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In the past decades, the German paper industry has been increasing the sustainability of paper production by continuously using more recovered paper. Today, recovered paper is an essential secondary raw material to produce new paper. Nevertheless, around 20 % of the paper produced in Germany is still not returned to the recovered paper stream and instead is mostly thermally used. To further increase the use of fibers from recovered paper as secondary raw material and thus, to reduce primary energy consumption and greenhouse gas emissions, new approaches to recycle recovered paper from mixed waste streams are needed. In the research project "Energy savings in paper production by opening up the value chains of recovered paper from lightweight packaging, residual waste and commercial waste" (EnEWA), a treatment process is developed to recover and recycle recovered paper from the mentioned waste streams. The advanced mechanical process to be newly developed consists of dry-mechanical sorting, the manufacturing of economically usable secondary raw materials (defibration), their wet-mechanical dissolution, separation and hygienization and finally their reuse in paper production. This enables the exploitation of new waste streams in material recycling and thus contributes to reduced energy consumption as well as CO2 emissions within the circular economy. Initial material characterization analyses show that on the one hand the quality of the paper discarded in mixed waste streams is sufficient to be used as secondary raw material and that on the other hand up to 50 % of the paper discarded could have been disposed of within the separate paper collection. The results are incorporated in the development of a sorting process for pre-concentrates, which serves as input material for the treatment process to recover paper fibers. In further steps, the results generated will be integrated, for instance, into the future development of the minimum standard for packaging recycling. [ABSTRACT FROM AUTHOR]

The dissemination of emerging electronic devices increases considerably alongside with the Internet of Things (IoT) paradigm. Some key elements considered in these devices (e.g. rare-earth) are becoming critical materials and are associated with serious environmental concerns. Therefore, there is a major requirement for designing differently our electronic technologies at the early stages of the research process. Many reports present the promising properties of paper-based electronics as meant to address this challenge. Through technical and life cycle assessments, this article discusses the main design levers in terms of material and fabrication process choices for the eco-design of paper-electronics water quality sensors. Our results suggest conscientious choices of conductive nanomaterials and additive fabrication techniques. [ABSTRACT FROM AUTHOR]

In Paper and Pulp (P&P) industry resource management scenarios, such as processing cell(s) efficiency reduction, equipment break-down or failures, alternative energy source (biomass and solar energy) utilization/ synchronization with regular energy sources and management decided change in production rate or product quality often occur in industrial processes causing material flow disruption, energy and production losses. In such scenarios, immediate and appropriate actions are required to be taken to minimize energy and production losses. Emerging resource management options tackle these issues through four different ways i.e., by mixing of readymade pulp at various processing stages, by varying the percentage of input raw material, by adopting a parallel utilization of alternative energy sources and by switching on/off some processing units or parallel working segments. These ways are implementable with immediate effect, accurately monitorable and their degree of application can be regulated within a wide range, thus denoted as adaptive solutions. This research work proposes a discrete-event simulation based real-time resource management technique for paper and pulp industry, wherein some resource management challenges are addressed and solved strategically with adaptive solutions. The presented examples validate that using the developed approach, the effective adaptive strategy can be timely planned to resolve occasionally or permanently happening resource management challenges ensuring an un-interrupted material flow throughout the plant along with the minimization of productivity and energy losses. [ABSTRACT FROM AUTHOR]

It is critical for manufacturers to identify quality issues in production and prevent defective products being delivered to customers. We investigate the use of deep neural networks to perform automatic quality inspections based on image processing to eliminate the current manual inspection. A deep neural network was implemented in a real-world industrial case study, and its ability to detect quality problems was evaluated and analyzed. The results show that the network has an accuracy of 94.5%, which is considered good in comparison to the 70–80% accuracy of a trained human inspector. [ABSTRACT FROM AUTHOR]

Small companies encounter difficulties to adapt to new markets with a sustainable perspective. The subject of this article is a paper transformation company which has been eager to develop the organic farming market by developing a new plant support. Alternatives made from plant fibres or plastics exist, such as jute and polypropylene. The main difficulty of this project was to ecodesign a paper product with a multicriteria approach. The factors considered have been divided into two categories. Firstly, the environmental impacts calculated according to the Life Cycle Assessment methodology (on raw material extraction, manufacturing and end-of-life) are summarised. Secondly, the characteristics of the product are discussed in regards to these results and sustainability questions as part of the interpretation phase. Finally, ways of improvement of the LCA methodology to improve the study in terms of geographical representativeness and societal considerations are discussed. [ABSTRACT FROM AUTHOR]

Life Cycle Assessment (LCA) has been used to evaluate the life-cycle Greenhouse Gas (GHG) emissions of pulp and paper production, and most previous studies rely on process-based models for specific product types (e.g., printing paper), industry-average data, or information from a few mills. In this work, a data-mining approach is used to quantify GHG emissions intensities of different paper products manufactured by the U.S. mills. Facility-level emission data collected from publically available governmental databases and mill-level production data collected from the private sector were integrated to track the GHG emissions for different product lines and paper products in mills (in total, 165 mills were matched and analyzed). The results highlight the ranges of GHG emissions intensities by different product groups and categories, and can be used as a transparent data source for LCA practitioners, policymakers, and the pulp and paper industry to perform further analysis on carbon accounting and strategic planning for GHG mitigation. [ABSTRACT FROM AUTHOR]

Abstract To improve energy efficiency of a Paper & Pulp industry, full capacity utilization of the processing equipment and diverse energy source utilities are promoted, while same time processing of diverse raw materials, meeting of strict processing time requirements, seamless material flow and obtaining required product quality are important concerns to be fulfilled. This is similar to a resources management problem while energy is considered as one of the measurable resources, wherein estimation of energy consumptions is crucial/complicated aspect. Discretized form of energy consumption (approximate energy consumed by unit amount of intermediate products at different intermediate processing stages) concept is utilized here with discrete event simulation for consumed energy estimation/analysis. The research deals with a real time working Paper & Pulp industry resource management problem, wherein overall energy consumption rate and production rate were analyzed for some commonly occurring situation of efficiency reduction of the processing station(s). Energy efficient solutions are evaluated with varying production flow (varying the percentage of raw material) and resources management options (turning on/off paper machines). Energy efficient solution which ensures minimum loss in production rate and minimizes disturbances in material flow is suggested for implementation. [ABSTRACT FROM AUTHOR]

The power of the rotating transverse paper cutting machine is huge because of unreasonable motion. The paper researched the motion mechanism of the structure, transformed the 3D trajectory of rotating blade and paper cutting marks into the same plane space. Ultimately the motion mechanism of the parameters of cutter and paper cutting are established and the reasonable cutting conditions are founded. At last the parameters are optimized according to the relationship of the planning and useless power consumption, achieve the goal of the roller rotate smoothly and the useless power consumption is zero in theory. [ABSTRACT FROM AUTHOR]

