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

Digitalisation provides many opportunities for process improvements by using smart devices such as Augmented-reality (AR) displays in production. Nevertheless, in-depth studies about the effects of these applications on key performance process indicators are sparse. The goal of this study is to evaluate AR-based remote maintenance processes supported by cutting edge optical head-mounted display technology. A total of 12 test runs conducted with real-world maintenance engineers in a real-world industry setting were analysed based on video recordings and semi-structured interviews. The study identified both case-specific and general issues, and the inherent potential of the technology.

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

Use of bio fuels is a sustainable solution in the current energy scenario, which is marked by unsustainable use of fossil fuels. Production of biodiesel requires microalgae as feedstock. The micro algae require nutrients, CO2 & light source for its growth. An approach where waste water treatment plant can act as source of nutrients for the growth of microalgae would be beneficial. Hence the present work was carried out to understand the life cycle assessment of biodiesel production from microalgae grown in waste water and associated impacts compared with biodiesel production from fresh water. The system boundary considered in the process includes cultivation of microalgae, flocculation, centrifugation, extraction, and transesterification. The functional unit considered for the study is 1 MJ of energy produced from biodiesel. The life cycle assessments (LCA) for the present case was carried out using Umberto NXT software and inventory was taken from Eco invent database v3.0 and literature. Results has shown that the microalgae grown in waste water requires lesser energy as compared to algae grown in fresh water.

In cooperation between a German and a Vietnamese manufacturing laboratory, activities of training for assembly are performed by support of motion tracking at similar workplaces. The assembly of bicycle e-hubs is trained with Vietnamese students to check the transfer of assembly process knowledge between distant countries. The experimental set-up consists of the remote “teach-in” of assembly instructions, motion tracking on workplaces and the evaluation of student's performance. The applicability of motion tracking vs. traditional paper based instructions for initial learning is investigated.

Augmented Reality Technology Augmented Reality Technology (AR) is one of the key technologies of digital transformation in industrial and non-industrial areas. Due to the rapid development of display hardware and tracking systems, virtual and augmented reality applications are being developed today that would only have been possible in large research laboratories with huge efforts a few years ago. This paper presents the application of the augmented reality technology in the industrial field. Based on industrial case studies the implementation of two AR applications and their potential to digitize the product lifecycle processes will be discussed. The case studies were developed within cooperation projects between the University of Applied Sciences Karlsruhe and a fire truck manufacturer with the focus on the following topics: product configuration management and assembly assistance. The objective of this work is the investigation of the AR application in the industrial environment and its capability as digital transformation technology along the product lifecycle. The presented paper delivers experiences and suggestions in terms of praxis-oriented development and deployment of the AR technology in the engineering field, which can be used for future AR research works.

Widespread use of carbon fibre across various industry sectors remains limited due to the high costs of the raw materials and energy demands during production. Furthermore, the environmental impacts associated with its manufacturing process, may significantly reduce the potential benefits of weight reduction or material savings in the end products. Despite the excellent mechanical and chemical properties, which would make the material a viable substitute in a wide range of applications, further production and cost improvements are necessary to raise interest and demand. The scarcity of available carbon fibre production related data and the confidentiality of the few commercial carbon fibre suppliers hinders further research efforts towards these improvements. This paper proposes a dynamic, modular Life Cycle Assessment (LCA) and Life Cycle Costing (LCC) concept tailored for carbon fibre production and is based on a pilot production set-up (120t/year). The model is intended to accelerate the assessment of carbon fibre production and serves as a tool for the exploration of various scenarios, including alternative precursor material, different carbon fibre grades or varying production parameters. In this paper the model is specified, embedded in the environment it is intended to be used and applied to a production setup based on data collected using the pilot scale carbonisation line at Carbon Nexus, a unique, open-access carbon fibre/composite research facility in Victoria, Australia.

