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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]

Sustainable development should meet the needs of the present without compromising future generation needs. While political and standards approaches insist on regulatory directives, this paper proposes a quasi-satisficing methodology considering the sustainability topic within a larger encompassing framework. Starting from three reflections originated by the classical thermodynamics, the effect of knowledge accumulation on the Production Possibility Frontier (PPF), and a model for the human wellbeing in manufacturing ecosystem, this paper proposes a new production model and a heuristic approach to drive the decision process towards sustainable manufacturing. [ABSTRACT FROM AUTHOR]

Industrial technologies have evolved towards circular-oriented manufacturing. New approaches in the management of production systems are needed to guarantee the success of such a circular approach. This paper identifies three research questions related to the complexities of decision-making within CEs and defines the concept of Green Gateways in the circular economy. A Green Gateway is a type of decision that supports to realizing the full potential of products and materials. Indeed, the potential value of products and materials must be assessed and leveraged and the concept of Green Gateway will be useful to identify common decisions and to define a common framework in the future. The integration of digital information and digital twins of products, processes, and circular value and supply chains emerges as a key factor in guiding decision-makers effectively, especially for Green Gateways where adequate information, tools and methods must be used to manage value retention options, product flows, production and pricing decisions jointly with the multiple objectives of profitability and sustainability. Additionally, this paper explores specific examples of circular economy use cases, drawing attention to their similarities and highlighting key insights. [ABSTRACT FROM AUTHOR]

The problem of effectively managing Configuration Lifecycle Management (CLM) processes for customizable products in the manufacturing industry presents challenges in striking the right balance between product customization and standardization, particularly for complex products like wind turbines. This paper addresses this issue through a comprehensive analysis and enhancement of CLM processes in a case company that manufactures, install, and service energy solutions focusing on wind turbines. This paper addresses the challenges in CLM processes through a comprehensive analysis and enhancement within a case company specializing in the manufacturing, installation, and servicing of energy solutions, with a specific emphasis on wind turbines. Specifically, within the CLM framework, the study delves into the Input Signal Management (ISM) process, addressing challenges associated with handling specific requirements, features, or functionalities that customers or stakeholders want in the product. The study maps the current state of the processes, identifies weaknesses, and proposes two suggestions to enhance the so-called Input Signal Management (ISM) process. The ISMs are the specific requirements, features, or functionalities that customers or stakeholders want to be included in the product. The challenge in the ISM process lies in handling a large number of requests, ensuring consistency, avoiding conflicts, and making timely decisions. Hence, the paper proposes two key enhancements to the process: the development of guidelines for data creation and categorization of ISM requests, and the prototyping of a Natural Language Processing (NLP)-based system for expedited request assessment. The paper emphasizes the significance of optimizing CLM processes for effective configuration management. Incorporating data-driven methods, the ISM process can become agile, efficient, and customer-centric, leading to better management of customization, reduced response times, and increased customer satisfaction. [ABSTRACT FROM AUTHOR]

In the drive towards sustainable design, the push for products with greater longevity, reparability, and recyclability has never been more crucial. Central to this is the integration of eco-design principles within manufacturing processes. However, there is a gap: manufacturers lack both standardized processes and digital tools to support them, even though the promising digital product passport largely focuses on product lifespan. Key Performance Indicators (KPIs) are paramount, serving as benchmarks for both the manufacturing process and environmental sustainability of a product. These KPIs encompass factors like energy, water, compressed air, and material resource consumption. To emphasize the importance of these metrics, Europe is vulnerable to supply disruptions due to its high dependence on raw materials from non-EU countries. This paper discusses the state of the art of digital twins and presents a digital shadow—a comprehensive digital tool design to support manufacturers during the product design phase. Drawing from a case study in the automotive sector, this tool not only aligns with recycling and sustainability objectives but also mitigates risks associated with raw material dependencies. [ABSTRACT FROM AUTHOR]

This paper presents the results of a case study regarding understanding frugal engineering design and process methodology through the fabrication of wearable tent-jackets for the protection of homeless individuals. Frugal innovations are being investigated for socially conscious products, but an understanding of frugal engineering methodology remains unexplored and is the focus of this research. Multiple socio-economic drivers are resulting in a lack of access to basic human needs such as housing, healthcare, etc. across the globe. Frugal engineering presents an equitable and practical engineering approach to address these challenges and merits exploring a methodology for innovations. In this paper, an origami-based design approach was employed to create a functional prototype of a tent for shelter that can be morphed into a wearable weather protection jacket. We call it a "wearable tent". The prototype was successfully fabricated using a reusable material that is used abundantly in the construction industry. The functional prototype was tested for its efficacy and effectiveness in terms of weight, strength, and ease of switching between the two configurations. The results suggest a potential model that could be explored and applied for socially conscious engineering design solutions for such severely resource-constrained cases for disadvantaged communities with future potential for digitally disadvantaged enterprises. [ABSTRACT FROM AUTHOR]