Reducing “energy waste” has helped manufacturing companies to systematically improve their productivity. However, optimization goals related to the resource energy (energy efficiency, demand side management, integration of renewables) are still addressed in an isolated manner. Here we present a stepwise methodology on how to unite the different facets of energy usage in a factory planning process. Our results indicate that addressing the energy related planning goals in an integrated manner helps to reduce both life cycle costs and the environmental impact of factories. Future research will refine the presented planning framework and quantify the correlation between conventional and energy related planning targets. [ABSTRACT FROM AUTHOR]

The functional performance of mechanical components depends on their surface characteristics, surface integrity and geometric accuracy. It is critical to understand the influence of these surface properties on product performance, so the manufacturing processes can be designed to deliver the required performance. This paper presents the influence of surface characteristics on functional performance including surface topography, surface 2D/3D parameters, tribological parameters and residual stresses. It then discusses the surface properties created by various finishing methods such as hard turning, grinding, superfinishing, mass finishing, etc. Finally, it provides guidelines for the design and selection of finishing processes in order to yield the critical product performance characteristics. [ABSTRACT FROM AUTHOR]

In this paper a new process to manufacture unidirectional reinforcements for eco-composite materials, made of natural fibers, is presented. Starting with flax rovings of different sizes, an apparatus was developed to feed and align the rovings over the wet-end section of a paper machine. The short kraft paper fibers are therefore mixed with the long flax roving as the machine is running, and at the end of the process, a sheet of the hybrid dry reinforcement is obtained and cut to size for impregnation with various resins, using different processes. This novel manufacturing process allows for high volume production of reinforcement. It is very flexible, and many different combinations of long and short fibers can be exploited for the production of a vast variety of dry reinforcements. In this paper, composite samples are obtained out of these reinforcements, using the resin infusion (RI) molding process with a commercial epoxy resin. The results are compared with those of usual glass fiber reinforcement. An interesting aspect is that the large variability, typical for natural fibers, is largely reduced when the short kraft fibers are present in the composite. In terms of permeability to resin, reasonably comparable values can be obtained compared to that of glass fabrics, if a low surface density of reinforcement is chosen. [ABSTRACT FROM AUTHOR]

Since the machining of complex aerospace components, like integral compressor-rotors (blade integrated disks), is very cost-intensive, optimizing the process by means of process simulations is an active field of research. With the rise of Quantum Computing, a new instrument with high optimization potential is moving into focus. In this paper, a possible application of Quantum Computing for the machining simulation of multiaxis milling of thin-walled aerospace components is discussed. For this reason, a simulation framework used for the milling simulation is analyzed and each component is evaluated separately in relation to Quantum Computing. Parts of the Harrow, Hassidim, and Lloyd algorithm are proposed to enhance the Finite-Element simulation-based component, like the modal analysis for dynamics simulation. This algorithm can solve linear system problems with exponential speed-up over the classical method. The paper presents a roadmap on how the classical steps of a modal analysis for dynamics simulation could be replaced by quantum algorithms based on quantum phase estimation. The implementation of the first working steps is presented to validate this approach. The linear system problem, arising from the dynamics simulation, is analyzed in detail and a minimal value problem of linear coupled oscillators is derived. [ABSTRACT FROM AUTHOR]

The manufacturing of complex cable systems e.g. for vehicle integration or the direct wiring inside control cabinets is a laborious, manual, and error-prone process. For cost optimization reasons, cable pre-assembly is carried out in the best-cost countries. Long supply chains and a lack of resilience in the cable industry have recently caused the re-location of the final assembly of mechatronic products to high-automation countries. This paper presents a robot-guided tool that enables automated feeding, insertion, and routing of confectioned cables. We developed a gripping device to handle cables as damage-sensitive handling objects. The cable is pneumatically gripped by a flexible surface and clamped to absorb the forces during the insertion process. In contrast to state-of-the-art grippers which are based on rigid gripping jaws, we show that a force-locked gripping mechanism can be realized without damaging and imprinting the cable insulation. In this paper, both the design of the gripping tool and the insertion process flow are presented. We tested the tool with a Fanuc CR15-iA robot in the laboratory environment. Our experimental results proved the technical feasibility and led to the deduction of potential improvements for further research. [ABSTRACT FROM AUTHOR]

Industry 4.0 is still the 'next big thing' for many companies, and they are currently trying to adopt Industry 4.0 in their strategy. While Industry 4.0 continues to evolve, understanding of its driving factors remains crucial for industrial development and economic growth for competitive advantage. Those driving factors are revealed not only by exploiting advanced technology such as automation, robotization, Internet of Things, AI, smart system, or big data analytics but also the human factors, including culture or willingness to change. This paper aims to analyze the essential driving factors for Industry 4.0 maturity models. All factors are identified from a structured literature review. Then, the weights expressing the importance of the factors are calculated by obtaining expert opinions. This study utilizes a linguistic fuzzy Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) and using Decision Making Trial and Evaluation Laboratory (DEMATEL) method for weighting and ranking the driving factors and mapping the relationship between them. The analyzed driving factors identified from 52 references containing maturity models for Industry 4.0 are categorized into six domains and 16 subdomains. The presented study findings in this paper can support academicians and practitioners in comparing, developing, and improving Industry 4.0 maturity models as part of their adoption strategy and development. [ABSTRACT FROM AUTHOR]

Obsolescence and shortages affect almost all value chains. The electronics industry, in particular the manufacturers of electronic and IT devices, are well aware of them. They have been organizing themselves since the 1980s. However, repeated crises are generating unprecedented waiting and stock-out phenomena. SNCF Réseau sets up and maintains signaling installations for railway tracks. The equipment used is long-lived, ageing and exposed to environmental aggression. Customers (i.e. freight or passenger transporters) must be offered a very high level of operational availability of track. However, in-service support becomes extremely challenging as soon as the equipment contains obsolete or obsolescent components, leading to possible downtime. To improve the efficiency of obsolescence and shortages management, SNCF Réseau wishes to reinforce its internal processes. A partnership through an empirical research project was then carried out in order to (1) Discover the main causes of obsolescence cases and shortages allowing to develop proactive management processes, (2) Find out the duration of the commercial life of the main target components, and (3) Find out the time between the announcement of the manufacturing discontinuance and the date of the last buy order; i.e. LBO for some classes of electronic components. The paper presents the results of this research made for 3 global suppliers. The project group, made up of ISAE-Supméca engineering students and their industrial and academic tutors, studied about 3000 components. The study shows, among other issues, that: (i) obsolescence and shortage information is often incompletely explicit, (ii) obsolescence cases are due to a small number of main causes, (iii) data processing does not always enable statistical models to be obtained, and (iv) customers have to use huge efforts to find out the right data about the obsolescence cases. The proactive management should be therefore agile to deal with these uncertainties. The paper ends by exposing some conclusions and describing the future research works the authors follow. [ABSTRACT FROM AUTHOR]