Electric stand-up scooters become increasingly popular for both private and shared use. They are a mass product but need to be adapted to different use cases or the local conditions of different cities. However, the sustainability of electric scooters is a matter of public debate, especially because of their short lifetime. Studies show that the reliability of e-scooters is one of the main factors influencing their environmental impact during their life cycle. This paper addresses the research question of the potential advantages and disadvantages of a reliable electric scooter from the perspective of ecological sustainability. It shows how reliability characteristics, especially durability, can affect product sustainability of mass customized products like e-scooters. The paper uses life cycle assessment (LCA) as a methodology. It suggests approaches to include reliability characteristics into LCA and life cycle engineering. Using the three case studies of electric scooters as an example, it outlines the influence of innovation cycles and modular design of batteries, of component durability and scooter lifetime as well as different maintenance strategies on the environmental impact of e-scooter sharing. Altogether the paper discusses possible trade-off between the advantages and disadvantages of improved reliability on the life cycle performance of e-scooters.

Although the increasing use of automation in industry, manual assembly stations are still common and, in some situations, even inevitable. Current practice in manual assembly lines is to balance them using the takt-time of each workstation and harmonize it. However, this approach mostly does not include ergonomic aspects and thus it may lead to workforce musculoskeletal disorders, extended leaves, and demotivation. This paper presents a holistic human-centric optimization method for line balancing using a novel indicator the ErgoTakt. ErgoTakt improves the legacy takt-time and helps to find an optimum between the ergonomic evaluation of an assembly station and its balance in time. The authors used a custom version of the ErgoSentinel Software and a Microsoft Kinect depth camera to perform online and real-time ergonomic assessment. An optimization algorithm is developed to find the best-fitting solution by minimizing a function of the ergonomic RULA-value and the cycle time of each assembly workstation with respect to the worker's ability. The paper presents the concept, the system-setup and preliminary evaluation of an assembly scenario. The results demonstrate that the new approach is feasible and able to optimize an entire manual assembly process chain in terms of both, economic aspects of a well-balanced production line as well as the ergonomic issue of long term human healthy work.

Products are increasingly individualized and enhanced to be able to communicate, e.g. via Industrial Internet of Things (IoT). However, the impact of products on sustainability (environmental and social) across their life is often not considered and analyzed. IoT-based or smart products, that are able to communicate, generate data, which can be used to monitor and optimize sustainability indicators. The Digital Twin (DT) is a new technological concept which focuses on product individual data collection and analysis. It provides the possibility to make use of the available data and optimize the systems individual sustainability as well as future product generations. However, the design and realization of such a DT requires new approaches and capabilities, which is an identified research gap. Therefore, this paper presents a methodology to develop DTs of physical IoT-based products, the so called DT V-Model with the aim to optimize the systems sustainability, specifically environmental aspects. It is based on the V-model for the development of smart products and is enhanced with additional roles and approaches for DT development. The methodology is described in detail. The result of a development cycle according to the DT-V-Model is a tested concept of a DT, which includes Digital Master (DM) data from the planning phase and Digital Shadow (DS) data from the production, operation and End of Life-phase. For a DT for energy efficiency, the Digital Master model consists of the information and models from the product development phase including the planned production and use phase energy consumption. The Digital Shadow consists of the actual production energy consumption and the use phase energy consumption. The methodology is applied to a use case of an IoT-based consumer product that can be customized to a certain degree by the consumer. A DT is developed to monitor and optimize the products energy efficiency in production and use. The necessary elements of the DT and the capabilities are depicted. The paper shows the feasibility of the methodology for the development of DTs, the necessary adaptions to common approaches for development and the specific characteristics of DT development for the aim of energy efficiency.