Wooden-house assembly is an area where still a big part of the work is done manually. In this case study, pairs of operators compose large wooden pieces together based on paper-print instructions complemented by visual guidance in the shape of laser marks projected from lasers mounted in the ceiling, based on Computer-aided design (CAD) data. Augmented Reality (AR) head-mounted displays (HMD) offer a unique platform for providing instructions and additional information superimposed in the work environment and thus can provide guidance in a cognitively ergonomic way. A particular advantage compared to other computing platforms is that the operators have free hands and can perform the manual work and follow guidance simultaneously. We present an evaluation of a prototype that dynamically transforms a CAD data file with design and measurements of wooden trusses to be manufactured, into an AR-based guidance system developed in Unity for Microsoft HoloLens 2 devices. We used an iterative participatory design process for prototyping and think-aloud protocol combined with observations for evaluation, involving professional assemblers in different stages of the process. Participants found the solution to potentially save time in their everyday work and simplify the task by offering increased visibility of the marks compared to the existing laser projection. Large-scale deployment of the system is still facing design challenges of which some are also discussed in the paper. [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]

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]

This paper describes a study on future competence needs in manufacturing and how a learning factory utilising a Connective Model for Didactic Design can be used in teaching and learning of these competencies. The paper briefly reports on a literature study, and a set of interviews in Norwegian manufacturing companies to get a better understanding on the expected future competence needs. This was used to design a learning process with four steps: 1: Exploration, 2: Product and process design, 3: Problem solving and 4: Debriefing. The method was tested in a case study where undergraduate students are learners following the 4-step method. The approach was evaluated through feedback from the learners. The case utilised a Festo CP-Factory learning factory at NTNU. [ABSTRACT FROM AUTHOR]

To abate global carbon emissions, there has been an unprecedented push for carbon transparency in the manufacturing industry. However, with products becoming increasingly complex, they contain sub-assemblies with components that have sub-components supplied by different manufacturers. This creates a multi-tier supply chain that complicates the propagation of sustainability information necessary to compute the product's carbon emission due to the lack of oversight and potential loss of information from manufacturers in the multi-tier supply chain. Blockchain technology, characterized by its immutability, visibility, and traceability, is a promising medium to propagate sustainability information. Despite the fact that many papers have proposed a system that is viable for products with a multi-tier supply chain in the literature, we have not seen its proof-of-concept yet. This work first proposes a blockchain-enabled system that enables the propagation of environmental sustainability information in a multi-tier supply chain. This paper describes the architecture of the proposed blockchain system and the data structure of each block in the blockchain. Based on the defined architecture, we implemented the proposed blockchain system with Ethereum and tested it using the pseudo-multi-tier supply chain of a torchlight. [ABSTRACT FROM AUTHOR]

Demographic change and the digital transformation are megatrends of our time. This paper presents a concept of how to use elements of the digital transformation to transfer knowledge in order to cope with the demographic transitions especially in manufacturing SME. The concept combines visual and tactile sensors in order to determine hand movements. In order to recognize hand movement this paper will review different approaches of how to describe hand movement in manufacturing environments. In an outlook, the application of this concepts and challenges applying the concept in a prototype will be discussed. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) holds a vast potential for various applications and benefits in production, particularly concerning the collaboration of humans and AI. Their complementary skills can lead to significant advantages. However, the implementation of AI presents technical, organizational, and employee-related challenges that must be addressed from a socio-technical perspective. Concrete actions are necessary, such as defining the roles and responsibilities between humans and AI and selecting appropriate AI applications. Given the extensive scope of potential changes and challenges, a systematic approach is required. Therefore, this paper presents an approach for strategically planning of the collaboration between humans and artificial intelligence in production. The approach is based on an extensive literature review and empirical surveys and includes use cases, design fields, and a procedure model. This model allows companies to assess their current situation in terms of relevant capabilities for human-AI collaboration in manufacturing. Additionally, it identifies any organizational capabilities required for human-AI collaboration that may be lacking for specific use cases, enabling the derivation of necessary measures. Some measures may be similar across different use cases, allowing for the creation of bundles of measures to integrate AI into different work processes. The approach is integrated into a company's strategic planning as a strategic initiative, ensuring alignment with other sub-strategies such as the IT strategy. [ABSTRACT FROM AUTHOR]