This paper studies the challenges encountered during the implementation of machine learning (ML) applications in production planning and control (PPC) functions of manufacturing companies. The application of ML in PPC promises numerous benefits, and several studies have revealed the positive impact of such an implementation on key performance indicators of production systems, such as lead time, due date reliability, and inventory levels. However, despite the theoretical potential and the increased research interest in the field over the last five years, practical applications remain rare and discourse on the existence of this gap between research and practice remains relatively sparse. In this paper, we identify general ML implementation barriers in the manufacturing domain based on a structured literature review and evaluate the relevance of these barriers within the PPC domain by interviewing ML and PPC experts. This research is beneficial for practitioners and researchers who aim to implement ML applications in PPC by enabling them to evaluate the possible barriers and challenges during implementation projects within their organizations. [ABSTRACT FROM AUTHOR]

Problems or challenges at operational/tactical levels in configure-to-order production environments are more often than not caused by inadequate information provisioning. Additionally, incomplete, uncertain, and ambiguous information hampers adequate decision making and control. This is especially the case in configure-to-order environments, where there is a high variability in processes. Consequently, standard software solutions often fail to allow for the required flexibility, adaptability, and agility. Moreover, in industry, software solutions (be it PLM, ERP, IoT, ..., MES or CAQ solutions), oftentimes tend to become dominant factors in primary processes, thus further stifling the anticipated flexibility. If, however, these software solutions – here conjointly referred to as Digital Infrastructure – are regarded as subcontractors in a digital twinning approach, the primary targets in production/assembly can remain the driver in the overall process. This paper depicts how this digital twinning approach can be used to establish versatile and adaptive information provisioning. To ensure that the approach is not merely a theoretical discourse, the approach is depicted based on a case study. This case study, embedded in a Dutch manufacturing company for special needs bicycles, is rooted in e.g., the seasonal fluctuation in the market, on the broad product portfolio, but foremost on the ability to strengthen customisation as the unique selling point in that portfolio. For this reason, a use case focuses especially on one of the most demanding parts of the production/assembly-line: the welding robot digital twin for frame welding. On the one hand, this twin encounters all product variants and exceptions; moreover, this twinning approach will aim to provide decision support for upscaling of the production facilities. Next to this specific application, the paper also addresses the middleware solution that is under development to integrate individual applications in the bigger company context and strategy. [ABSTRACT FROM AUTHOR]

Learning factories that focus solely on education may benefit from replicating software systems that drive processes, activities, and workflows in industrial environments. However, such systems (e.g., PLM, ERP or MES) will not meet the requirements if the learning factory intends to be an environment where education and research merge. The flexibility, volatility, ambiguity and incertitudes that characterise the integrated learning-research environment need to be addressed with an approach that replicates industrial reality, but that also accommodates and stimulates the versatility of the learning factory. This paper depicts how the digital twinning approach integrates the physical units of a learning factory and the software systems, but also data acquisition, simulation, and educational/didactic approaches to production/assembly processes and production optimalisation. The approach thus also includes, for example, IoT, planning, monitoring, diagnosis and (quality) control. In addition, the digital twinning approach is used to combine the current state of the learning factory and its activities with designed (to-be) and potential (could-be) representations of the environment in order to stimulate the evolution/improvement of both research and education and their combination. For this purpose, digital twinning is combined with the concept of daydreaming. The paper illustrates the approach based on an ongoing development trajectory of a new learning factory, in setting it up as an environment for education and simultaneously as a testbed for research. It discusses how the development process relies on the digital twinning approach and how, when the learning factory is commissioned, this digital twinning approach will increasingly integrate the use of and activities in the learning factory into the development/evolution cycle of that learning factory. [ABSTRACT FROM AUTHOR]

After the introduction of the term 'biointelligence' in the context of production science about five years ago, different understandings have evolved that impede a comprehensive implementation of the concept. As a result of the dissent about the definition and its theoretical position in science, neither a congruent method of scientific knowledge retrieval exists nor a collection of methodological foundations for the development and management of biointelligent systems. This paper thus reviews how the conceptual understanding of the term has evolved and how perspectives on the topic have changed in recent years. It adopts a two-track research approach consisting of a systematic literature review on recent biointel-ligence publications as well as a survey with 69 participants in established biointelligence networks. The paper is intended to provide an impetus for definition efforts in different scientific disciplines and standardization initiatives in order to ensure a smooth transfer to industries. [ABSTRACT FROM AUTHOR]

Current trends such as mass individualization, matrix production, cloud manufacturing and production as a service can be addressed by implementing adaptive manufacturing process chains. Adaptive manufacturing process chains contain alternative manufacturing options at several levels (e.g. feature-based geometry sequence, manufacturing methods, production machines, clamping systems, fixture positions, tools or processing strategies) to be able to continuously adapt to the real time conditions of a manufacturing environment in the best possible way under given target variables. Currently, the design of process chains is often manual and based on implicit empirical knowledge, which on the one-side leads to non-optimal process chains and on the other side does not include alternative manufacturing possibilities. The required effort for implementing adaptive process chains is thus opposed to high costs and complexity in work planning. In this paper, a literature review is conducted to identify and evaluate approaches in the scientific literature for the generation of work plans of adaptive process chains in one-off manufacturing. The literature review is divided into three phases and is broken down into six steps. The preparation phase defines the scope of the research. In the implementation phase, approaches are first identified utilizing a keyword analysis based on the theoretical and practical principles of work planning and selected in a 2-step selection process. The evaluation of the relevant approaches by using defined evaluation criteria enables the identification of research deficits for the derivation of further research questions. The literature review in this paper concludes that no approach addresses all the different aspects of work planning comprehensively. In particular, the interactions of individual decisions between the planning dimensions are insufficiently considered. [ABSTRACT FROM AUTHOR]

Engineer-to-order (ETO) supply chains are configured to deliver unique and complex products to serve specific market demands. If market requirements change, ETO supply chains must adapt to survive and thrive. This paper examines the measures ETO companies should consider to ensure that their supply chains meet the demands of changing business environments. In particular, the paper focuses on how the yard-intralogistics processes of a large supplier are affected by the current transition toward sustainable energy solutions within the oil and gas industry. A comparison between current intralogistics processes and the requirements of emerging product portfolios and project setups reveals the need for process redesign and supply-chain realignment. Current yard-intralogistics processes, which are designed for large-scale projects, lack the efficiency required by the smaller-scale, renewable projects that will be necessary in the future. Our investigation shows that intralogistics processes must be significantly changed and that such a shift will not be possible without addressing operational and organizational structures concurrently. We argue that there are critical interdependencies between operational and organizational measures. The ability of companies to manage this interdependency is crucial for ETO-based industry transitions. A framework for the transformation of ETO companies in the context of industry transitions is presented, highlighting the operational and organizational challenges involved. This framework serves as a guideline for practice and as a reference model for further research. [ABSTRACT FROM AUTHOR]