The large demand of high-performance steels to improve the safety and energetic performances in automotive is driving the increasing need of higher uniformity of mechanical properties of rolled products, both within a single coil and in large batch productions of multiple coils. To achieve these targets, we are witnessing an evolution of rolling towards the introduction of sensors and Artificial Intelligence (AI) algorithms to allow the real-time monitoring of both the strip tensile properties and the evolution of microstructure during the annealing process. The work described in the paper is part of a research work that is aimed at developing an AI framework that could allow the full real-time control of the entire mill rolling plant thanks to (i) innovative sensors applied at different steps of the coil processing, (ii) mixed analytical and numerical algorithms for the data computation and analyses, and (iii) IT infrastructure to collect and elaborate the data. The focus of the paper is on the tension-levelling operation which is one of the first that is performed in the rolling mill and can have a relevant influence on the strip mechanical characteristics. To reach this aim, a predictive model applicable to the tension-levelling process was developed, as this process is used to minimize flatness imperfections and residual stresses by means of plastic deformation. During the process, the material is subjected to cyclic tension-compression stresses, obtained with repeated and alternate bends under superposed tension. Consequently, the desired product standards, in terms of thickness and flatness, are achieved, while the mechanism of deformation changes the final mechanical properties of the material, increasing the yield strength and decreasing the maximum elongation. Finally, the predictive model is used to predict the coil mechanical characteristics, by using the data sampled from the sensors placed inside the tension-levelling machine.

During the last decade, the European manufacturing industry has experienced a growing trend towards customization and personalization. As a response to increasing global competition and changing customer needs, there has been increasing attention to achieving shorter time-to-market, and manufacturing products for smaller market segments. Throughout this paper, the term ‘high variety products’ will be used to describe products that contain at least one product part with a customized geometry. There are two primary aims of this study. First, to illustrate the current challenges that Small- and Medium-sized manufacturers face with high variety products. Second, to explore how these challenges can be addressed systematically. Assembly tasks within existing manufacturing systems for high variety products typically involve a combination of manual labor and automatization. As geometrical variation is considered complex for automation, cost considerations can hinder increasing the level of automation in assembling high variety products. However, this objection might not be legitimate. Hence, this paper proposes a new design method to improve decision making for cost-effectively realizing high variety products. Two core findings of this study include: First, the achievable product variability of a production process depends on the process step least robust to geometrical variation. Second, the adjustability of product design is regarded as an enabler for customization and increased automatization of the production process.

Changes during the product development process pose a high risk of unexpected high implementation effort. To reduce this risk, the impact of engineering changes have to be estimated in advance before their implementation. However, in large systems, the numerous interdependencies often create challenges to the approximation of the implementation effort by inducing complex interdependencies which are often not easy to identify from the start. This paper therefore proposes a method to quickly determine change efforts of complex systems. To be able to estimate change efforts, the dependencies between system components have to be mapped together with their corresponding modeling effort (e.g. modeling time). The SysML (System Modeling Language) system model is a powerful tool to support this process as it offers the possibility of a very individual and versatile data storage. Furthermore, specific data (e.g. calculation time) can quickly be identified with algorithm such as “metachain navigation”. We propose in this paper to use a SysML system model for an advanced documentation of the product and construction structure to be carried out in parallel to the modeling process of a system simulation model (in this case finite-element- and multi-body-simulation). Assemblies and associated components are represented by the product structure, while the construction structure represents the force flow between two components. Furthermore, in SysML individual data, e.g. modeling time and calculation time, is stored in an instance that is connected to a specific component or assembly. As a demonstration example, the approach is shown on a structural dynamic analysis of a powertrain with seven subassemblies and over 100 components. Following the proposed method, the effort of a specific component change of the powertrain can be estimated quickly due to the stored data in the system model.

