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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]

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]

A common aerospace and defense industry challenge is low volume production of components for legacy aircraft due to compromised casting and forging supply chains. A hybrid manufacturing approach is presented to address this challenge that uses additive friction stir deposition, structured light scanning, and CNC milling. The paper describes a novel slicing and toolpath development strategy for additive friction stir deposition of a relevant aerospace geometry, post deposition measurement of the two-sided preform and identification of the machining work coordinate system, and five-axis CNC machining to obtain the final part geometry while ensuring stable machining behavior. [ABSTRACT FROM AUTHOR]

In comparison to conventional manufacturing technologies, additive manufacturing (AM) offers great design freedom, the integration of functions into components, new lightweight design concepts and high material efficiency. In aerospace and turbomachinery, this technology is increasingly coming into focus, especially the laser-based powder bed fusion of metals (PBF-LB/M) process. PBF-LB/M is already used for some aerospace components, which are often exposed to high thermal and mechanical loads. Dependent on the component geometry, support structures are required for AM, which then usually have to be removed by machining. One suitable support structure is the use of material with specifically induced porosity. This ensures good heat dissipation and thus homogeneous component properties, high retention forces and short process times in PBF-LB/M. However, the machinability of porous, additively manufactured material has hardly been researched so far. One preliminary investigation of milling porous, additively manufactured Inconel 718, though, showed significantly poorer machinability of the porous material compared to the dense material. To further examine this phenomenon, this paper presents the results of fundamental machinability studies with porous, additively manufactured Inconel 718 in orthogonal cutting. The investigations with tungsten carbide cutting tools on a special fundamental test rig include the analysis of the cutting force, the chip geometry, the chip temperature and the surface quality. The research results provide explanations for the poorer machinability of the porous material and derived approaches for improving the machinability in future studies. [ABSTRACT FROM AUTHOR]

Welding is a widespread process requiring substantial amount of energy and generating high carbon emissions, so calculating and reducing its carbon footprint is crucial to make it more sustainable. This paper compares welding methods (RLW, GMAW) regarding their carbon emissions and provides guidelines to identify Carbon Intensive Elements (C.I.E.). A mathematical model is employed for theoretical calculations and Life Cycle Assessment for analytical ones. Carbon emissions are normalized per welding parameters, showing that RLW is more carbon efficient than GMAW. Based on the approach used and produced results, a strategical framework developed as a roadmap to identify the most C.I.E. [ABSTRACT FROM AUTHOR]

The tool lifetime and the material removal rate significantly influence the production costs of a machined component. In order to maintain process reliability, tools are often changed too early in industrial practice, which is a waste of resources and results in increased tool costs and extended setup times. Tool condition monitoring systems based on machine learning have shown great potential to detect and predict tool wear, thereby reducing the risk of tool failure and optimising tool change intervals. Nevertheless, common supervised learning methods for tool wear detection require extensive data generation and high labeling effort. This paper presents two approaches with minimal to no labeling effort for predicting the end of the service life of drilling tools based on anomaly detection. Therefore, a classification approach to anomaly detection based on XGBoost and an unsupervised autoencoder architecture are evaluated on data acquired during machining experiments. In addition, the performance when applying the models of both approaches to other cutting parameters is investigated. Beyond a high classification performance on all datasets, the XGBoost approach can also be used as to derive an indicator for reaching the end of life, even when applied to other cutting parameters. For the autoencoder approach, it is shown that its reconstruction error correlates with the anomaly label and thus can also be used for anomaly detection. [ABSTRACT FROM AUTHOR]

To meet productivity goals, machining operations require high levels of efficiency to achieve the desired manufacturing quality in the available time. Additionally, machine tools can lead to safety-critical scenarios due to the high process dynamics and hazardous forces and torques. Therefore, workers need to exhibit special care while interacting with certain kinds of machines. Operational safety can be increased by eradicating the dependence on tangible control of machine tools. Current developments in Artificial Intelligence, especially in natural language processing, lead to increased utilization of speech-based interaction between humans and machines. However, these developments are mainly limited to areas of consumer electronics. Nevertheless, this type of human-machine interaction offers great potential for machining operations. Artificial neural network architectures like Transformer have rapidly emerged as the dominant architecture for natural language processing, overtaking alternative neural architectures such as Convolutional and Recurrent Neural Networks in performance for natural language understanding and natural language generation tasks. The architecture scales with training data and model size, enables efficient parallel training, and captures wide-ranging sequence features. These advancements can be used for enhancing the ease of use and safety of machine tools, which are often safety critical. In this article, a Voice-User-Interface based control of machine tools is conceptually outlined. Such a control can increase the efficiency of the workers, as it eradicates the need of hand usage. It further increases the safety of workers operating the machines and can detect anomalous sounds corresponding to machine failures during the machining process. The voice is first passed through a speech-to-text pipeline for converting the speech signals to relevant text, the text output is mapped to machine commands and passed to the physical machine. The implementation of the setup guarantees the speech-based control – even for safety-critical tasks -of machine tools. Moreover, this paper outlines the benefits and challenges of the developed VUI architecture. [ABSTRACT FROM AUTHOR]