Circular economy (CE) focuses on maintaining the value of goods and materials as long as possible, reducing waste and resource usage, and keeping resources within the economy when a product has reached the end of its life. Products and materials have to be utilized many times to produce additional value. In the automotive industry, CE involves processes throughout the value chain comprising multiple dimensions such as energy, materials, lifetime, and utilization. The CE performance of the automotive industry can be measured using key performance indicators (KPIs) from those dimensions. However, since multiple stakeholders are involved throughout the automotive product lifecycle and value chain, calculating KPIs requires data from heterogeneous sources. Thus, a non-uniform understanding of the KPIs among those stakeholders may arise due to a lack of explicit semantic description, including the related assessed CE scenarios, data providers, and data sources. Meanwhile, various sectors have used ontologies to facilitate a common understanding of information structure among systems and organizations. Our paper presents an ontology-based model that enables sharing a common understanding of KPIs used to assess CE performance in the automotive industry. We identify the indicators, the corresponding data requirements, and sources. In this paper, we present the ontology model describing the semantics of data required for the indicators. We also show the deployment model illustrating the implementation of the ontology model in the CE performance assessment phases. [ABSTRACT FROM AUTHOR]

Manufacturing industry needs major transformation to meet disruptive environmental, social, and economic challenges, thus requiring a highly skilled workforce. This paper presents key functionalities, results, and best-practices for the launch and operation of a national upskilling platform. The Swedish upskilling programme Ingenjör4.0's operations have been constantly user-monitored through participant surveys measuring appreciation for training content from the participants and identifying areas with potential for improvement. Thematic analysis of 137 survey responses identified dimensions relevant for an upskilling programme's success. Results show that success factors and hurdles typically lie within the following dimensions: relevance, organization and structure, working life competencies, support from teachers, and collaboration with other learners. The paper concludes that national programmes like Ingenjör4.0 can, in a short time, have deep impact on skill levels for manufacturing industries in areas such as industrial digitalization. Highlighted success factors are: participant appreciation of highly relevant content, collaboration with other participants, highly competent teachers, and the collaboration between universities. Obstacles for the learners are feelings of mismatch in challenges and prior knowledge, lack of feedback and applicable working life examples in the teaching, and the need for increased collaboration with other participants. [ABSTRACT FROM AUTHOR]

With the rising concept of Industry 5.0, the worker's needs and well-being are placed at the center of the production process. This new human-centered approach will let the industrial sector reach many social goals besides employment and growth. Working in an unhealthy workplace can lead to stress, frustration, and worry. Moreover, the operator's well-being and performance could be affected and compromised by high mental demand due to the increase in production complexity. As a result, work-related stress has grown to be one of the major health issues, and new tools and methods for stress detection need to be researched and developed. However, the detection and assessment of mental stress in real settings is still an open challenge. Within working contexts, the most widespread assessment methods are subjective evaluations based on self-rating questionnaires whereas objective evaluations or multi-modal approaches are mainly used in controlled environments. This paper proposes a systematic review of the research works related to stress detection on-field in working environments. Although there are many research papers on stress detection in controlled laboratory conditions, the number of studies examining this topic in a working context is very limited. A descriptive and detailed analysis provides an extensive overview of the current state of the art. Finally, the paper addresses open issues, challenges, and future research directions for stress detection in working environments. [ABSTRACT FROM AUTHOR]

Current disrupting trends in manufacturing like mobile robot-based intralogistics and digital twins require precise indoor positioning. 5G-based positioning has the potential to be more cost-efficient than other positioning systems due to shared hardware for positioning and communication. But first it has to achieve high positioning accuracy and stability. No positioning technique has yet been established as standard in the industry in combination with 5G because traditional positioning approaches can deliver unstable results when multi-path fading, reflections, or missing line-of-sight effects occur. Identifying promising techniques is still an open research topic. It is established in the literature that deep learning-based approaches can mitigate these effects better than traditional ones by learning unique fingerprints that correspond to precise positions. However, fully-connected neural networks tend to overtrain on these fingerprints which again leads to unstable positioning due to a lack of generalization. This can be explained by the inability of fully connected neural networks to learn structural interrelationships in high dimensional data. This is because their input data is flattened, which means the structure is removed. Convolutional neural networks are being widely used with image data and can learn structural interrelationships. This paper presents a technique that uses convolutional neural networks in combination with a channel-smoothing approach to position mobile robots in the research factory ARENA 2036. The results presented in this paper indicate an indoor positioning accuracy similar to other industrial positioning systems, with improved stability compared to a fully-connected approach. [ABSTRACT FROM AUTHOR]

Value co-creation has become a widely used concept in various domains due to its broad applicability. However, the lack of a consensus among researchers regarding the underlying concepts and related processes has resulted in disparate definitions, leading to misunderstandings both within and across disciplines. Moreover, the abundance of literature on value co-creation makes it challenging to identify relevant papers and derive future research directions. Therefore, it is difficult to find the most relevant papers and to derive future research directions in each case. To address these challenges, this paper elaborates a bibliometric analysis of value co-creation, which offers the possibility to analyse the multitude of contributions on the topic. Through the use of different techniques of performance analysis and science mapping, we outline the different domains in which value co-creation is studied, identify connections between different streams, highlight overarching concepts and find emerging trends. This provides a foundation for future researchers in disciplines such as management, business, social sciences, information systems as well as engineering, who are interested in contributing to the value co-creation research. [ABSTRACT FROM AUTHOR]

Obsolescence and shortages have a strong impact on all value chains today. They can be found in all areas. They have an impact on both consumers and manufacturers. They can affect all components, products, systems, etc. Facing these two phenomena, ABMI, a multi-specialty engineering company, proposes despecialization as a solution. It allows components to be remanufactured and upgraded without the original supplier's specifications in case of obsolescence or shortage. This despecialization generally makes the system more resilient and robust. However, when despecialization is carried out to address obsolescence and shortages, different sets of consequences may arise. The improvements made to one component may, for example, degrade the performance of certain functions, or they may change the behavior of other components, or even change the properties of the system, i.e. reliability, maintainability, and availability. All these changes are the result of the propagation of the consequences of obsolescence and shortages. It is therefore essential to study the dependencies, especially between components, in any despecialization action to ensure the proper functioning of the system and its sustainability over time. In this paper, certain dependencies are studied in a case study of a mechanical system. We use Bayesian network theory to explore the modeled dependencies. The objective is to show the propagation of the effects of architectural changes on the resilience of the redesigned or despecialized product. The paper concluded with conclusions and perspectives on future research undertaken. [ABSTRACT FROM AUTHOR]