This paper introduces a method for training operators using digital technologies. More specifically the training is focused on the teleoperation and remote control of resources and sensors using a simulated environment with virtual elements, before entering the real one. The objective is to improve the training of the operators and bring them closer to Industry 4.0 technologies where robotic and other automation solutions are tackled to support production activities. In this training activity the benefits of using a simulated environment as well as VR technology are presented. More specifically, a simulation application has been created and can be executed on a PC showing a virtual environment where a mobile platform can navigate freely around, calculating its path, and use its camera for obstacle or part detection purposes. On the other side, the VR application can be executed on a smartphone device with Android OS and can be used to see the field of view of the camera attached on the robot. The aforementioned applications can be connected through TCP/IP connection and enable the operator in real time to see and navigate in the virtual world using either the VR or simulation application. Furthermore, a specific scenario has been created and elaborated in the paper, where the robot needs to navigate inside a virtual infrastructure and perform inspection activities using its camera. Lastly, the benefits of each application are further detailed in the paper, focusing mainly on the validation of the inspection automated solution and its remote control in an intuitive and immersive way.

This paper summarizes the work done in the context of an industrial research project in collaboration between the Italian Institute of Technology and the company Fameccanica Data Spa. This paper introduces the design of a six bar linkage mechanism, actuated by a single motor, for re-orienting and aligning objects. The main application consists of grasping empty bottles lying flat on an input conveyor, rotating, aligning and placing them in upright position on another conveyor. The breakthrough aspect of the general and scalable design introduced lays in a system of kinematic equations whose solutions correspond to the linkage lengths. Such lengths define a six-bar-linkage able to re-orient and align vertically a generic object. Finally, this paper presents the mechanism prototype created for such industrial application for performing tests in an operational environment at high speed.

The era we live in today is full of uncertainty and varying customer demand. Firms are managing various difficulties including consistent change, shorter item life cycles, assorted client necessities and expanded vulnerability of interest. The purpose of this paper is to put forth the dimensions along which a firm can improve its performance and enhance its sustainability through supply chain agility. Supply chain agility is a key component of all successful firms today, be it in the services or in the manufacturing sector. It is believed that an agile company would tend to outshine its competitors, who have not adopted the agile principles. Seven dimensions of supply chain agility have been identified in this paper; and their effect on each other and on supply chain agility is assessed using Fuzzy DEMATEL. Four major factors- accessibility, alertness, flexibility and swiftness are identified as the key determinants of agility. It is found that the operational flexibility and analytical capabilities of a firm play the most crucial role in determining its agility as they had the maximum effect on the overall system. This study provides the management of companies with a clear guide map as to how they can improve their agility in today’s highly competitive market and how one dimension of supply chain agility has an impact on another dimension. Identification of dimensions of agility and evaluating their impact on each other as well as on supply chain agility using Fuzzy DEMATEL is a new and unique contribution to the concept of agility.

In product development, early decisions made within the requirements specification and design phase have significant impact on overall functionality, quality and lifecycle cost of the product. In Systems Engineering, these oftentimes iterative design phases are steered by underlying development models, providing processes, guidelines and best practices. One family of guidelines are the “Design for X” (DfX) tools or principles. In the development of mechatronic systems e.g. production systems, traditionally the mechanical, electric and software domain are to be distinguished. Over time, driven by trends of digitalization, communication and interconnection of systems, the product innovation and thus the required development effort has been shifting from the mechanical domain towards the software domain. The question of system interoperability thereby is of a fundamental nature. This is especially relevant within the current paradigms of Industry 4.0 and the Digital Twin. To reflect the growing importance of interoperability (i.e. the ability of systems to purposefully interchange interpretable information) as a requirement for the product within the design phase of technical systems, this paper introduces “Design for Interoperability” (DfIOp) as a new DfX principle. First, this is motivated by the ever-growing importance of information technology for current and future products. Then, the main contribution of the paper is the combination of the state of the art in the research domains of DfX, interoperability, Systems Engineering and Industry 4.0. Consequently, DfIOp will be presented, including a systematization model as well as a first method for its implementation. Further contemplation on DfIOp shows, that it enables derivative principles such as “Design for Data Analytics” and “Design for Digital Twin”. In future research, the underlying models as well as methods of DfIOp are to be further detailed and integrated into the research context.