Since bolted joints are ubiquitous in manufacturing, their effective and reliable quality assurance is particularly important. Most tightening processes rely on statistical methods to detect faulty screw connections already during assembly. In this paper, we address the detection of faulty tightening processes using a clustering based approach from the field of Unsupervised Machine Learning. In particular, we deploy the k -Means algorithm on a real-world dataset from the automotive industry. The model uses Dynamic Time Warping to determine the similarity between the normal and abnormal tightening processes, treating each one as an independent temporal sequence. This approach offers three advantages compared to existing supervised methods: 1.) time series with different lengths can be utilized without extensive preprocessing steps, 2.) errors never seen before can be detected using the unsupervised approach, and 3.) extensive manual efforts to generate labels are no longer necessary. To evaluate the approach, it is applied in a scenario where actual class labels are available. This allows evaluating the clustering results using traditional classification scores. The approach manages to achieve an accuracy of up to 88.89% and a macro-average F1-score of up to 63.65%. [ABSTRACT FROM AUTHOR]

In the perspective of feed-forward control through a manufacturing process chain, production data can be used downstream to correct subsequent processes and improve product quality. In an extrusion blow-moulding (EBM) use case, supervised learning (SL) has been applied to predict geometrical dimensions based on process data, as a possible basis for feed-forward control. The labeled tabular dataset showed signifcant time dependency which complicates the learning task. In this paper we present a time-series regression approach and compare three common SL algorithms – Random Forest, Gradient Boosting and XGBoost. The results show that only part of the variations in product geometry could be learned from process data and that future work will be necessary in order to increase the models' performance. [ABSTRACT FROM AUTHOR]

In manufacturing, different costs must be considered when selecting the optimal manufacturing configuration. Costs include manufacturing costs, material costs, labor costs, and overhead costs. Optimal manufacturing configurations are those that minimize production criteria, such as costs, production speed, and flexibility, while still meeting the required production levels and quality standards. To find the optimal manufacturing configuration, manufacturers often use a combination of traditional techniques, e.g., mathematical modeling, simulation, and optimization, to evaluate the tradeoffs between different cost factors and identify configurations that provide the best balance between cost and performance. However, these techniques may require long development and simulation time, and/or may require expert knowledge. This paper presents a method for selecting the optimal manufacturing configuration, focusing on cost optimization, using a reinforcement learning (RL) approach for sequential decision-making. The proposed method involves developing a RL environment, requiring lower development and simulation times than traditional techniques, that captures the incurred costs, recurring costs, production rates, and setup times of manufacturing configurations. The problem is then solved using the Proximal Policy Optimization algorithm to identify the configuration that minimizes costs while still meeting the required production levels and quality standards. The effectiveness of the proposed method is validated through a machining process planning case study with multiple cost factors and production constraints. In particular, the machining process plan was developed for an industry-relevant product prototype. The results show that the proposed method can find solutions that are robust to stochastic noise, providing valuable insights for manufacturers looking to optimize manufacturing operations. [ABSTRACT FROM AUTHOR]

Driving innovation has become necessary for securing manufacturing companies' competitive capabilities in the context of digitalization, human centricity, sustainability, and resilience (Industry 5.0). However, due to its costs and uncertainties, the innovation process carries a high economic risk. Decisions of this magnitude can only be legitimized by a well-founded and thus often costly project evaluation. This paper aims to present a comprehensive evaluation approach to support companies in evaluating and classifying manufacturing innovation projects in the early phase of the innovation process. Based on a comprehensive literature analysis and expert interviews, a concept for an evaluation technique combining four dimensions has been elaborated. The first dimension is the potential analysis. It is evaluated using a type-1 Mamdani Fuzzy expert system, analyzing potentials to compute the effect of innovation on competitive strategic goals in manufacturing. Second, the initial efforts are identified by providing a structured list of possible expenses. The third dimension is an innovation project's overall risk to a company. It is calculated using an adapted Failure Modes and Effects Analysis. The failure modes are identified with the help of a comprehensive overview of potential risks that need to be evaluated regarding their significance, probability of occurrence, and reaction possibility. The fourth dimension helps the user to classify the selected innovation projects as incremental or radical for the company. The results of the four evaluation modules are summarized and visualized for their use in the decision-making process. Concluding, the approach has been validated by an initial use-case application to an industrial decision problem and verified by evaluating the approach's performance regarding the fundamental requirements for decision support systems. [ABSTRACT FROM AUTHOR]

A Digital Twin (DT) is a virtual replica of a product or product-service system, which can be used to provide transparency of a product's sustainability and to positively influence the ecological impact throughout its lifecycle by means of intelligent data analytics. This paper identifies current sustainability-focused application scenarios of DTs in the manufacturing industry and outlines the results of a systematic literature review (SLR). The identification of the state-of-the-art and the assessment of current DT concepts with regard to the addressed product lifecycle phases, technological maturity and sustainability scope point towards key directions to guide future research. [ABSTRACT FROM AUTHOR]