This paper reports on the efficacy of six different image processing schemes to properly resolve cutting tool motion from video. We compare the edge detection and tracking scheme with intensity- and phase-based optical flow-based schemes and with digital image correlation. We also introduce the use of two new schemes: one based on particle image velocimetry (PIV), and another based on the use of convolution neural networks (CNN). Methods are illustrated with video of two tools. Results are benchmarked with twice integrated accelerations. We find that the edge detection and tracking scheme, the PIV scheme, and the CNN scheme are robust. [ABSTRACT FROM AUTHOR]

Serial-axis robots are being used in precision digital manufacturing for more intensive applications than previously expected. These industrial robots can show global inaccuracy and positional drift errors over time, which affects the quality of manufactured parts. Photogrammetry is the use of multiple cameras to measure objects in 3D environments, and in this study, camera calibration of a photogrammetry tool consisting of a stereo pair of infrared cameras is investigated. Accurate camera calibration is essential for precise measurement and monitoring of industrial robot positioning in 6 degrees of freedom (6DoF) by a photogrammetry tool. Possible areas of improvement in the camera calibration of the photogrammetric system include the calibration routine and the distortion model accuracy, which are explored in this paper. Variations of the calibration routine are tested and compared to the original routine. Routines with more images and even spacing through the node measurement volume are shown to have more repeatable parameter values. Wide-angle lenses are used to minimize setup volume; however, this results in large amounts of distortion towards the image edges. Improved distortion parameters could result in an increased useable image area. A further in-depth investigation is conducted into the accuracy of the distortion model within the camera calibration, which is found to overcorrect for distortion. Conclusions from these investigations can be used to improve camera calibration for the photogrammetric sensor network, ultimately improving the accuracy of the measurement system and the digital manufacturing platform. [ABSTRACT FROM AUTHOR]

In recent years, extensive research has been carried out on the influence of tool and process design on tool wear for bevel gear cutting. However, due to the complex interactions between tool, gear and process design, an analytical analysis of the cutting process has proven difficult. In this paper, the influence of the tool design on tool wear in continuous bevel gear manufacturing is analyzed using both simulations and cutting trials. Initially, a theoretical analysis is performed using a manufacturing simulation and a method is derived to calculate the effective tool angles. Subsequently, the results of cutting trials using the same tool designs are presented and compared to the simulation results. [ABSTRACT FROM AUTHOR]

In times of increasingly complex production processes and volatile customer demands, the production adaptability is crucial for a company's profitability and competitiveness. The ability to cope with rapidly changing customer requirements and unexpected internal and external events guarantees robust and efficient production processes, requiring a dedicated control concept at the shop floor level. Yet in today's practice, conventional control approaches remain in use, which may not keep up with the dynamic behaviour due to their scenario-specific and rigid properties. To address this challenge, deep learning methods were increasingly deployed due to their optimization and scalability properties. However, these approaches were often tested in specific operational applications and focused on technical performance indicators such as order tardiness or total throughput. In this paper, we propose a deep reinforcement learning based production control to optimize combined techno-financial performance measures. Based on pre-defined manufacturing modules that are supplied and operated by multiple agents, positive effects were observed in terms of increased revenue and reduced penalties due to lower throughput times and fewer delayed products. The combined modular and multi-staged approach as well as the distributed decision-making further leverage scalability and transferability to other scenarios. [ABSTRACT FROM AUTHOR]

Production system simulation is a powerful tool for optimizing the use of resources on both the planning and control level. However, creating and tuning such models manually is a tedious and error-prone task. Despite some approaches to automate this process, the state-of-the-art relies on the generation of models, by incorporating the knowledge of experts. Nevertheless, effectively creating and tuning such production simulations is, thus, a key driver for reducing costs, carbon footprint, and tardiness and therefore an essential factor in today´s production. Beneficial would be automated and flexible frameworks, since these are applicable to different use cases requiring less effort. Yet, in the age of Industry 4.0, data is ubiquitous and easily available and can serve as a basis for virtual models representing reality. Increasingly, these virtual models shall be interlinked with the current state of real-world systems to form so-called digital twins. As automated and flexible frameworks are missing, this paper proposes a novel approach where observed real system behavior is used and fed into a large-scale machine learning model trained on a plethora of possible parameter sets. The main target is to train this machine learning model to minimize the reality gap between the behavior of the simulated and real system by selecting corresponding simulation system parameters. By estimating those parameters an enhancement of the simulation will emerge. An interlink to real systems can be derived resulting in a digital shadow which is capable to forecast the future similarly to reality. The approach to overcoming the gap between reality and simulation (real2sim) is validated in simulations. [ABSTRACT FROM AUTHOR]