The efficient execution of process planning activities requires the knowledge from several distinct domains. However, in this context, a common issue is the existence of collective knowledge in the one domain with a lack of networked expertise with other domains. Motivated by this, the paper proposes an approach to support process planning by comparing the generated part design with older, validated products. The utilization of this earlier CAD part models has the potential to reduce development costs, shorten the production start-up time and improve the product quality. The new concept provides the manufacturing personnel with a method for comparing the newly designed part with a pool of validated models to identify the most similar one. Each previous CAD part model is linked with the necessary manufacturing information, so that the initial values for the manufacturing process are available without a time consuming testing phase. The method is divided in three main steps: the global similarity comparison, the segmentation of the part and the local similarity comparison. In the first step, the geometry is projected onto a sphere and then transformed to a matrix. Afterwards these matrices are compared and clustered into corresponding groups. In the following step, a Machine Learning algorithm segments the objects into specific, manufacturing relevant groups. In every cluster, the segmented geometries are again compared for similarity. The combination of the first and the second ranking results in a global similarity hierarchy for the newly designed part. In this paper, the entire procedure is shown with the example of sheet-bulk metal formed parts. This new manufacturing process particularly benefits from this method, as the amount of data is still limited and therefore little expert knowledge exists.

Product variety is a growing trend of offering highly configurable products at the cost of inducing complexity in manufacturing. Joining is a key manufacturing process and historically was a paper-based process with incomplete variety documentation. Nowadays, digital joining element design is a substitution of paper for 3D space. Nonetheless, it remains an ambiguous manual task with limited automation, resulting in time-consuming iterative error-prone development trajectories and costly reworks. This contribution addresses the state of the art in joining element design in both research and industry practice. It reviews product variety and its impact on joining processes. The paper identifies a need for integrating product variety into joining element design and it proposes a solution pathway using artificial intelligence methods.

Social Product Development (SPD) is represented by tenants including crowdsourcing, open innovation, cloud-based design and manufacture (CBDM) and mass collaboration that either individually or in concert contribute to the democratization of design, manufacture and innovation. Although widely and very successfully used in thousands of documented case studies [1], these tenants have not yet fully arrived within the domain of professional and commercial industrial product development. Amongst the reasons for this are a lack of clear definitions as to what these tenants are and clear guidelines or procedures that outline how they can be used to aid the various phases of the product development process. In this paper, the authors investigate how success for each of the tenants or any combination thereof can be influenced. The tenants of Social Product Development can be mapped according to three factors; proximity to other participants (Pa), proximity to leading organization (Po) and the number of participants (N). In this paper, the authors hypothesize that these three variables are related to the success of SPD tenants. An analysis study is then conducted with expert researchers to test this hypothesis and determine whether these variables are influential on SPD success. The expert researchers determined that only one relationship, between open innovation success and organizational proximity existed, therefore rendering all other relationships non-existent and disproving the hypothesis. Results and limitations of the study are discussed before aims for further research are highlighted. These include clarifying definitions of success for Social Product Development, providing success factors for the tenants and supporting practitioners in applying SPD tenants.

Ultrashort-pulsed lasers allow a precise, low thermal damage and material independent micro processing. However the full potential of ultrashort pulsed lasers with their currently available laser power cannot be used due to limitations in ablation rate per pulse keeping the high quality in ablation. Beside scaling up by multi spot technology, the application of ultrashort pulse bursts is another possibility to increase the ablation rate. In this paper a newly developed ultrashort pulsed laser with a seeder frequency of 500 MHz has been investigated in cylinder micro engraving application. The laser offers the possibility to control the shape of a burst sequence in amplitude and timing. The influence of the pulse sequence on the ablation rate and quality in combination with the different metals has been investigated. The pulse burst frequency has been changed between 1 - 5 MHz with a scanning speed of 16 m/s. The pulse to pulse overlap at the work piece was adjusted from 0% to 90%. Comparing to single pulse ablation the ablation rate with GHz pulse burst is increased by factor 2.2. The surface roughness is Ra = 0.38µm with GHz pulse burst, whereas the roughness is Ra = 1.423 µm with single pulse. In this paper experimental ablation results and quality aspects will be presented and discussed.