In this paper, a high-level methodology for the transition to environmentally sustainable practices in the manufacturing industry is developed. The methodology presented in this paper is at the conceptual level and is developed to be applicable to a wide array of industrial settings. The sustainability transitioning methodology provides decision-makers with a roadmap timeline for the methodical decision-making to adopt environmentally sustainable technologies and practices. This is accomplished through integrating two key tools for the selection of new technologies: the technology readiness level, and the ease of implementation versus the impact analysis. The proposed methodology informs decision-makers of the priorities and the perceived impact of the potential technologies. Test and validation are carried out with a case study from the United Kingdom's aerospace sector. Results from the case study revealed that applying the methodology could influence decision-makers to approve or dismiss the use of new technologies. [ABSTRACT FROM AUTHOR]

Industry 4.0 is accompanied by various technologies, which offer great potential for optimizing today's manufacturing of electric motors. To meet high-quality standards and to enable further improvements, methods from artificial intelligence, in particular machine learning (ML), are recently moving into focus. Although needle winding is one of the most widely used processes for winding stators with inner slots, the potential of ML has not yet been tapped. Therefore, this paper introduces a novel, data-driven approach for quality monitoring of needle winding processes using ML techniques. To acquire quality-related process data, a common needle winding machine is equipped with suitable sensors first. Based on this, a proof of concept for an ML-based regression model aiming at predicting the coil quality solely based on process data is provided. [ABSTRACT FROM AUTHOR]

Manual removal of burrs on castings introduces health, safety, and environmental concerns. Automated removal of highly variable casting burrs could improve safety, but requires a solution based on robots, smart sensors, and advanced algorithms to tackle the problem in a flexible and cost-effective way. This paper presents a system for automatic tool trajectory generation for robotic deburring of cast parts with a specific focus on the robotic tool trajectory generation algorithm. The system generates a robotic trajectory adapted to the specific workpiece based on CAD model and 3D scans of the workpiece. The registered 3D scans and CAD model is used to generate a 3D model of each individual workpiece. This is fed into the tool trajectory generation algorithm. The algorithm uses the generated 3D model as well as a priori knowledge of the casting process to generate the tool trajectory. The tool trajectory planning algorithm has been tested on a set of various 3D models of cast parts, and the generated robotic tool path has been both simulated and tested on a KUKA KR 60. [ABSTRACT FROM AUTHOR]

Deploying machine learning (ML) models in production environments comes with challenges such as the model's integration into live production and the missing trust of process experts in new technologies. These challenges must be addressed already in phases ahead of the deployment. Therefore, this paper aims to clarify how to ensure the deployability of methods used during model development. For this purpose, criteria for measuring and evaluating deployability in manufacturing environments are defined. A subsequent analysis of existing data preprocessing methods and ML algorithms regarding deployability as well as deployment options serves to counteract deployment issues early on in an ML project. [ABSTRACT FROM AUTHOR]

Manufacturing companies are increasingly pushed towards more sustainable production by socio-political and environmental factors. In order to assess their sustainability performance, companies often use the widespread life cycle assessment (LCA) as defined by ISO 14040 and supported by various LCA software solutions. However, this is regularly associated with well-known shortcomings such as difficulties in gaining appropriate life cycle inventory data, lack of result transferability, and the amount of time needed for LCA studies. To overcome these problems, approaches for automating the LCA process have proven to be promising in other engineering disciplines (e.g., civil engineering). However, hardly any approaches to automating the LCA of manufacturing processes exist. Based on an extensive literature review, this paper presents a concept for an automated LCA of manufacturing processes. The approach consists of three steps and covers the phases of data extraction, LCA model building, and analysis. It is geared towards using implicitly existing manufacturing information and facilitating the so far mostly manual data collection and modeling processes. To conclude, an outlook is given on how the proposed concept will be further developed in an industrial setting. [ABSTRACT FROM AUTHOR]

In manufacturing value chains (MVC), an increasing globalization used to be the persistent direction for decades. Recent crises massively disturbed global MVCs and lead to the question, if a trend reversal, a "deglobalization", is about to happen. Some authors already see this trend reversal as a given, but did not provide any supporting data so far. This paper intends to shed light into the topic by reviewing underlying reasons, motives and key drivers for companies to operate in global MVCs, identifying corresponding indicators, and gathering data for these. The subsequent analysis of the data reveals a distinct slowdown of globalization growth rates, but no trend reversal, yet. [ABSTRACT FROM AUTHOR]

3D bioprinting is an emerging technique employed for engineering complex structures that can be used for regenerative medicine. Extrusion of hydrogels through a nozzle has produced layer by layer designs towards engineering patterns. Despite the significant advances of these systems in the biomedical field, there are some inherent constraints to achieve higher resolutions without impacting the viability of the cells contained in the bioinks. Recently, there has been research on polymerization using light and photosensitive materials that point toward finer details within more reproducible manufacturing conditions and more complex products. Recent advances in bioprinting include the implementation of the construction of multimaterial structures to mimic in a more precise way a complex tissue. This paper showcases the development of a novel design using a retrofitting strategy toward a Material Efficient and Low-Cost Bioprinting system based on a Liquid Crystal Display (LCD) technique that reduces the required materials with a low-volume material reservoir and optimizes the processing parameters. Towards a new generation of devices, we present a novel open-source retrofitting kit for adapting a low-cost commercially available Anycubic 3D printer to process a bioink. As a proof-of-concept, an initial study of the concentration of gelatin methacryloyl (GelMa) and Lithium Phenyl -2, 4, 6 -trimethylbenzoylphosphinate (LAP) photoinitiator and the manufacturability is hereby presented for hexagonal, circular, and square features. The design of a new setup enabled a reduction of 90% of the required bioink. While further research is required to validate this concept, this is a step towards a new generation of affordable bioprinting systems. [ABSTRACT FROM AUTHOR]

