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Recent push towards sustainable development have lead machine tool builders to begin adopting novel business models which are usually peculiar in product-service-systems. In particular, business models as 'pay-per-use' introduced the issue of rental of production capacity integrated with the management of the life cycle of assets in a Circular Economy perspective. However, when it comes to machine tool builders, the high degree of customization of capital goods and in particular of multi-stage automated assembly systems that are strongly linked to the characteristics of the final product make this solution more difficult. Hence, the implementation of such business models represents a clear challenge for machine tool builders and manufacturing system suppliers from technological perspective. This work discusses preliminary results in the technological implications of such strategy. A real industrial case related to cross-sector multi-stage automated assembly systems is presented to highlight the technological challenges and potential research directions. [ABSTRACT FROM AUTHOR]

From both economic as well as environmental perspective energy demand is of strong and even more increasing relevance for manufacturing nowadays. But typically energy demand and manufacturing system performance (e.g. output, time demands, quality) are considered separately while actually being inherently connected in technical systems. Against this background, the paper presents a data-based approach to analyze and model those important aspects and interdependencies in an integrated manner. The developed theoretical framework and defined methodology enable deeper understanding of energy related impacts of operative influencing factors and eventually to derive fields of action for improvement. The approach was exemplary applied in an industrial case study. Results underline the feasibility, applicability, and potential benefits of the approach. [ABSTRACT FROM AUTHOR]

The Circular Economy is struggling to establish itself in manufacturing systems, with the latest Circular Gap report showing that circularity is declining as the environmental situation worsens. Against this backdrop, several researchers have suggested that implementing emerging digital technologies of Industry 4.0 could be a key resource in this transition towards greater circularity in production. As circular manufacturing systems imply a relatively high complexity of flows, emerging technologies could be particularly useful in this context. After a brief review of the literature on material, information and human flows, a coupling with digital technologies is proposed to address the challenges of flow management in the circular economy. A critical analysis of the research on the use of digital technologies in the management of flows in circular manufacturing systems is then carried out. Finally, the perspectives for research in this area are presented, emphasising the need for multidisciplinary research and distinguishing the areas of research that can be commissioned to fill these research gaps. As part of a multidisciplinary project combining engineering, humanities and social sciences, this research prioritises an approach that equally integrates the three pillars of sustainable development (financial and technical, environmental and social). [ABSTRACT FROM AUTHOR]

A manufacturing system can simultaneously exploit more strategies from the Circular Economy paradigm, such as reduction, reuse, repair, refurbishment, and remanufacturing. These strategies involve finished products, raw materials, and components, and each adopted strategy can unpredictably impact the system performance along one or more dimensions (technical, economic, and environmental) at both the planning and the life cycle levels. This paper deepens the simultaneous measurement of the multi-dimensional and multi-level (strategic and tactical planning and life cycle design) performance of the manufacturing system in the presence of circular economy strategies. The Multi-layer Enterprise Input-Output (MEIO) formalisation method is extended to integrate Life Cycle Assessment (LCA) and Life Cycle Costing (LCC) approaches to perform scenario analyses. The proposed approach is applied to a numerical example to show the impacts of adopting new processes for circular economy strategies in a brownfield context. The considered scenarios combine different circular economic strategies applied to different levels of the Bill of Materials of a multi-product environment. The proposed approach can help practitioners and production managers evaluate adopting circular economy strategies. Moreover, the approach can support the development of methods for simultaneous multi-dimensional performance assessment of manufacturing systems at the operational level. [ABSTRACT FROM AUTHOR]

The integrated product life cycle encompasses key stages in product development, production, and maintenance. Effective management of these stages is vital for operational efficiency and competitiveness. However, the impact of design, product configuration, and operational changes on manufacturing performance requires further investigation. This systematic literature review examines the interplay between the manufacturing systems' life cycle, design, configuration, and operations. Literature emphasizes the integration of software and digital tools to achieve operational excellence and competitiveness. The findings offer valuable insights for future research and aid decision-making for practitioners seeking to enhance manufacturing processes through effective integration of product, manufacturing, and digital technologies. [ABSTRACT FROM AUTHOR]

The complexity in the collaboration between humans and robots in smart manufacturing remains a significant challenge. This paper introduces an LLM-based manufacturing execution system enhancing Human-Robot Collaboration (HRC) in smart manufacturing. By leveraging Large Language Models (LLMs), the system provides a natural language interface for operators, integrates with Digital Twins for real-time data, and employs behavior-based control for robots. This integration facilitates intuitive interactions and rapid system programming, addressing communication complexities in HRC. The effectiveness of this approach is validated through two HRC assembly case studies, demonstrating significant improvements in collaboration and efficiency. [ABSTRACT FROM AUTHOR]