Disassembly is a crucial process for achieving circular products, enabling function recovery, material reuse, and recycling. Disassembly planning is complex due to epistemic uncertainty associated with each unique product's conditions, i.e., quality and aleatoric uncertainty about the capabilities of available resources and processes, and the cost benefits of associated operations impede planning. Therefore, the disassembly is intended to result in keeping the maximum value for the disassembled units of the product. In selective disassembly, the specification of the units of the product to be disassembled is acquired, leaving the rest of the product intact. The benefit of selective disassembly is to minimize waste during dismantling and maximize the reuse of the disassembled components for economic and ecological sustainability. The challenges in disassembly sequence planning include product complexity, operational and technological process capabilities, and the lack of information regarding the product architecture. For this complex planning task, limited studies have been performed on incorporating process capabilities with respect to the operations resources for selective disassembly planning. In this paper, an approach for optimal sequence planning of the selective disassembly process is put forward, taking into account multiple constraints, i.e., quality, time, and process capability. The intelligent planning approach takes advantage of a reinforcement learning model to handle the complexity of the planning problem. The approach has been implemented and tested on an industrial reference assembly. The result shows that the complex task of selective disassembly planning can be efficiently performed utilizing the proposed approach. [ABSTRACT FROM AUTHOR]

Hybrid modeled digital twins, using a combination of physics-based and data-driven models, offer great potential for manufacturing process improvement at different levels of the manufacturing system. Commonly digital twin approaches in academic research have a high level of model input availability. However, in industry this availability level is often not met. This leads to significant gaps for the model development and operation, that hinder the transfer of the benefits to actual manufacturing processes. To overcome these hurdles, this paper presents a conceptual framework for identifying, analyzing, and overcoming model input gaps. To exemplify the proposed approach, we present a polishing process use case based on the data of a hybrid model from previous work. [ABSTRACT FROM AUTHOR]

This paper presents a comprehensive literature review of the research and application of machine learning (ML) algorithms in recommender systems (RS). The study aims to identify recent trends, explore real-life applications, and guide researchers in positioning their research activities in this domain published in 2023 (Jan-June). The findings are categorized into different domains including education, healthcare, ML algorithms (auto-encoders and reinforcement learning), e-commerce, and digital journalism. The review highlights the enhanced recommendation accuracy, increased scalability, personalization and context awareness, diverse ML techniques, and strategies for handling cold start and data sparsity, and the foundation for future advancements in ML algorithms for RSs considering the application in manufacturing enterprises. [ABSTRACT FROM AUTHOR]

The technological advance present in Industry 4.0 (I4.0) has enabled the application of a wide range of technologies related to digitalization, connectivity, and automation. These technologies generate strong prospects of impact on the value chain, modernizing and optimizing distribution and production processes in manufacturing, as well as an exponential increase in the volume of data in cyberspace. In this context, Digital Twin (DT) is one of the advanced technologies embedded in the I4.0, which provides simulation capabilities to predict, optimize and estimate system states and configurations in a fully digital way in conjunction with cyber-physical systems. DT enables the replication of the real world in the virtual environment through the integration of technologies such as the Industrial Internet of Things, Cloud Computing, and Artificial Intelligence, generating data in real-time. As DT is at the center of a major paradigm shift in I4.0, the use of this technology enables opportunities to simulate cyber-attacks and defend scenarios without accessing the physical environment. However, these opportunities have some risks, as the connectivity implicit in DT between the physical and virtual environment increases the cyber-attacks surface. Cyber security becomes a critical concern to be addressed in projects that use and integrate DT. In this sense, this paper aims to analyze cyber security risks related to DT in the I4.0, and opportunities and challenges when using DT as a crucial component of a comprehensive security strategy, by providing means to simulate, test and monitor systems for vulnerabilities and threats. This study seeks to emphasize the need for best practices associated with the risks of cyber-attacks on virtual models, which can impact the physical systems they represent. This work makes a valuable academic contribution also by highlighting the importance of cyber security in the context of the use of DT in I4.0. [ABSTRACT FROM AUTHOR]

Smart manufacturing (SM) enhances the competitiveness of manufacturing companies by promoting automation and overall equipment effectiveness (OEE), targeting to produce 100% qualified products fully automatically. One of the key challenges to the SM initiatives is the continuous demand fluctuations in the specification and quantity, especially when a new product variant comes to the production line. Reconfigurable manufacturing (RM) system provides cost-effective, rapid response to abrupt market changes. It provides a solution by its flexibility in repurposing tools, adding machines, and modifying software to rapidly respond to changing demands at low unit costs. The ability of SM technologies through self-programming and cloud computation may significantly complements RM initiatives. There is increasing evidence that SM and RM may augment each other through their complementary strengths, leading to the new paradigm of smart reconfigurable manufacturing (SRM). To highlight the complementary strengths, this paper investigates the converging trend of RM and SM based on natural language processing, e.g., topic modeling and semantic embedding. Key characteristics and industrial use cases are subsequently summarized to systematically delineate the new SRM paradigm and illustrate its advantages and feasibility in practice. [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]