In the complex and dynamic conditions of global markets it is increasingly challenging for manufacturing companies to find or identify new reliable and meaningful key performance indicators. Traditional performance indicators like lead time, inventory level, on-time delivery rate and capacity utilization are still in focus, but interdisciplinary research suggests the inclusion of more innovative performance indicators, such as synchronization. The synchronization level of a manufacturing system can be viewed twofold: logistic and physical synchronization. Physical synchronization is defined as "the rhythm and repetitive behaviour of production processes", while logistical synchronization describes "the coupling of work systems that are linked by material flows." Both types of synchronization correlate with the logistics performance of companies. Previous research on this topic has already provided first insights, highlighting potentials and fields of application. In this paper, we provide a structured literature review underlining relevant applications and explore their respective potentials. Thus, we show how synchronization effects can be exploited, and which systemic properties, such as network topology, characteristics and process time variation influence the occurrence of synchronization. [ABSTRACT FROM AUTHOR]

Predicting product quality represents a common area of application of machine learning (ML) in manufacturing. However, manifold challenges occur during the integration of ML models into production processes. Therefore, this paper aims to provide a guideline for the deployment of ML models in production environments. Relevant decisions and steps for deploying models in predictive quality use cases are demonstrated. The results for each component of the proposed guideline - deployment design, productionizing & testing, monitoring, and retraining - have been validated with industry experts including exemplary implementations. [ABSTRACT FROM AUTHOR]

Innovation managers constantly seek to improve manufacturing systems using continuous improvement as a core process. On the other hand, significant technological advances promise to improve manufacturing systems' performance radically. Therefore, manufacturing units must be able to deal with complex and volatile environments and at the same time act explorative and exploitative to produce both incremental and radical innovations. The organizational ability to achieve this balanced target state is called ambidexterity. The challenge of continuously increasing efficiency while at the same time developing and integrating radical innovation into the manufacturing system has grown enormously in recent years. Consequently, a severe challenge is imposed on the innovation management of the manufacturing unit. This implies ambidexterity management in manufacturing, encouraging organizational research to identify relevant design dimensions and describe concrete managerial implementation measures in a manufacturing context. Therefore, this paper presents an approach to enable ambidextrous innovation management in manufacturing, fostering radical innovation. Relevant organizational dimensions and their interconnections are identified using scientific literature and expert interviews. In addition, examples of concrete managerial implications for implementing ambidexterity in manufacturing are presented. [ABSTRACT FROM AUTHOR]

Analyzing data with the help of Machine Learning (ML) promises to raise significant potentials in all relevant target dimensions and different application fields of industrial production. Through the increasing availability of data in the context of digitalization as well as continuously more powerful and cost-effective possibilities for data processing, the amount of economically viable scenarios for implementing ML-based business models (BMs) in production rises. Despite the emerging data-related possibilities, especially small and medium sized enterprises (SMEs) struggle with identifying reasonable use cases for ML in their own company. This can be ascribed to a lack of knowledge about the necessary elements for ML applications' sustainable implementation and operation. Therefore, this paper aims to develop a framework for the holistic generation of ML-based BMs in manufacturing. At first, characteristics as well as general and specific requirements for ML-based BMs in manufacturing are elaborated. Subsequently, a morphology for the systematic development of ML-based BMs is generated using the insights gained. In a concluding step, the application of the developed concept is validated based on a selected use case. [ABSTRACT FROM AUTHOR]

This paper presents prospects for a human-centered design of future industrial labor in a framework depicting the dilemma between technological feasibility and labor-politically desirability, under the constraint of an economically reasonable design of work and technology. Using the example of order picking systems, this paper focuses on the challenges of progressive digitization in industrial processes. The analytical approach is the socio-technical system, which assumes that new technologies will also result in personnel and organizational changes. These considerations form a framework, which amalgates the perspectives of human actors, technology and organization and ultimately leads to a complementary design of industrial labor. [ABSTRACT FROM AUTHOR]