The classical manufacturing systems assume that all produced items are of perfect quality. They also need to consider the rework process in manufacturing operations. Moreover, most of the previous literature considers single-stage production-inventory systems and does not consider multi-stage options. However, in real-world production-inventory systems, defective items are inevitable, and a fraction of the produced items may be defective. In addition, to avoid extra costs and consider environmental issues, organizations tend to rework activities. We propose single and multi-stage production-inventory systems in manufacturing operations where the process is defective, rework is possible, and a percentage of items are scrapped. A main assumption in the current paper is that the defective rate is assumed to be an uncertain parameter. The grey systems theory, as a mathematical tool to address uncertain information in real-world situations, is utilized to model the random defective rate via a grey nonlinear programming problem. The proposed issues are investigated via numerical examples to assess the impact of grey parameters on optimal solutions. [ABSTRACT FROM AUTHOR]

Workforce assignment (WA) is a crucial aspect of production planning and control. However, current global crises have led to workforce absence and skill shortages. To tackle these challenges, a short-term WA approach is necessary for manufacturing systems flexibility and workforce health. Conventional methods for WA face complexity issues due to complex manufacturing systems, requiring substantial computation time. A Quantum Annealing-based WA approach is proposed in this paper to allocate tasks and shifts to workers. The proposed method is validated in a use case, showing improved solution quality compared to traditional methods. [ABSTRACT FROM AUTHOR]

Based on the availability of sensors, data and connectivity in manufacturing systems, Digital Twins (DTs) are increasingly used, e.g. for improving processes through advanced prediction or monitoring capabilities. With the increasing dissemination of the DT concept, it is to be expected that in the future there will be several DTs in a manufacturing system, each of which will pursue a different purpose and process different data. As a consequence, these DTs bring individual benefits, but are mostly used within a limited manufacturing system area. In addition to the originally intended use of individual DTs, this results in the potential to use their respective results or output data for other purposes in the manufacturing system (additional benefits). Therefore, the question of administrating several DTs within the same manufacturing system arises. While not only preventing redundancies, this also enables the synergetic use of DT capabilities. In order to systematize this benefit, this paper describes a procedure with which DTs can be integrated into the structure of a manufacturing system. Subsequently, a method for the systematic achievement of the described additional benefits is elaborated. The resulting overall procedure is illustrated using an exemplary application scenario. [ABSTRACT FROM AUTHOR]

Studies affecting manufacturing system resilience have been of rising interest in recent years due to increased rates of naturally caused, and socio-economic disturbances. The non-linearity in modern manufacturing systems, attributed to a host of internal and external complexities in structure and operations cannot be properly modeled using conventional white-box techniques. This paper focuses on building a black-box description of a manufacturing system as a solution, addressing the inherent non-linearity, and further supporting fast investigations on a larger solution space with the example of an application scenario. This work acts as a precursor towards the implementation of a conceptual workflow describing a cross-domain application of machine learning (ML) for optimizing manufacturing system resilience. [ABSTRACT FROM AUTHOR]

Engineering design is the basis for technological advancement and the development of new products. Many changes have emerged over the last few decades due to scientific advancements, evolution in novel engineering materials, advances in computational technology, the opening up of the global village, economic constraints, short life spans of products, and the need for a responsive life cycle design paradigm in engineering products. To meet customer or operational requirements, a certain design methodology is used to design and develop products. This has resulted in notable growth in product design methodologies across disciplines, particularly architecture, engineering, and industrial designs. The engineering design methodology has addressed design procedures, epistemological issues, and working procedures in the design disciplines for several decades. In this article, the research seeks to delve into novel design methodologies currently in use and interrogate their limitations and advantages to create synergies among the best practices in engineering design approaches, with particular emphasis on responsiveness to continuously changing customer requirements. To achieve this, special emphasis will be placed on a design methodology for life cycle, end-of-life, concurrent engineering, eco-design, and reconfigurable design. Based on a strategic focus, the drive is for innovative engineering design that requires design approaches that respond to continuous product changes. The results from this study demonstrate the relationships between engineering design methodologies and solution models that respond to industry and customer requirements. [ABSTRACT FROM AUTHOR]

The combination of innovative advance and fluctuating client needs are paving the way to continuous changes in the manufacturing environments. As a result, mechanical enterprises aim for the honing of their processes by means of Computer Integrated Manufacturing (CIM). Their holy grail is connecting the shop floor systems to the high business layer systems. Based on our previous literature review on the CIM architectures available in the literature, we have identified six major aspects and detected the limitations of the existing appeals. We attempted to get the better of these fall backs and to be as exhaustive as to cover all 6 aspects through the BLAEM Architecture (Bi-Level Architecture for efficient Manufacturing). Through this paper, we will be presenting an execution of the BLAEM by means of a real case study. [ABSTRACT FROM AUTHOR]

