production chains in the Smart factory of Industry 4.0. This study has three main stages: an overview of the main definitions of the concepts “engineering and production chain” and “model”, the generation of working definitions of these concepts for the purposes of this article, and synthesis of a model of the main engineering and production chains in the Smart factory of Industry 4.0. It was concluded in the course of the analysis that the defined six main engineering and production chains in the Smart factory of Industry 4.0 are part of its cyber–physical production system. The tools that support the Model of the main engineering and production chains in the Smart factory of Industry 4.0 are also synthesized in this article. The essential added value for science and production engineering of the Model in the main engineering and production chains in the Smart factory of Industry 4.0 are defined for the first time in the literature as an option to deepen the processes of re-structuring the real traditional physical production into a cyber–physical production process by integrating specialized software products in the operation of each of the defined chains and the entire cyber–physical production system.

Due to constantly changing conditions, demand, and technologies, companies increasingly seek flexibility. Productivity results from automation, improved working conditions and the focus of people in production in interaction with machines. Unfortunately, the human factor is often not considered to increase flexibility and productivity with new concepts. This work aims to develop a hybrid assistance system that allows a dynamic configuration of cyber-physical production systems considering the current order situation and available resources utilizing simulation. The system also considers human factors in addition to economic factors, which contributes to the extended economic appraisal. [ABSTRACT FROM AUTHOR]

Cyber-physical production systems (CPPS) represent a relevant aspect related to industry 4.0 and the advances promoted by the digitization and use of artificial intelligence in the production environment in the search for the development of smart factories. This study aims to assess the maturity level of cyber-physical production system (CPPS) within manufacturing industries in the Amazon. The research uses a quali-quantitative approach to analyze the problem by conducting exploratory case studies (indepth case) and the research framework used aimed to evaluate and measure the CPPS within three manufacturing industries in the Amazon (n = 3) to measure their maturity. Findings reveal a positive relationship between the type of production system adopted by the company, the level of automation, and the maturity of the CPPS. The proposed methodology can assist other companies in the development of the technological strategy, supporting the digital transformation process in order to obtain competitive advantage. The study contributes by addressing the topic of cyber-physical production systems from the point of view of operations management and strategy.

Cyber–Physical Production Systems (CPPSs) as distributed Systems of Systems (SoS) are at the center of attention from different industries. CPPSs face different categories of errors. These errors will cause failures of the entire production chain. To handle this concern, production systems should be converted into fault-tolerant production systems. To present such systems, a fault tolerance approach was developed to help possible faults prediction and detection of faults causes in this study. Also, the increasing complexity and uncertainty of CPPS call for Digital Twin (DT)-based fault tolerance approach. The proposes approach uses an extraction module to extract the faults signatures efficiently. Based on all extracted faults, appropriate responses could be generated through reliable faults patterns prediction. This method is provided using Fault Tree Analyzer (FTA), Zero-suppressed Decision Diagram (ZDD), and Support Vector Machine-Adaptive Neuro-Fuzzy Inference System (SVM-ANFIS) structure. The results based on digital twin-based CPPS of the food production system as a use case show that the proposed approach can predict reliable faults signatures to prevent failures and make a much reliable production system. Also, this method can guarantee that CPPS is up and running with optimal levels at all times. • Presenting a new digital twin-based fault tolerance approach. • Presenting an extraction module as a knowledge-based module for presenting faults and signatures of them. • Presenting prediction module as a soft computing-based module for defining reliable variable faults signatures in CPPS. • Making higher production per hour because of low average time to handle predicted faults in CPPS through proposed approach. [ABSTRACT FROM AUTHOR]

Recent technological advancements and the evolution of industrial manufacturing paradigms have substantially increased the complexity of product-specific production systems. To reduce the time cost of modelling and verification and to enhance the degree of uniformity in the modelling process of system components, this article presents a componentised framework for domain modelling and performance analysis based on the concept of "multi-granularity and multi-view" for a production line of personalised and customised products, for plug-and-play manufacturing processes to involving a large number of model input parameters. The coloured Petri net tool is utilised as a simulation tool for mapping domain models to computational models for simulation and performance evaluation. This paper presents a method for setting the input parameters of a production system when using WIP, through-put and cycle time as metrics. The results of the performance analysis demonstrate the applicability of the proposed framework and provide direction for the production line's layout design and scheduling strategy. [ABSTRACT FROM AUTHOR]

Reconfiguration demand is increasing due to frequent requirement changes for manufacturing systems. Recent approaches aim at investigating feasible configuration alternatives from which they select the optimal one. This relies on processes whose behavior is not reliant on e.g. the production sequence. However, when machine learning is used, components' behavior depends on the process' specifics, requiring additional concepts to successfully conduct reconfiguration management. Therefore, we propose the enhancement of the comprehensive reconfiguration management with transfer learning. This provides the ability to assess the machine learning dependent behavior of the different CPPS configurations with reduced effort and further assists the recommissioning of the chosen one. A real cyber-physical production system from the discrete manufacturing domain is utilized to demonstrate the aforementioned proposal. [ABSTRACT FROM AUTHOR]

With the ongoing Industry 4.0 (I4.0) revolution, plant management and supervision play a key role in the development and (re-)design of industrial plants. In the arising scenarios, the need to coordinate human workers and autonomous systems, sharing the same environment, teaming together, becomes a fundamental requirement. Indeed, even though automation in standard assembly lines has reached high efficiency and reliability, for complex and new applications, a certain amount of failure must be considered for future addressing. This paper presents a framework for the flexible coordination of such a complex and heterogeneous cyber-physical system. A digital Ttwin mirrors in real-time the plant system, while a dashboard displays plant status, providing the human operators with fundamental tools for supervision and prompt intervention in case of failure. The framework was developed and tested in an industrially relevant environment, specifically for the assembly of the interior of an aircraft fuselage. [ABSTRACT FROM AUTHOR]

The development of cyber-physical production systems (CPPSs) as the core of intelligent manufacturing systems facilitates frequent changes in product variants and batches, dynamic configurations of processing resources and relevant parameters during customized manufacturing. Production prediction, scheduling, and optimization in such a complex system require a consolidated theoretical foundation. Formal techniques, as one of the critical enablers toward this end, can be used to detect and analyse malfunctions in a CPPS based on product information before starting production. However, the inconsistency of information terminology during the interaction between heterogeneous production system components may lead to semantics-level ambiguities during the formal modelling process. This paper proposes a formal modelling framework for CPPS based on mathematical definitions of the unified 'Product-Process-Resource' concept. A factory formal model language (FFML) is defined and applied to the modelling of a real-world production scenario. The applicability and accuracy of the proposed framework are verified by transforming constructed formal models to coloured Petri-nets for simulations. The verification results have a certain guiding significance to the layout design and scheduling strategy decisions of production lines. [ABSTRACT FROM AUTHOR]