The process that involves companies developing the capabilities to provide services and solutions that supplement their traditional product offerings is commonly called servitization. With regards to manufacturing, the servitization process has been found to provide many advantages, such as: increasing the value of the offering, enhancing product customization, developing customers’ loyalty, generating profits throughout the whole product life cycle, etc. Despite the many studies related to this process, there is a major lack concerning its quantification. Through an in-depth analysis of medium-large size Italian companies in the manufacturing sector, this paper examines the process of servitization proposing (i) an assessment of its extent in terms of percentage of servitized companies (ii) a taxonomy of the services offered and (iii) an estimation of their relative diffusion. Also, the paper offers a perspective on the effects that this process may have on production technologies and systems. [ABSTRACT FROM AUTHOR]

This paper presents an approach for a model-based planning process for the early phases of manufacturing system planning (MSP). The goals are a better integration of MSP with product development (PD) in the early design phases and an improvement of the coordination of the MSP-related planning disciplines. The presented approach is based on model-based systems engineering (MBSE) and is supported by a modeling scheme which uses the systems modeling language (SysML). The approach consists of a process description of the MSP and of different so called SysML views which describe the information artifacts of each MSP step. It is divided into four different modeling levels that describe the manufacturing system's structure and behavior from different points of view. In order to validate the approach, an exemplary cylinder head production was modeled. Extractions from this example are shown as SysML-diagrams. To conclude the paper, the advantages and disadvantages of using a model-based planning approach with SysML for MSP are discussed. [ABSTRACT FROM AUTHOR]

Over the last decades, one of the steadiest challenges for manufacturing companies is the need for reduction of time-to-market and for enhancement of the efficiency of their product engineering process (PEP). Triggering factors are changing customer demands, shorter technology cycles that enforce product changes and a noticeable market saturation. Hence, companies need to increase the efficiency of the PEP despite considerable improvements in this field such as concepts of simultaneous engineering and digital factory. Today, the three main phases of the PEP (product development (PD), manufacturing system planning (MSP) and manufacturing) are still performed sequentially. Due to already existing improvements in parallelizing steps of the PEP, efforts for further improvements should concentrate on the interface between MSP and PD. Traditionally, MSP starts after PD is completed. Thus, on one side, information regarding the product's manufacturing is withheld from product developers. As a result, decision making assistance for selection between design alternatives is not available. On the other side, MSP is not able to influence the product design. This leads to more expensive or not manufacturable products. This paper presents an innovative approach for an integrated MSP based on fuzzy information from early phases of PD. It solely uses information from specification phase and conceptual design phase of PD within the first steps of MSP. The approach aims at noticeable improvements e. g. more frequent feedback loops, reduction of expensive correction loops after product development, and a smooth manufacturing ramp-up. The paper summarizes the theoretical foundations for PD and MSP, highlights the need for research, and outlines further steps for the concept development. Additionally, early phases of PD and MSP are described and investigated in more detail with regard to links between the two domains. The basis for the development of a concept is given which aims at an early start of MSP and more efficiency within the PEP. [ABSTRACT FROM AUTHOR]

This paper presents the initial results of a wider research project that investigates how service design might positively contribute to the development of product-service systems within manufacturing small to medium sized enterprises. The paper presents the results of interviews with three firms that have begun to embrace service design. The analysis of these results is used to produce a conceptual framework with nine dimensions that aims to aid understanding of a company's potential readiness for servitization through service design. [ABSTRACT FROM AUTHOR]

The Fourth Industrial Revolution (Industry 4.0) has created significant technological growth for manufacturing organizations worldwide, attracting important attention from the research community. Industrial automation and the introduction of smart digital technologies to traditional manufacturing processes has led to a generation of intelligent production methods to engineer smart products. In the last few decades, the term 'maintenance' has evolved with researchers offering various perspectives. The aim of this paper is to identify the issues related to industrial maintenance, uncovering its historical evolution, and providing a perspective for new types of industrial maintenance linked to Industry 4.0. [ABSTRACT FROM AUTHOR]

Digital transformation potentially improves business processes, leading to increased flexibility in manufacturing and logistics networks. Therefore, decision support systems for responsive manufacturing are gaining importance. In this paper, a discrete event simulation model is applied. In order to assess the potential of digital transformation in the context of Industry 4.0 towards production and logistics network robustness, different simulation scenarios are observed. The results contribute to identify potential flexibility measures and to quantify their impact in order to mitigate control uncertainties within production and logistics networks. [ABSTRACT FROM AUTHOR]

Research on Virtual Reality (VR) in manufacturing is rarely driven by environmental sustainability. This paper explores the feasibility of developing VR technologies to reduce environmental impact, drawing from a case study in an automotive company. We developed a VR demo to support the communication for design review between the technology centre in Sweden and the manufacturing site in China, thus reducing travel frequencies. This reduction was verified through user experience and feedback from focus group discussion and questionnaire. The results show a reduction of travel frequencies for design review, therefore contributing to environmental sustainability. [ABSTRACT FROM AUTHOR]