Two-Sided Assembly Line Balancing (2S-ALB) is essential to the production of large-sized high-volume products, including automotive production, at assembly plants. The 2S-ALB problem involves different assembly resources such as worker skills, tools, and machines required for the assembly. This research modeled and optimized the 2S-ALB with resource constraints. In the end, besides favorable workload balance, the number of resources can be optimized. For optimization purpose, particle swarm optimization was modified to reduce dependence on a single best solution. This was conducted by replacing the best solution with the top three solutions in the reproduction process. Computational experiment results using 12 benchmark test problems indicated that the 2S-ALB with a resource-constrained model was able to reduce the number of resources for use in an assembly line. Furthermore, the proposed Modified Particle Swarm Optimization (MPSO) was capable of searching for minimum solutions to 11 out of 12 test problems. The good performance of MPSO was attributed to its ability to maintain particle diversity over iterations. The proposed 2S-ALB model and MPSO algorithm were validated later using an industrial case study. This research makes a two-fold contribution: (a) Proposition of a novel 2S-ALB with resource-constrained model and (b) a modified PSO algorithm with enhanced performance. [ABSTRACT FROM AUTHOR]

In the medium- and long-term future, quantum technologies will become a megatrend shaping global industrial value creation. Quantum technologies will open novel pathways for manufacturing systems innovations, while also providing new opportunities for Sustainable Development. Manufacturing companies must already strive for a better understanding of quantum technologies and their potential applications if they are to maintain and improve their competitive advantage in the future. An overview of the theoretical background and state-of-the-art for quantum technologies and manufacturing systems is presented. Fields of application for quantum technologies are explored in the context of relevant manufacturing systems domains. Potential applications are proposed based on an analysis and synthesis of relevant literature, industrial case studies, as well as the authors' ideations. A case is laid out for how quantum technologies can potentially facilitate industrial symbiosis within limited global ecological boundaries. In other words, the case study describes how Sustainable Development can be potentially facilitated using quantum computing and quantum AI. [ABSTRACT FROM AUTHOR]

The paper explores and presents results of an investigation handling design methodologies using the bio-space and biological phenomena combined with the technical space for the development, design and application of products, machine tools, processes and manufacturing systems carried out by a multinational team including Fraunhofer and CIRP fellows. The main biological approach for designing was bio-inspiration. However, links and examples implementing bio-integration and bio-intelligence were also considered and analyzed during the investigation. The paper describes a systematic design procedure starting from requirements and applications related to manufacturing, using well-known theories and experience in engineering, however analyzing and applying biological materials, properties, structures, phenomena and complete systems. Sustainability aspects combined with the immense and ever growing capacity of ICT and digitization possibilities provided new options for design methodologies. A moveable and flexible machine tool was virtually used as a demonstrator to describe the new methodology. It was proved that the ongoing process of learning from nature and applying biological phenomena, structures and materials, together with new technologies such as Additive Manufacturing (AM) can develop new ideas, processes, manufacturing systems, and products with benefits to performance, productivity, efficiency and sustainability. Nature and biological domains can be integrated, can inspire, be implemented or even applied as an intelligent system in the design procedure. [ABSTRACT FROM AUTHOR]

Digitalization and Industry 4.0 are promising developments for solving current technical challenges. However, to fully exploit the potential of digitalization and Industry 4.0, not only manufacturing and information systems must interact, but biological systems too. The interaction of biological, manufacturing and information systems is an emerging research field defined as Biological Transformation in manufacturing or biologicalisation in manufacturing. This publication introduces three different projects that represent the application of Biological Transformation in manufacturing to inspire and initiate further research in this area. The interactions of the three system types are described in detail, and their potential for the manufacturing sector is discussed in reference to the framework Biological Transformation in manufacturing. [ABSTRACT FROM AUTHOR]