The efficient and safe movement of goods and people require reliable railway systems. Quality assurance of manufactured and assembled systems and correct maintenance of such systems are required to keep rolling stock in good operational condition. Quality assurance and maintenance in the railway industry can be costly and time-consuming, but the expansive growth of data due to smart sensors and monitoring technologies makes it possible to leverage the potential of machine learning to reduce cost and labour. Improved reliability and safety, and reduced costs are benefits that the use of "Big Data" and machine learning techniques can realise. However, despite these potential benefits for manufacturers, rail operators, and passengers, the rail industry is still labelled for its lack of innovation, while in most other industries, data is regarded as a strategic asset for competitive advantage. This paper demonstrates how machine learning and data analysis can be used to benefit railway industry manufacturers and operators when applied to rolling stock data. It also illustrates the lost opportunity in the rail industry for not applying data-driven solutions to their full potential. The paper also discusses the current applications of machine learning in the railway industry and provides the requirements for the implementation of machine learning techniques. Machine learning is applied to pantograph data of a South African railway operator's rolling stock. Classification – a machine learning technique – is used to identify and categorise events within the dataset to discover whether pantograph bounce occurs due to faulty sensors, faulty pantographs, or defective infrastructure. In this paper it is demonstrated how machine learning can benefit rail manufacturers and operators to improve manufacturing and assembly processes, as well as maintenance practices. It is concluded that railways should treat data similarly to other railway assets, with suitable management and governance practices. [ABSTRACT FROM AUTHOR]

Wire harnesses are vital for any modern automotive vehicle. They control the basic functions in a vehicle, for example, windshield wipers and critical functions such as sensors, cameras, and autopilot functions. Thus, the quality of wire harness assembly is highly important. Today, wire harnesses are usually assembled manually, which creates unergonomic and tedious working conditions for operators. Traditional and collaborative industrial robots have been identified as possible solutions to overcome challenges faced by operators in this type of assembly. The international research community has proposed many solutions for automating the assembly of wire harnesses in automotive vehicles but despite these solutions, the industry has not been able to adopt a method to automate this assembly process fully or partially. This paper presents a review of findings on robot-assisted wire harness assembly processes based on a systematic literature review. Specifically, the assembly of wire harnesses in Electric Vehicles (EVs). The state-of-the-art review focuses on solutions to improve unergonomic work situations and ensure the quality of assembly operations. Best practices and reasons for the lack of extensive implementation in automotive final assembly systems are described. Further, the paper presents suggestions based on success stories where the automation of the wire harness assembly in automotive vehicles has been realised by leveraging human-centred automation solutions. Based on the findings, this paper identifies the research for future study. The findings also indicate that there is already technology that can support the automation of wire harness assembly processes in EVs but it is crucial to identify the human aspects and the role of humans in the assembly of wire harness assembly process. [ABSTRACT FROM AUTHOR]

With the increasing demand for individualized production and swift adaptability to changing market needs, new manufacturing system concepts emerge. Aiming for high flexibility and scalability, they rely on universal reconfigurable machines that can be used in various production steps by automatically changing tools and sensors. Consequently, the data processing infrastructure is required to support the frequent exchange of process-specific software applications, ranging from control and monitoring tasks to complex sensor data processing. This can be a major challenge when computation-intensive software is supported by hardware acceleration to achieve the desired performance or latency requirements. In this paper, we present an edge unit architecture that allows for hosting such applications while meeting the needs for modularity and reconfigurability. It builds upon a hypervisor to partition fixed processing resources among the applications. We present a mechanism that extends this partitioning to field-programmable gate arrays (FPGA) by using dynamic partial reconfiguration. This provides the option to deploy application-specific hardware accelerators with computation-intensive applications while maintaining modularity and on-line reconfigurability. We conclude the paper by pointing out the potential of the proposed architecture based on a use case for automated disassembly in a reconfigurable robotic cell. [ABSTRACT FROM AUTHOR]

Cyber-physical systems (CPS) offer immense optimization potential for manufacturing processes through the availability of multivariate time series data of actors and sensors. Based on automated analysis software, the deployment of adaptive and responsive measures is possible for time series data. Due to the complex and dynamic nature of modern manufacturing, analysis and modeling often cannot be entirely automated. Even machine- or deep learning approaches often depend on a priori expert knowledge and labelling. In this paper, an information-based data preprocessing approach is proposed. By applying statistical methods including variance and correlation analysis, an approximation of the sampling rate in event-based systems and the utilization of spectral analysis, knowledge about the underlying manufacturing processes can be gained prior to modeling. The paper presents, how statistical analysis enables the pruning of a dataset's least important features and how the sampling rate approximation approach sets the base for further data analysis and modeling. The data's underlying periodicity, originating from the cyclic nature of an automated manufacturing process, will be detected by utilizing the fast Fourier transform. This information-based preprocessing method will then be validated for process time series data of cyber-physical systems' programmable logic controllers (PLC). [ABSTRACT FROM AUTHOR]

Production control is an essential component of a production system used to monitor and manage production orders. Centrally controlled production systems rely on basic data management, such as storing the bill of materials and required work schedules. Components of customer orders are added to generate the corresponding production orders. However, this type of production control can pose serious problems in case of unforeseen events such as machine failure or changing customer requirements during production. In such scenarios, costly and time-consuming re-scheduling becomes necessary, which endangers the company's competitive position and leads to data retention issues. This paper introduces a new method of production control based on the concept of smart orders. Smart orders use smart contracts for autonomous routing based on programmed information in the source code, enabling self-control through the production system. Smart contracts are transaction programs that work with 'if-then logic", allowing for partially flexible process schedules and reducing the need for human intervention. The smart order uses the functionalities of blockchain technology, such as security, transparency, and immutability, to ensure the integrity of production data. The paper concludes by presenting the expected value propositions of smart order-based production control on the production system. Depending on the production control system used, companies can integrate the results presented in this paper into their own blockchain implementation strategy. [ABSTRACT FROM AUTHOR]

The sustainable operation of grinding processes depends on various parameters. Not only does the grinding process have an enormous influence on the quality produced and the use of resources, but the peripheral systems have to be considered as well. When networked into a complete system, a collection of parameters, state variables, performance criteria and external factors is created. However, without the appropriate framework and the targeted definition of these values, no directly producible reference to a sustainability-oriented operation of the grinding system can be established. To detect the correlations and dependencies of the variables, Industry 4.0 approaches can be used to digitalize the manufacturing system. Cyber physical production systems (CPPS), when specifically designed for sustainable operation of the manufacturing system, contribute a positive impact in terms of e.g. energy and resource efficiency. In principle, CPPS initially carry an environmental backpack due to the sensor technology and hardware to be integrated, which is balanced out over the application period of the CPPS and finally results in a positive impact. Nevertheless, it has already been shown that CPPS can be made predictable in their efficiency increase through their systematic development. These advantages of a systematic development can greatly simplify the use of CPPS, especially in grinding. The inaccuracies and non-determinable factors of a grinding process are minimized in this paper using the CPPS and a systematic methodical approach. Similarly, the targeted use of sensor technology achieves the maximum benefit in terms of quality and sustainable operation. The use case in this paper is related to a grinding system in which sensory components are integrated and thus a sustainability-oriented measurement and control concept is built up. Through the integration, clear potentials are recognizable, which can be related not only to the parameters and the product quality but also to the resource consumption. [ABSTRACT FROM AUTHOR]