The production of customized furniture and other objects in woodwork currently needs a high amount of manpower in the adaptation of production steps and results in high construction costs, even for machine-milled parts. Thereby and due to the still increasing shortage of skilled employees there is a high demand for adaptive and automated production, especially in milling. This paper shows an approach, which addresses the need for high adaptability by an innovative part-to-tool-application. By enabling a conventional 6-axis robot to handle all production steps, a multi-spindle-concept and an automatic material supply the production of individualized wooden parts becomes fully autonomous. [ABSTRACT FROM AUTHOR]

Industry 4.0 is characterized by the transformation and networking of production systems as a result of utilizing digital technologies and processing huge amounts of data. The features of Distributed Ledger Technologies have the potential to revolutionize production by creating trust and transparency in stored data, eliminating dependence on single entities and performing automated transactions in real time. Due to the disruptive change coming with an implementation, manufacturers remain skeptical about the technology. To raise awareness of the potential of Distributed Ledger Technology, this paper describes valid use cases and examines the interactions with business models in real world manufacturing scenarios. [ABSTRACT FROM AUTHOR]

Every manufacturing process undergoes geometrical deviations from the defined geometry. In order to produce workpieces within the specified quality, a downstream quality assurance is carried out. This quality assurance is expensive and time consuming. If the geometrical error could be estimated during and after the manufacturing process this quality assurance can be reduced. The geometrical error emerges due to different causes. One of these causes is the deflection of tool and workpiece. This alters the depth of cut and therefore causes a radial error on the machined workpiece. An additional cause is the progressing tool wear during continuous machining, also influencing the depth of cut. This paper presents a method to determine the radial error after turning. The method is based on finite difference calculations, suitable for the investigation of the local error of multi-diameter workpieces. Within the developed model the deflection due to shearing and bending of the workpiece and the tool are examined. Furthermore, the tool wear is considered. Additionally, the calculation for a locally varying depth of cut is presented. The results show that by taking the tool wear into account the model error was decreased from 63.08 % to an absolute error of 5.72 %. Using the proposed model, it is possible to generate relevant data about the geometrical manufacturing error immediately after every machining process. [ABSTRACT FROM AUTHOR]

Demand volatility and uncertainty in the business environment lead to an increasing relevance of volume-oriented changeability (VoC) for manufacturing companies. Strategic investment decisions are often based on forecasts and predictions, which more frequently fail to materialize due to obsolete assumptions or unpredictable events with extreme impact. If the production output fluctuates, fixed costs of production plants emerging from these investments result in high variance of unit costs, which has an impact on operational performance. Despite these demand and investment risks, managers on the strategic level must make capacity decisions without endangering the plant's performance both in growth and decline phases. Therefore, the paper conceptualizes building blocks for strategizing VoC of assets in production plants. These building blocks systemize capacity strategies for economical and dynamical up- and downscaling of production output. The paper depicts the impact on financial targets and analyzes contextual requirements and interdependencies with the organizational concept. By means of this decision support concept, managers of the production plant can select and combine interdisciplinary measures for developing an asset management strategy in the face of demand volatility and uncertainty. [ABSTRACT FROM AUTHOR]

Due to dynamic business environments and volatile customer requirements, manufacturing companies are increasingly relying on agile development methods to implement changes to their products more quickly. The drive towards speed, however, is in conflict with the thorough analysis of engineering change impacts on more and more complex manufacturing systems. In this context, efficient change management that enables rapid processing of changes while maintaining consistently high product quality is key for competitiveness. This paper examines existing literature and presents a concept for the data-based improvement of engineering change impact analyses in manufacturing. [ABSTRACT FROM AUTHOR]

Storage technologies such as lithium-ion batteries (LIB) are a key technology to enable emerging transportation as well as sustainable energy policies. The manufacturing of LIB cells is characterized by high scrap rates of up to 40 % in the industry and a high energy demand, leading to a high environmental impact and high costs. Furthermore, the LIB cells themselves and their manufacturing are highly complex systems with various quality parameters and complex material flows. However, with the rising demand for energy storage technologies, the production of LIB cells needs to become more environmentally friendly and more cost-effective. To achieve this, a better understanding and a higher transparency of the material and energy flows in the context of quality parameters is needed. This paper presents a gate-to-gate methodology for the evaluation of material and energy efficiency in context of quality parameters of LIB cell manufacturing. The implementation of the presented concept is based on data acquisition and a tracking and tracing system and tested at the pilot production line of the Battery LabFactory Braunschweig (BLB). The results of this assessment can be used as inputs for further sensitivity analysis in system assessment methodologies such as Life Cycle Assessment (LCA) or Life Cycle Costing (LCC) respectively Total Cost of Ownership (TCO). [ABSTRACT FROM AUTHOR]