This paper reports on a highly ambitious international study undertaken in the period 2018–2020 on the topic of convergence between biology and advanced manufacturing systems. The international team (authors of this paper) worked together to analyse the status of this convergence through the assessment of concrete examples, referred to here as demonstrators, within advanced manufacturing systems. Four independent demonstrators from different sections of the manufacturing value chain and involving bio-inspiration, bio-integration and/or bio-intelligence were selected to test the following hypothesis: "That Future Manufacturing Systems will incorporate Components, Features, Characteristics and Capabilities that enable the convergence towards Living Systems". Each of these four demonstrators have succeeded in supporting this hypothesis and in providing clear evidence to confirm that significant performance benefits may be derived through the " biologicalisation " of advanced manufacturing systems. This conclusion is of great significance for the next phases of development of manufacturing science and engineering globally. The evidence reported in this paper provides a robust basis for recommending that a deeper analysis of the implications of biologicalised manufacturing systems be undertaken. As a result of this early stage work, it is concluded that there is a high likelihood that this new convergence will lead to a major paradigm shift in advanced manufacturing. Outstanding opportunities exist for high levels of innovation in the next stages of development of advanced manufacturing processes and systems from the biological perspective. The relationship between the human and the physical manufacturing system will also change and the world of advanced manufacturing will be confronted with many new challenges including important ethical questions. [ABSTRACT FROM AUTHOR]

The evolution of manufacturing systems, influenced by changes along four axes - products, technology, business strategies and production paradigms - is presented. Adoption of human-centric decision making in meshed collaboration with intelligent systems is examined. Implications and preparedness for the shift towards more responsive, intelligent adaptive systems are reviewed. Research and industrial use cases are presented. A vision for the new future Adaptive Cognitive Manufacturing System (ACMS) paradigm and its characteristics, drivers and enablers are articulated highlighting the digital and cognitive transformations. Perspectives and insights are offered for future research, education, and work to realize the evolution of manufacturing systems. [ABSTRACT FROM AUTHOR]

The paper is devoted to the mathematical problems of building control systems of modern and promising production systems that ensure the adaptation of production processes to changes in the external and internal conditions of the system. The proposed algorithm for solving the scheduling problem is based on a modification of the algorithm for finding the shortest path between the vertices of the graph. It is a simple and effective tool for solving the problem of minimizing the changeover time. Its effectiveness has been proven. The algorithm has received an adaptive extension when applied in cases where it is necessary to take into account random influences on the production process. For this purpose, planning algorithms are considered, taking into account the actual noise and interference. Computer algorithms for production planning using a system of correction tolerances for the required work schedules and equipment loading are synthesized. [ABSTRACT FROM AUTHOR]

Asynchronous systems with deterministic processing times are critical for fabricating discrete parts, especially in countries offering high salaries. Despite their widespread application, it is difficult to analyze these systems using conventional analytical modeling tools as commonly used hypotheses for these models consider different conditions compared to real behaviors. This paper presents a new analytical model for the performance evaluation of asynchronous two-stage manufacturing lines fabricating discrete parts with finite buffer capacity. A continuous-time Markov chain flow model that mimics the behavior of discrete asynchronous manufacturing systems by employing threshold-based control mechanisms is presented. The results obtained with the proposed model are compared with those using discrete event simulation, and they show good agreement. The model proposed in this paper can be used as a building block for the evaluation of longer asynchronous lines. [ABSTRACT FROM AUTHOR]

Application of reconfigurable manufacturing systems (RMS) plays a significant role in manufacturing companies' success in the current fiercely competitive market. Despite the RMS's advantages, designing these systems to achieve a high-efficiency level is a complex and challenging task that requires the use of optimization techniques. This study proposes a simulation-based optimization approach for optimal allocation of work tasks and resources (i.e., machines) to workstations. Three conflictive objectives, namely maximizing the throughput, minimizing the buffers' capacity, and minimizing the number of machines, are optimized simultaneously while considering the system's stochastic behavior to achieve the desired system's configuration. [ABSTRACT FROM AUTHOR]

The corporate environment is characterized by shorter product lifecycles with a growing number of variants, a high volatility in demand and increasing globalization. Manufacturing companies are therefore faced with the challenge of selecting an economically as well as ecologically reasonable operating point for their manufacturing system, while meeting volatile demands. In the present paper, the systematic approach for multi-objective operating point optimization comprises a six-step approach and contributes to the economic and ecological evaluation of operating points. Finally, an industrial evaluation of the systematic approach is shown on the basis of a case study within the plastics industry sector. [ABSTRACT FROM AUTHOR]

Manufacturing systems need to be highly adaptive and flexible to meet heterogeneous customer requirements and require fast and accurate object identification. This paper presents the implementation of deep learning for the object detection in a factory in which images of machines are used to train four models. For each model, three trials with different hyperparameter configurations are performed. After the training, evaluations based on the loss curves, mean average precision curves, and performance metrics are carried out. Finally, the trained models are tested with a video in order to select the model with the best detection performance and accuracy. [ABSTRACT FROM AUTHOR]