Science-Based-Targets has been initiated to allocate ‘carbon-budget’ to industrial sectors to stay within planetary boundaries and support industries to move towards absolute sustainability. Clean and/or renewable energy are fundamentals for achieving Science-Based-Targets. Accordingly, development/usage of ‘Photo-Voltaic-solar-panels’ (PVs) have increased significantly. However, PVs’ development requires considerable resources up-front (material needed and energy-intensive manufacturing/production processes with hazardous emissions/impacts). This paper studies imposed Environmental-Impact of PVs while considering projected/future power demand. This paper investigates if PV-industry can supply needed ‘sustainable energy’ for meeting Science-Based-Targets with current technology. Results/findings are used to define needed technological improvement and/or to help developing policies towards realizing Science-Based-Targets.

With digital transformation changing industrial labor drastically, the aim of this paper is to conceptualize a new methodical framework for the production system of the future. For this, mega trends for the industrial work of the future are considered. Existing concepts and tools for production systems are analyzed with regard to the identified challenges of digitalization and future work. The result of this paper is an academically relevant and practically applicable approach for the development of a framework for the digital transformation of the production system. The approach consists of a development process and eleven design principles. The theoretical developments are accompanied by practical applications within the environment of the “Future Work Lab”, an innovation lab for technical, social and organizational changes in industrial labor.

This comparative environmental LCA analyzes the impact of various note-taking methods: a paper notebook, an Apple iPad, and a reMarkable tablet. Inventory data was predominantly populated from databases and prior studies. The TRACI methodology was adopted while ReCiPe methodology provided supporting verification. Results showed that the paper notebook outperformed the tablets in most impact categories. Tablets produced more health-related impacts and ecotoxicity while the notebook produced more land-related impacts and ozone depletion. The environmental impacts of the paper notebook were sensitive to page quantity while both tablets were sensitive to circuit board mass, usage allocation, and electricity emission factors.

This paper unravels critical and imminent issues in co-innovation of bioplastic packaging. The current state of bioplastic packaging signifies the challenges caused by the mismatch of the characteristics of bioplastic packaging and the products they are intended for, and confirms the needs for a supplier-customer co-innovation. Although co-innovation, to a large extent, demonstrates immense potentials in promoting product improvement and environmental benefits, co-innovation in bioplastic packaging has not been extensively studied. This paper offers the processes and mechanisms of co-innovation in the form of a conceptual framework, detailing the joint activities and joint resources that address the specific bioplastic packaging characteristics.

With the introduction of Health 4.0 we face a new era in healthcare and notice the disruption of delivery, adoption and use through newly introduced technology. Wearables have become increasingly important in the medical sector and their remote application and widespread use are significant to the development of technology in healthcare today and the future. The implementation of wearables requires regulations and clinical approval when intended to use for health tracking and monitoring. Within this process designers play a crucial role. Design methodologies are the guideline to accomplish a successful path towards the creation of a new product. In this paper the authors explore and draw from established design methodologies to support the creation of a framework for design of wearables in a health 4.0 context. Identifying the positions of design practices and analyzing the correlations in the context of Health 4.0 is therefore presented within this paper.

This paper shows extensive employment of animations as a response to the needs of the GPS implementation facilitation. The general architecture of the developed application that helps to understand how the geometrical tolerances shall be applied and what are their functional aims is shortly presented. This paper is focused on the line profile and the surface profile tolerances that are more precisely defined due to set of rules given in recently published standard ISO 1660:2017. The paper contains a few screenshots of the interactive animated sequences that demonstrate two alternative possibilities of bended round bar tolerancing as well as shows appropriate verification methods. The experience gathered during university lectures and a number of vocational trainings confirms the effectiveness of the proposed solution.