Sustainability assessment in manufacturing has predominantly focused on environmental dimensions without accounting for absolute (consumption and or pollution) limits and has remained limited to efficiency-based improvements. Efficiency-based assessments are increasingly yielding counterintuitive results. An effectiveness-based approach could integrate local and global absolute pollution limits in the sustainability assessment and ensure resource-effectiveness using circularity indicators. Circularity is increasingly viewed as a viable approach to achieving sustainability by discerning the underlying interconnected, diverse systems. This paper presents a preliminary systems-based framework for effective sustainability assessment in manufacturing. Steps to understand and identify effectiveness indicators have been discussed. A case study of photovoltaic manufacturing industry has been discussed in the context of the framework. [ABSTRACT FROM AUTHOR]

By looking at the last few decades, industrial production has undergone great changes. Industry 4.0, also called the fourth industrial revolution, describes the change in the entire value chain through the digital networking of systems, machines, and products. In addition, product variety and complexity in assembly increased due to customization. Big Data Analytics, Internet of Things, Horizontal and Vertical Data Integration and Cyber-Physical Production Systems are just some examples of technologies that find their way from research into industrial practice. However, the most important resource is often neglected, when talking about industry: the human. When we look at companies, we find different types of personnel in production, each with different requirements and capabilities. Assistance systems can be used to counteract these new challenges and offer adequate support to each individual worker. In the past, much research has been done to develop new worker assistance systems, while the analysis of specific needs of user groups in production has been ignored. This paper presents a function-based mapping of industrial worker assistance systems to different user groups and proposes a method for selecting the most appropriate assistance system to each user group. [ABSTRACT FROM AUTHOR]

The quality of production planning and control in production companies largely depends on the accuracy of the database regarding the status quo of the production process, as well as the time required for each production step. Especially in single and small batch production, it is difficult to create a reliable database due to the large product spectrum and the variation in the production process. For this reason, this paper examines the potential of Bluetooth Low Energy (BLE) Beacons to create a low-cost real-time locating system (RTLS). In comparison to other existing RTLS solutions like RFID, BLE stands out through very low hardware costs and a high compatibility to almost every mobile device. Through a signal strength based approach in combination with probabilistic neural networks, the RTLS has been able to achieve a positioning accuracy with a mean absolute error of up to 0,74 m in an industry-like environment. [ABSTRACT FROM AUTHOR]

Over the past decades, robots have been extensively deployed in multiple industries. More recently, industrial robots have been taken out of their cages, being more present in dynamic, uncertain environments interacting in the vicinity of humans. Traditionally, robots have been mainly developed to perform pre-programmed tasks. However, some tasks are too complex or expensive to be performed by a robotic system alone. One of these hard tasks is the handling of large fibre sheets in composite part production. This paper presents a force-based approach towards human-robot co-manipulation of tasks involving the handling of non-rigid materials, such as composite fibres. Our approach fuses the data of a force-torque sensor and skeleton tracking data from a 2D camera to control the mobile manipulator in an intuitive manner, by using the intelligence of the operator as much as possible. By using this approach, tools such as path planning, high-level task planning, or modelling of objects to be manipulated are not essential to obtain the results. The overall approach is illustrated with a co-manipulation proof-of-concept experiment in which a mobile manipulator robot handles a flexible textile sheet together with a human operator. [ABSTRACT FROM AUTHOR]

The impact of the forth industrial revolution on the social and natural environment is considered significant and far-reaching, even though the interactions of the human, natural and manufactured assets are less understood, extremely complex and unpredictable. The paper discusses how present days' – so-called cyber-physical – manufacturing systems operate in the fabrics of society and the natural environment. It is underlined that they show and – to an increasing extent – will show features and capabilities reminiscent of living beings and organizations. This view helps to understand how manufacturing interacts with the ecosystem and suggests resolutions for the dilemma of competitive and sustainable manufacturing. [ABSTRACT FROM AUTHOR]

In the production chain of Lithium-ion battery (LIB) cells, various processes influence intermediate product features, which then influence the LIB performance. It is important to know these influences in order to improve product quality and to control the production. This paper presents a concept for a data-driven cyber-physical system based on quality gates in LIB cell manufacturing. The concept utilizes data-driven modelling in order to predict the performance of future LIB cells by using quality gates of intermediate product features. This prediction of product performance enables a better assessment of intermediate product quality and helps deriving recommendations for latter processes. [ABSTRACT FROM AUTHOR]

The production of Lithium-Ion Battery (LIB) cells is characterized by the interlinking of different production processes with a manifold of intermediate products. To be able to ensure high quality and enable a traceability of different production and product characteristics (e.g. energy consumption, material), a tracking and tracing concept is required. In this paper, a practical tracking and tracing concept throughout the production of LIB cells, enabling inline data-driven applications, is introduced. As a part of this concept an intelligent tracking and tracing platform is shown, which allows the generation of a pre-clustered data sets to facilitate future data-driven applications. [ABSTRACT FROM AUTHOR]