Volatile markets and high customer requirements regarding schedule reliability increase the relevance of robust production planning. To achieve robustness in planning, system-inherent buffers are used. Buffers include resource capacities that are kept free to respond to changes. Targets other than achieving the production plan are not considered, so a trade-off between reliability and the further development of the manufacturing process is not possible. This paper presents a new approach for production planning based on robustness analysis that enables a multi-criteria optimization. An information system enables the company-specific design of the robustness analysis. [ABSTRACT FROM AUTHOR]

Deriving conclusions for manufacturing systems from strategically planned external scenarios can be difficult, as external developments are not usually directly linked to manufacturing systems. Understanding production planning, scheduling, and control as a filter for external developments helps to make scenario-based planning more tangible. The presented approach aims to identify external scenarios and translate those into input variables for manufacturing systems to provide a systematic and transparent starting point for risk management and change processes. Therefore, strategic planning methods become interconnected with a receptor model for the operation of manufacturing systems. [ABSTRACT FROM AUTHOR]

In manufacturing systems characterized by job shop architectures, long lead times, and multiple part types, operators are important resources of the system, because they supervise and actively support the technical operations of machines. However, Industry 4.0 tools, as digital twins of manufacturing systems, rarely consider the human aspect. As a consequence, the real impact of workforce on production performance cannot be assessed precisely. In this paper, the effect of work-force availability on manufacturing systems operations is shown, with a special focus on job-shop systems. Results show that there is a strong relation between the operators management and the short-term production planning. The method is applied and validated within a real industrial case in the aeronautics sector, characterized by high value-added complex parts. [ABSTRACT FROM AUTHOR]

This paper presents a Markov Chain approximation to model stations in manufacturing lines with general distributed processing times. The proposed Markov Chain approximation enables the use of continuous flow models for the performance evaluation of serial lines with finite buffers and mixed manual – automated operations. Each station in the line can consist of a highly automated machine with deterministic processing times, or of a human operator performing manual operations with general distributed processing times. Stations with random processing times are modelled through a continuous time – discrete state Markov Chain characterized by an operational state with a deterministic processing time, and by an auxiliary down state used to stochastically dilate the overall completion time of a part on the station. The Markov Chain parameters are defined through moments fitting of the probability distribution of the processing time of the original station. The resulting Markov Chain represents the behavior of the station in isolation and is then used as input in the decomposition techniques, based on continuous flow models, for the performance evaluation of serial lines. The model has been applied in the analysis of the production performances of a real assembly line. [ABSTRACT FROM AUTHOR]

The paper explores and presents results of an investigation handling design methodologies using the bio-space and biological phenomena combined with the technical space for the development, design and application of products, machine tools, processes and manufacturing systems carried out by a multinational team including Fraunhofer and CIRP fellows. The main biological approach for designing was bio-inspiration. However, links and examples implementing bio-integration and bio-intelligence were also considered and analyzed during the investigation. The paper describes a systematic design procedure starting from requirements and applications related to manufacturing, using well-known theories and experience in engineering, however analyzing and applying biological materials, properties, structures, phenomena and complete systems. Sustainability aspects combined with the immense and ever growing capacity of ICT and digitization possibilities provided new options for design methodologies. A moveable and flexible machine tool was virtually used as a demonstrator to describe the new methodology. It was proved that the ongoing process of learning from nature and applying biological phenomena, structures and materials, together with new technologies such as Additive Manufacturing (AM) can develop new ideas, processes, manufacturing systems, and products with benefits to performance, productivity, efficiency and sustainability. Nature and biological domains can be integrated, can inspire, be implemented or even applied as an intelligent system in the design procedure. [ABSTRACT FROM AUTHOR]

Manufacturing systems are subject to continuous changing conditions, which are due both to external reasons (e.g. changing demand) and to the natural system evolution, (e.g. machine degradation, operators' upskilling). At tactical level, production engineers are challenged to continuously improve the system performance. At strategical level, the manufacturing company must monitor the system status and proactively identify reconfiguration actions to ensure system fitness to the evolving competitive scenario. A novel Digital Twin based on an analytical model for performance evaluation of manufacturing system embedding evaluation of joint parameter variations is introduced. In particular this work concentrates on how tactical decision makers can benefit from an integrated system model. The method is proved in a real industrial case in the railway sector. [ABSTRACT FROM AUTHOR]

Digitalization and Industry 4.0 are promising developments for solving current technical challenges. However, to fully exploit the potential of digitalization and Industry 4.0, not only manufacturing and information systems must interact, but biological systems too. The interaction of biological, manufacturing and information systems is an emerging research field defined as Biological Transformation in manufacturing or biologicalisation in manufacturing. This publication introduces three different projects that represent the application of Biological Transformation in manufacturing to inspire and initiate further research in this area. The interactions of the three system types are described in detail, and their potential for the manufacturing sector is discussed in reference to the framework Biological Transformation in manufacturing. [ABSTRACT FROM AUTHOR]