This paper introduces a simulation-based planning method that metaheuristically optimizes production planning and the control of components of a flexible energy system for energy intensive production. Significantly increasing energy efficiency and energy flexibility are key requirements and success factors, especially for energy-intensive manufacturers, amid rising energy prices, volatility, and the overall need to reduce the energy and pollution footprint. The considerable potential for an optimized integrated planning and control of production and energy system components remains unexhausted, due to the underlying planning complexity, combined with a lack of available planning methods. This paper aims to introduce a novel planning approach that provides an automated integrated planning functionality to improve energy efficiency and flexibility alongside production goals. The method considers fluctuating short-term energy markets as well as local hydrogen-based energy storage components. Both method development and evaluation are based on a case study in energy-intensive steel production. The achieved results show a significant benefit from the developed dynamic planning and optimization for different variables particularly in two different heat-treatment process technologies. [ABSTRACT FROM AUTHOR]

The impact of the covid pandemic and the chip crisis on industrial production are just a few examples that emphasize the complex and volatile environment production systems must cope with. Especially operational scheduling tasks intensely suffer from these influences due to increased complexity in decision-making as well as frequent rescheduling activities. At the same time, they highly affect production-related key performance indicators such as lead time or resource utilization. As several publications have already shown, applying innovative and data-based methods from the field of Reinforcement Learning (RL) to complex scheduling tasks provides great potential to handle the challenges arising from a complex and volatile environment. The building blocks of an RL approach strongly depend on a company's individual task specifications and optimization goals. Yet, a methodology that considers these specifications for the design of RL approaches in production scheduling has not been introduced, preventing the transfer from laboratory examples to wide-ranging industry applications. To address this research gap, this paper aims to provide a conceptual methodology to generate RL solutions depending on the scheduling tasks and objectives. The methodology proposed in this paper consists of four central modules that constitute the building blocks of an RL solution. The first module derives the action space for the RL approach based on the underlying scheduling tasks. The second module constructs the reward function of the RL approach based on a company's individual scheduling targets. The third module derives the state vector from the components of the reward function. The last module selects an appropriate optimization algorithm for the RL approach and merges the previous modules to learn an optimal scheduling policy in a simulation environment that can be applied to real-world problems. As a result, the application of RL-based scheduling enables production systems to meet current requirements and evolve into resilient and self-optimizing systems. [ABSTRACT FROM AUTHOR]

The biologicalisation of manufacturing (BoM) is becoming a major driver for innovation, rendering conventional production processes more sustainable and efficient. So far, most biological transformation approaches focus on improving single technologies or materials in the production chain irrespective of the adjacent processes. However, the potential for BoM might even be greater if a broader approach is taken. Similar to how biological systems achieve efficiency by utilizing self-sufficient, highly interconnected processes, linking multiple individual BoM-technologies in a factory environment could lead to previously unattained improvements. This paper examines the potential of such a holistic transformation using the example of a manufacturing process from the pulp and paper industry. A production process for bleached pulp based on the chemical kraft process was chosen and screened for its inherent environmental challenges. Existing bio-based optimization approaches were then collected and assigned to the production steps followed by an evaluation of possible synergies and interactions concerning their simultaneous implementation on a factory scale. The survey revealed similarities and interlocking functions for a wide variety of BoM-technologies that might help to overcome some of the current hurdles faced in their individual implementation. Based on the findings a vision is presented about i) how a factory scale BoM holds the potential to improve sustainability of kraft pulp production in the short term and ii) how it could play a role in enabling more flexible and circular processes in the long term. The presented ideas should thereby serve as an impulse for a general investigation of wider biologicalisation approaches and provide a starting point for the creation of systematic methodical procedures to investigate and evaluate biological transformation processes on a factory scale. [ABSTRACT FROM AUTHOR]

Due to the ever-shortening innovation cycles and the rapid change of customer demands, enterprises introduce new, innovative, and modern products at an increasingly faster frequency and desirably lower costs. The technological change of products and mass customization increase the product complexity. This development requires manufacturing enterprises to perform more frequent production ramp-ups. Many production ramp-up projects miss the time, cost, and quality targets, causing manufacturing enterprises to lose potential profit due to later market introduction. Although digital tools are widely applied during the serial production phase, their potential during production ramp-up is hardly investigated in the scientific literature. Moreover, no guidelines exist on how to use digital tools during the production ramp-up in various industries. To provide data-driven support for the production ramp-up phase, requirements for the functionality of digital tools need to be defined. Henceforth, this paper applies a requirements engineering (RE) approach to elicit the requirements for digital tools to support the production ramp-up phase based on a comprehensive literature review and semi-structured expert interviews. In total, 13 experts from seven different industries participated in the interviews. After introducing the necessary theoretical foundations and the current state of research in ramp-up management literature, the RE process is described in detail. The RE process is based on user stories – an agile development tool – and the ISO 29148, which outlines an international standard for RE processes. The result is a set of requirements for the functionality of digital tools during the production ramp-up phase. Finally, the paper presents an outlook on future research perspectives for applying the elicited requirements in practice. [ABSTRACT FROM AUTHOR]

Rising energy prices and regulatory requirements are forcing both large manufacturing companies and small and medium-sized enterprises to minimize costs and emissions associated with energy supply. To operate the considered systems more efficiently overall, numerous energy efficiency measures have already been successfully implemented in recent years. This is particularly the case for the cross-industry air conditioning systems that are the focus of this paper. However, due to the growing share of renewable energies and the resulting fluctuating electricity prices and CO2 emissions factors, energy flexibility measures are becoming increasingly relevant. In this context, flexibility measures allow industrial system operators to benefit from these fluctuations, primarily using energy storages and optimized operation strategies. However, the implementation of energy flexibility measures requires high data availability, a good understanding of the system, and flexibility in production planning. To be able to effectively use the existing energy flexibility potentials, air conditioning systems are examined, that contain widely used cross-section technologies. In this paper, a method to enable optimized energy-flexible operation of industrial air conditioning systems is presented. A common feature of manufacturing sites is a similar topology of the thermal supply infrastructure across industries. The complex overall system is divided into several subsystems, e.g., cooling supply system, ventilation system and the production hall. Production processes typically release waste heat into the indoor air of the production hall, which then must be cooled. By accounting for thermal and electrical output and optimizing the operation strategy using mathematical optimization, both operating costs and CO2 emissions associated with energy provision can be significantly reduced. The presented use case reveals that operational cost savings of up to 18 % and a reduction of CO2 emission associated with energy supply of up to 16 % can be realized by optimizing the cooling supply system on its own. [ABSTRACT FROM AUTHOR]