Traditionally, performance, time and cost have been the main guidelines for production systems design, but other aspect such as human factors and safety are starting to emerge and take a main role for designers. Considering these findings, we are developing a multi-criteria design framework based on a Behavior-Energy-Structure model (BES) that makes use of energy flow through components to characterize its behavior via Energetic Technical Functions and their structural properties. This approach eases the incorporation of human factors and safety through the design phases, allowing designers to create safe, reliable and efficient production systems. [ABSTRACT FROM AUTHOR]

The importance of quality and efficiency has increased in recent years. Moreover, the rise of computational power and the development of advanced analytics has enabled the industry to enhance the performance of manufacturing systems. Therefore, further transparency of intermediate product states is necessary to derive appropriate actions. The goal of this paper is to develop a framework to enable the data-driven analysis of product state propagation within manufacturing systems to improve the transparency of product quality related cause-effect relationships. Based on their intermediate product features, machine learning algorithms assign products to classes of similar characteristics. This approach is practically applied to a case study from the electronic production industry. By using visual analytics tools, the propagation of product states along the manufacturing process chain is exemplarily analyzed. [ABSTRACT FROM AUTHOR]

A Digital Twin can be utilized for prospective planning, analysis of existing systems or process-parallel monitoring. In all cases, the Digital Twin offers manufacturing companies room for improvement in production processes leading to cost savings. However, many companies do not apply the technology, because the generation of a Digital Twin is cost-, time- and resource-intensive and IT expertise is required. This paper presents an approach for generating a Digital Twin efficiently, in which the obstacles will be overcome by using fast scans of the shop floor and subsequent object recognition. The focus here is on creating a simulation model. [ABSTRACT FROM AUTHOR]

In the recent years, many methodologies and tools to support the energy efficiency re-design of production systems have been developed, however, they do not investigate the real-time manufacturing process. In this paper, a methodology for energy efficiency re-design of production systems in a context of smart manufacturing is proposed. The continuous production-machine data collection with operator feedbacks enables the creation of a knowledge-based repository that provides useful support during the design of manufacturing systems. A case study in an automotive sector company has allowed to implement the methodology and to assess its effectiveness. [ABSTRACT FROM AUTHOR]

Reliability is the measure of the likelihood that a product, system or service will perform its intended function adequately for a specified period of time. Low reliability of a manufacturing system, besides the costly repairs and replacements, also implies reduced production, and consequently, significantly reduced profits. Therefore, it is very important to have a way to assess reliability, as a key performance metric for manufacturing systems, and cyber-physical systems in general. The newly developed information and communication technologies that are increasingly becoming part of the current and future manufacturing systems, both allow and invite for more sophisticated approaches to assessing reliability of manufacturing systems as opposed to the traditional expert knowledge-based approaches. In this paper, we describe the significance of evaluating reliability for the progress and acceptance of the Industry 4.0 technologies, as well as the new directions and possibilities for enhanced reliability analysis that these new technologies can provide. Finally, we provide an overview of the implications of these novel ways of analyzing reliability in the context of Industry 4.0. [ABSTRACT FROM AUTHOR]

Failures on machine tools not only occur on main components, but also on auxiliaries like cooling units or oil mist separators, which causes productivity losses similar to failures on main machine components. Due to their separation from the machine's control network, their health status is in most cases not monitored. In this study, a new approach for online condition monitoring of auxiliary units by the example of an oil mist separator connected to a 5-axis machine tool is presented. The data is analyzed via machine learning principles in order to deduct an adequate condition assessment, encompassing environmental influences. [ABSTRACT FROM AUTHOR]

Reliability is the measure of the likelihood that a product, system or service will perform its intended function adequately for a specified period of time. Low reliability of a manufacturing system, besides the costly repairs and replacements, also implies reduced production, and consequently, significantly reduced profits. Therefore, it is very important to have a way to assess reliability, as a key performance metric for manufacturing systems, and cyber-physical systems in general. The newly developed information and communication technologies that are increasingly becoming part of the current and future manufacturing systems, both allow and invite for more sophisticated approaches to assessing reliability of manufacturing systems as opposed to the traditional expert knowledge-based approaches. In this paper, we describe the significance of evaluating reliability for the progress and acceptance of the Industry 4.0 technologies, as well as the new directions and possibilities for enhanced reliability analysis that these new technologies can provide. Finally, we provide an overview of the implications of these novel ways of analyzing reliability in the context of Industry 4.0. [ABSTRACT FROM AUTHOR]

Manufacturing systems are among the main consumers of energy. Several methods and tools have been developed to support companies toward energy efficiency. However, they generally require high computational effort, onerous measurement campaigns or complex models. In this paper, a method to perform a lean energy assessment starting from the asset characteristics is proposed. It is based on a set of corrective factors that consider both technical and productive aspects. The final goal is to overcome the lack of precise data needed to identify hotspots from the energy point of view. A real industrial case study allows implementing the method and demonstrating its usefulness. [ABSTRACT FROM AUTHOR]

Typically, development of changeability and reconfigurability in manufacturing are greenfield approaches. New platforms, products and manufacturing systems are developed with new features, capability or technology. While companies can achieve the desired level and type of changeability with greenfield approaches, development of new platforms and systems is a costly affair if existing systems and platforms in the company is not considered. This study outlines an approach for systematic brownfield platform development. Seven stages are listed, describing how candidates for inclusion in a platform are identified and subsequently developed based on existing manufacturing systems and production landscape. [ABSTRACT FROM AUTHOR]

In the course of digitalization many small and medium-sized companies face the challenge of using the existing database for process optimization in manufacturing. Furthermore, the demand-oriented expansion of the database is a great challenge. A lack of competencies, limited financial resources and historically grown data structures, which show a strong heterogeneity and lack of transparency, are the central obstacles. A specific approach, how data analytics projects for process optimization should be carried out in manufacturing, is presented. In particular, the question which sensors should be implemented to expand the database is answered. The approach is applied exemplarily for a manufacturing line. [ABSTRACT FROM AUTHOR]

In manufacturing context, social dimension is often neglected. With Industry 4.0, companies focus more on technologies and data. However, human continues to play a key role in cyber-physical systems and company growth. This work proposes a method to help the company to evaluate workers' experience and identify the optimal solution to improve workers' well-being and company performance. It starts from personalized social analysis within a production plant to identify ergonomics problems and intelligently suggest effective corrective actions. The latter are selected achieving the best trade-off between social, economic and productive aspects. Three case studies are proposed to validate the method. [ABSTRACT FROM AUTHOR]

Traditional sustainability assessment conducts life cycle economic, environmental, and social impact assessment individually and utilizes multi-criteria or optimization methods for decision making. This method is usually challenged by systems thinkers who believe that system is an integrated whole with elements interconnected and the analysis requires continuous learning and understanding. In manufacturing systems, there is still a lack of understanding on linkages of economic, environmental and social activities and impacts, especially the interactions based on manufacturing system behaviors. This hinders manufacturer strategic and tactic decision making at unit process, shopfloor, and enterprise levels. This articles proposes a dynamic sustainability assessment method based on system dynamics modeling of behaviors and systemically connects economic, environmental, and social impact assessments in manufacturing system sustainability assessment. The method identifies sustainable manufacturing problems with characteristics. Creative method design from systems science is used to incorporate knowledge from system dynamics, risk assessment, sustainability assessment, and life cycle engineering to tackle complex problem, and design the method structure. A case study demonstrated that modeling based method is able to assist manufacturing practitioners and researchers understand the underlying economic, environmental, and social relations in manufacturing systems. [ABSTRACT FROM AUTHOR]

Abstract The need for tools to help guide decision making is growing within the manufacturing industry. The analysis performed by these tools will help operators and engineers to understand the behaviour of the manufacturing stations better and thereby take data-driven decisions to improve them. The tools use techniques borrowed from fields such as Data Analytics, BigData, Predictive Modelling, and Machine Learning. However, to be able to use these tools efficiently, data from the factory floor is required as input. This data needs to be extracted from two sources, the PLCs, and the robots. In practice, methods to extract usable data from robots are rather scarce. The present work describes an approach to capture data from robots, which can be applied to both legacy and current state-of-the-art manufacturing systems. The described approach is developed using Sequence Planner – a tool for modelling and analyzing production systems – and is currently implemented at an automotive company as a pilot project to visualize and examine the ongoing process. By exploiting the robot code structure, robot actions are converted to event streams that are abstracted into operations. We then demonstrate the applicability of the resulting operations, by visualizing the ongoing process in real-time as Gantt charts, that support the operators performing maintenance. And, the data is also analyzed off-line using process mining techniques to create a general model that describes the underlying behaviour existing in the manufacturing station. Such models are used to derive insights about relationships between different operations, and also between resources. [ABSTRACT FROM AUTHOR]