Kanban systems are still the most popular method for pull control of material replenishment. In the Industry 4.0 era, the so-called eKanban represents a digital version of the classic Kanban. Thereby, in some eKanban variants, capturing actual stock-level by digital sensors would help or helps already to reduce capturing time and human failures. Currently, such digital sensors are not really beneficial because they are wired and expensive to install. In this paper, we investigate the potential of battery-powered stock-level sensors for eKanban systems using a simulation study. Battery- powered stock-level sensors are easier to install and, therefore, more beneficial than wired sensors if the battery´s lifetime is long enough. Because of the limited energy, it makes sense to trigger such sensor systems dynamically depending on the actual and predicted stock levels and on order points for minimum stock. For example, the sensor systems usually sleep and capture the stock-level only in a fixed interval for example every hour. Besides fixed intervals, it can be useful to trigger such sensors dynamically by reducing the activations in time with no or few material take-outs. To identify the best trigger strategy, this paper investigates how artificial intelligence (AI) methods, in this case, reinforcement learning, can be deployed. Thereby, the used material flow simulation maps the application scenario of a four-stage assembly line and compares the AI-based trigger with a manually operated demand notification. In this way, it can be shown that battery lifetime is increased fourteen times more compared to the manually operated demand notification, and battery runtimes of several months can be achieved by AI-based optimization of the sensor energy consumption. This approach leads to an eKanban solution becoming more economical, and thus smaller companies can also consider digitizing their material flow control [ABSTRACT FROM AUTHOR]

The present paper studies the automated optimization of reconfigurable robot cells to support engineering planning activities. The specific type of reconfigurable robot cells under investigation allows an arbitrary arrangement of functional machine modules on a base plane accessible to the robotic manipulators. The research is motivated by the fast-growing configuration space of reconfigurable robot cells which results from the configurability of the cell layout and the performed workflows. Searching for optimized configurations within this configuration space is an iterative procedure which currently requires lots of manual efforts and expert assessment skills for each iteration. Performing many optimization iterations requires much time, which typically results in only a few performable iterations and thus in suboptimal configurations. To compensate for this, automated support for the underlying planning problem is required. The specific planning problem under investigation concerns finding motion-time optimized layout configurations for fixed workflows. This planning problem was chosen because of its strong association with the productivity of robot cells and the fact that it is the basis for further considerations on the influences of performed workflows. The main contribution of the research is a guideline concerning the decomposition of the mentioned optimization problem. This decomposition concerns the specific formulation of the optimization problem as well as its solving principle. The approach of the paper is to compare combinations of single- and multi-objective problem formulations as well as single- and multi-step solving procedures. Respective variants of evolutionary and conventional algorithms are tested for solving the optimization problems. The experiments are conducted using a specifically developed simulation and optimization framework which supports layout reconfiguration, robot path planning, and time parametrization based on state-of-the-art methods. [ABSTRACT FROM AUTHOR]

In recent years, multiple crises have shown the fragility of global supply chains. In particular, climate change and exploding energy prices shed light on the never-ending waste of resources caused by the linear economic model. In response, circular economy emerges as a promising alternative. For a systemic transition, companies must rethink and innovate their business models, current product life cycles, and underlying mindsets. Cross-sectoral collaboration in networks is necessary to redesign, reuse, recycle and remanufacture products and close the loop of material flows. In the field of software, this collaboration was reached by open source software (OSS). In the 1990s, OSS appeared as an emerging phenomenon overcoming existing limitations imposed by intellectual property laws. However, the concept of openness is not limited to software. First projects and research on the concepts of open source hardware (OSH), open innovation and open design show that openness can be a game-changer for the circular economy. This paper explores possible ways, prerequisites, and obstacles of open source concepts enabling circular value creation in the industry. We combine explorative data from a systematic literature review and more than 10 interviews with experts from open source and circular economy. We identify data exchange, trust, and accessibility as key aspects and show challenges within the necessary transition process. In an exploratory approach, we identify necessary lines of future research in order to facilitate open source movements toward a circular economy. The paper provides insights for academics and practitioners from production, supply chain, and management as it combines interdisciplinary perspectives to unlock the potential of open source concepts and collaboration for enabling circular economy. [ABSTRACT FROM AUTHOR]

Small and medium enterprises (SMEs) struggle with the uptake of industrial data in their business. SMEs have expressed interests to integrate physical legacy machines in factories and uptake Industrial Internet of Things (IIoT) for data-driven industrial processes. A widely recognizable aids for companies are testbeds or test-before-invest environments where companies can familiarize themselves with Industry 4.0 (I4.0) technologies. In this paper, an easily scalable approach to collect industrial data is presented. The environment can be used by SMEs to benchmark the developed solutions using affordable licenses and test aspects of the industrial data best suitable for their purposes before making investments. In TAMK FieldLab I4.0 pilot trial, horizontal integration is done in between 14.0 field and control layer to integrate different machines' data to common database and common format to be used for visualization and later for data analysis. The focus of the paper is on data connectivity capabilities. The trial utilize data from different machines. First phase includes exporting data from different machines to same data table with time stamps, to enable comparison of data with same time base. With common time base it's possible to produce synchronized timeseries data visualization from all TAMK FieldLab's machines. Used common database is PostgreSQL, where data from all machines is stored. For horizontal field level integration minimum amount APIs and protocols is used. For common API protocol MQTT was chosen as it's lightweight and supported widely. ABB robot data is taken trough OPC/UA and MQTT. From Omron mobile robot data is transferred trough ARCL. The paper will identify gap areas of the implementation in comparison with I4.0 and when appropriate, RAM 4.0 framework. Finally, the next steps that will be taken in near future regarding artificial intelligence (AI) and upgrading the IIoT system to match with RAMI 4.0 framework will be presented. [ABSTRACT FROM AUTHOR]

Cyber-physical production systems (CPPS) are highly complex and difficult to manage systems. To address these issues, sensors have to be integrated to collect data and monitor processes, and thus provide in-depth intelligence for decision-making processes. The integration of sensors is a major challenge due to the infrastructure required for connectivity and flexibility. However, in existing manufacturing systems, the infrastructure for comprehensive data acquisition does not exist. Expanding the cable infrastructure to a sufficient level leads to high individual deployment and maintenance efforts. Approaches to implement different wireless infrastructure for different use cases lead to a high heterogeneity of communication technologies. 5G meets the networking requirements for many industrial use cases. This enables the flexible, wireless realization of process-integrated sensors. However, currently no implemented 5G-based data acquisition and utilization for a machine tool exists. In this paper, a framework for 5G-enabled sensors is developed and implemented for a machine tool. An existing digital twin for a milling machine is adapted to implement data-driven analysis. An accelerometer is 5G-enabled to transfer data in real-time for evaluation and visualization. Moreover, a smartphone-based sensor with 5G capability collects data of the machining process. This paper outlines the components needed for 5G-enabled data acquisition and analysis and aims to elaborate the benefits, potentials, and transferability of 5G sensors for further use cases. [ABSTRACT FROM AUTHOR]