Abstract The advancing digitalization of industrial processes offers new possibilities for improving monitoring and control of factory systems. To enhance the monitoring of energy efficiency, an indicator system was developed that displays the efficiency depending on relevant physical inputs and outputs. These Process Status Indicators can characterize single processes either time-discretely or time continuously. The behavior of the indicators is analyzed regarding an energy converting process. Exemplarily, the generation of compressed air and the influence of using waste heat are observed. The results of the simulative investigation depict that the time continuous indicators can represent the efficiency regarding all considered characteristics. [ABSTRACT FROM AUTHOR]

Reconfigurable manufacturing systems (RMS) are designed to improve responsiveness and adaptability to individualized demands, creating a potential solution for mass personalization. System reconfigurations provide flexibility to fluctuating demands, and can be enhanced by adjustments of machine components. However, improper balancing between maintenance and reconfiguration actions can result in system breakdowns and can hamper system health and ability to reconfigure. This paper proposes a degradation-aware RMS decision-making model to optimally determine and adjust operational actions in real-time considering demand fulfilment, maintenance cost, and system health. The proposed approach has the capability to capture the causality between operational action sequences and the resulting system deterioration through artificial intelligence-based methods. [ABSTRACT FROM AUTHOR]

Abstract In the current manufacturing context, characterized by short product life-cycles, large product variety, product customization and short innovation cycles, achieving target production quality performance is challenging, especially due to the frequent ramp-up phases the system undergoes along its life-cycle. Available production quality methods focus on high-volume productions and long-term system performance, while they lose effectiveness during the system ramp-up, where instability and unknown disturbances affect the system dynamics. This paper proposes a reference framework for improving production quality performance during the system ramp-up phase. Two strategies for properly dealing with this problem are discussed, consisting in anticipating ramp-up problems during the design phase and performing continuous improvement of production quality performance measures during the system ramp-up. The most effective approaches following these strategies are revised and future research directions in this new research area are drawn. [ABSTRACT FROM AUTHOR]

Increasing product variety and the shortening of product lifecycles require a fast and inexpensive reconfiguration of existing manufacturing automation systems. To face this challenge one solution is a Digital Twin, which can be used to reduce the complexity and time of reconfiguration by early detection of design or process sequence errors of the system with a cross-domain simulation. For engineering the Digital Twin and systemically synchronizing the data of mechatronic components in the interdisciplinary engineering models of a Digital Twin during the life cycle of manufacturing automation systems, this paper presents a concept for the engineering of a Digital Twin based on model integration in a PLM IT-Platform and an Anchor-Point method to systematically detect variances of the mechatronic data structure between the digital models and the physical system. The data of a mechatronic component from interdisciplinary domains, developed by the corresponding engineering tools are referred to as anchor points. This paper analyses domain-specific challenges in automation software-code to develop an assistance system for rule-based consistency check and for synchronizing the engineering models of the Digital Twin of the manufacturing automation system based on the Anchor-Point method. [ABSTRACT FROM AUTHOR]

Innovative processes become available due to the high processing capacity of emergent infrastructures, such as cloud and ubiquitous computing and organizational infrastructures and applications. However, these intense computation processes are difficult to follow, where co-decision is required, for which the existence of disruptive visualization and collaboration tools that offer a visual tracing capacity with integrated decision supporting tools, are critical for its sustainable success. This project proposes: a) a set of immersive and disruptive visualization tools, supported by virtual and augmented reality, that enables a global perspective of any production agents; b) a data analytics tool to complement and assist the decision making; c) a resource federated network that allows the brokering and interaction between all existing resources; and d) a dynamic context-aware dashboard, to improve the overall productive process and contribute to intelligent manufacturing systems. The application domain addressed is Zero-Defects Diagnostics in manufacturing as well as in Industry 4.0 in general. [ABSTRACT FROM AUTHOR]

Process modularity in terms of mass customized manufacturing is considered as a key criterion in designing process architecture. The impact of process modularity on effectiveness of mass customized can be almost predicted based upon experience with traditional mass production. This study firstly analyses relationship between product modularity and process modularity. Then, in order to identify possible tolls for process modularity management, potential process modularity indices are compared and assessed on testing examples. Subsequently, theoretical principles for designing manufacturing and assembly process structures are formulated. The results showed that management of process architecture in mass customized environment might not be conducted in isolation from management of product development. [ABSTRACT FROM AUTHOR]

Digitalization allows for production control based on the current state of the manufacturing system. Thereof, this paper proposes and applies a data-driven adaptive planning and control approach that uses simulation-based optimization to determine most suitable dispatching rules in real-time under varying conditions. The data integration between the real manufacturing system and the simulation model is implemented through a data-exchange framework. The approach is evaluated in a scenario of a Brazilian manufacturer of mechanical components for the automotive industry, achieving better operational performance compared to the procedure previously applied by the company as well as in comparison to static dispatching rules. [ABSTRACT FROM AUTHOR]