The development of new products using social technologies among defined crowds of individuals or groups, commonly known as Social Product Development (SPD), has become a growing trend in engineering design and manufacturing literature. This paper proposes an approach to studying the actors involved in SPD from a complexity science point of view. Specifically, we suggest that organizational efforts towards SPD must be concentrated on human and technological elements to achieve success in New Product Development (NPD). The paper explores the differences and similarities between the creation of new products and SPD and proposes a differentiated approach, based on complexity science for the different stages of NPD and SPD. Our findings show that SPD requires the integration of the complexity approach due to the combination of the knowledge, skills, and abilities of those involved during the development of new products. In addition, it is recommended that engineering design managers consider the importance of leadership styles, work structure, and organizational culture to achieve the expected outcomes of SPD. [ABSTRACT FROM AUTHOR]

As applications for optoelectronic products expand, the producers of laser modules face a challenge to reduce in-line waste as an attempt to improve their economic and sustainability performance. To achieve this goal, a feasible strategy is the recovery of value through the re-use of components and materials from non-conforming parts. This value retention strategy relies on efficient disassembly and re-manufacturing operations. As a matter of fact, the disassembly of optoelectronic products remains to be investigated. Typically, the properties of optical components irreversibly change during the assembly stage, particularly during bonding and fixation stages, hence the complexity of de- and re-manufacturing processes emerges. In this paper, the disassembly and requalification of laser modules are studied, and techniques to unlock defect-reuse potential are explored. The focus of this paper is to explore industry-ready techniques and apply them within the context of value retention practices to in-line defective products. The industrial case addresses two product families with increasing complexity in component functional inter-dependencies. The paper presents a method for assigning quality classes to failed products by analyzing the product structure and historical failure data, which in turn triggers the defect management strategies for product requalification and components reuse. The paper contributes to the formalization of cost analysis in pursuing the value retention strategies for different classes, while taking into consideration the costs of disassembly, rework, and disposal. [ABSTRACT FROM AUTHOR]

The manufacturing capabilities of additive manufacturing allow great design freedom for mass customization of different products. This new solution space needs to be explored and served by engineers when designing individual variants of a product. Therefore, the methods of model generation for the individual variant with individual customer specific requirements must be improved to take advantage of this design freedom. This paper discusses the specific challenges of designing a customized car seat by showing its general process chain and the challenges associated with the design of foam replacement structures that offer the possibility to customize the stiffness of the cushion. A possible framework for the underlying digital process chain is then discussed. This framework manages model synthesis according to anthropometric data and safety requirements as conflicting requirements within various complex engineering correlations. In a case study, the chosen Computational Design Synthesis (CDS) framework is applied to the problem of designing an individualized car seat. Detailed descriptions of the concept for each block in the process chain are presented within the case study. The paper and conclusion discuss whether the framework meets the challenges of the application example and further steps for the project. [ABSTRACT FROM AUTHOR]

The development of battery systems using pouch cells is a complex process due to the various system levels and domains to be considered as well as multiple design options such as cell to pack or cell to module designs. Among other things, a combined consideration of the electrical and thermal properties of the system is required in addition to the selection of suitable cells. Also, the packaging of the cells and modules in an existing installation space is a challenge that often represents a limiting factor in the performance of the battery system. Battery cells that can be produced in a flexible format offer potential for better use of existing installation spaces. However, these new degrees of freedom in the design of the battery system also represent an increased complexity and a larger solution space. To handle this complexity, new methods and tools are therefore required to support the developer. Therefore, this paper includes a literature review showing current approaches in battery system development and optimization with special focus on pouch cells. From this, the authors form the hypothesis that battery systems can be further optimized using pouch cells produced in a flexible format and according research questions. Finally, the paper describes a research methodology to answer these questions including the analysis of the state-of-art in battery system optimization, development of an optimization tool and case studies for evaluation of the approach. [ABSTRACT FROM AUTHOR]

Design engineers are required to deliver design solutions by a fixed deadline. Increasing demand for shortening lead time limits the exploratory and creative freedom of the designer. This paper proposes an automated design system that can substitute part of the repetitive and standardized tasks in the design process, increasing efficiency and shifting the design engineer's efforts to creative work. Computer Aided Welding Fixture Design is an engineering domain increasingly being pressed for shortening lead time. Welding Fixture Designs are multi-part structures that secure an assembly product during a welding process. The compact and complex assembly product to be fixed and welded presents a configuration and geometric challenge for the fixture regarding sequence, collisions, tolerances, welding path, human interaction, etc. This paper presents the welding fixture design problem in its general form. To describe the freedom of automation, the paper introduces the term decision space as the subset of a design space feasible by automation. Depending on the desired outcome, the decision space can be exploited through various strategies and automation approaches. This paper presents one of these approaches by implementing an evolutionary algorithm to the parametric fixture design problem of the use case. The results show reduced engineering efforts and the potential for expanding the exploratory spectrum of design proposals during conceptualization. [ABSTRACT FROM AUTHOR]

With the EU Green Deal and the UN Sustainable Developments Goals, the vision of a greenhouse gas-neutral and resource-efficient economy is already firmly anchored in world politics. In this context, the automotive industry faces many challenges including the increasing scarcity of natural resources, a rising demand in terms of sustainable vehicle design, production and materials sourcing. Due to all this, end-of-life decisions regarding dismantling have become increasingly important. A high proportion of secondary materials will be required in the vehicles of the future. To response to these challenges, companies have turned their focus towards the circular economy as a central approach to close material loops. In the German research project "Catena-X" a new data ecosystem with digital twins is one enabler that is being developed. The digital twins represent a promising approach to the circular economy by ensuring transparent, product-specific and end-to-end data exchange throughout the entire product lifecycle, from the material sourcing to the eventual dismantling and recycling. As one particular and unique solution, a decision framework that facilitates the best circular strategy at the end of a vehicle's life is developed and presented in this paper. The underlying data-driven decision support framework is based on circular economy KPIs. This includes material, components and specific vehicle KPIs to best identify the most suitable circular strategy. The framework was methodologically developed by an interdisciplinary team of partners, who are stakeholders throughout the value chain and participants in the Catena-X project. An integrated approach of user-centered design, an adapted version of the V-model and the Scaled Agile Framework were used for the methodology in the development of the solution. The paper presents the concept of a digital twin for a decision support system, that includes a central decision logic that also includes the relevant KPIs and a survey for evaluating the decision logic utilised with a chosen dismantling company. [ABSTRACT FROM AUTHOR]

In the context of ongoing digitalization, manufacturing companies face new challenges and need to expand their portfolios to include new digital services. This paper describes how new digital services are designed for a component manufacturer in the context of a research project. The central enabler for the new digital services is the digital twin based on the asset administration shell (AAS) that makes the necessary data available. Existing approaches start from a standardized modeling form in order to design AAS submodels based on it. This paper is about the specification of functionalities for services as a basis for further modeling and, thus, also for AAS submodels. This paper results in a logically arranged set of methods that displays this approach. The essential steps are definition of the services' basic functionalities, identification of the user groups for specific services, specification of user stories, and subsequent detailing and prioritization with the QFD method. The paper ends with an outlook on the ongoing research activities to define the application-specific AAS submodels. [ABSTRACT FROM AUTHOR]