This paper provides an approach for modeling a customer’s perception of product surfaces using a multisensory measuring system, which fuses multiple sensor data. The multisensory measuring system objectifies customers’ perception by fusing haptic, optical and acoustic sensors according to the human stimulus processing chain. A model for customers’ perceived quality is a part of the customer’s digital shadow, which introduces human factors to product development. This paper shows the development of the multisensory measuring system, the data acquisition and the fusion of the different data formats in order to create a major part of the digital shadow of the customer.

The challenge is to identify cost-efficient measures for decreasing the energy demand at a reasonable effort in existing production sites. Metal cutting machine tools should be an integral part of investigations in the course of energy efficiency optimizations. This paper expands existing approaches for the rapid identification of energy efficiency potentials. Since most of the energy demand of machine tools is emitted as waste heat, the paper also focuses on how this influences central cooling systems. In addition, the authors have measured, analyzed and evaluated several metal cutting machine tools in a case study and planned corresponding retrofits.

Growing Electric Vehicle (EV) markets and increasing focus on Circular Economy (CE) strategies have stimulated research in the modelling of EV battery repurposing as a special case of product re-use in Life Cycle Assessment (LCA). This paper reviews the methods suggested in literature. Based on the results, the study proposes three types of LCA approaches on EV battery repurposing, taking the perspective of an automotive manufacturer in a CE context. Furthermore, the paper suggests a classification of EV battery repurposing cases depending on the chosen reference scenario and derives directions for future research.

Tolerance and fit (T&F) are crucial as it affects the performance of mechanical system. Fitting condition between shaft and bearing is the most typical one that affects the pressure distribution between the them that, in turn, determines its contact stiffness. This paper is aimed to explore a method to analyze the statistical distribution of the contact pressure and stiffness between the shaft and the bearing based on the size distribution of the T&F specification. We analyzed the pressure distribution corresponding to the T&F specifications of the shaft and the bearing by the probability density function and convolution. Contact pressures and stiffness of the mating surface are analyzed according to the amount of interference. A roller bearing with diameter 50 mm is taken as an example to illustrate this analysis. The contact pressure and stiffness are analyzed under a transition fitting condition o50H6/k5, as recommended by many bearing makers and design handbooks. The results showed that there is a high possibility to become clearance fit in this case and the expectation is a gap of 0.5μm, which implies loss of the contact stiffness thus far from the original design intention. Two approaches, either using tighter tolerance of bearing inner ring or higher dimensional deviation of the shaft diameter, are further investigated to overcome this problem. The results showed that probability and expectation of contact stiffness increase by employing either approach. The main contribution of this paper is to develop a methodology for analyzing statistical distribution of contact stiffness between the shaft-bearing assembly based on the T&F specification. It also shows tighter tolerance for the bearing inner ring or higher dimensional deviation of the shaft diameter will improve contact stiffness, if the T&F specification does not meet stiffness requirement.

Laser welding has been increasingly adopted into the automotive sector due to its competitive processing speed, less restrictive single-sided access requirements and improved process flexibility. These benefits notwithstanding, laser welding still remains susceptible to the weld cracking especially for 6xxx high strength aluminium extrusions, which are widely used in the automotive industry for body-in-white and battery tray manufacturing. This paper reviews current challenges and opportunities for construction of battery tray using aluminium alloys with laser welding process. It aims to provide a view on the selection of welding equipment in terms of beam oscillation, power modulation, beam shaping, filler wire and shielding gas, and analyze their impact on joint integrity for 6xxx grades aluminium extrusions. The driving idea is to control the thermal history in and around the molten pool and to modify the chemical composition in the fusion zone to reduce the formation of solidification cracks. Results of the study have shown that the modification of chemical composition by the use of filler wire is currently the most efficient approach to improve joint strength. Further results evidences also showed that beam shaping with adjustable ring mode laser helps to stabilize the keyhole and achieve a wider molten pool and weld interface width. Manufacturing implications are reviewed and discussed throughout the paper.

Today, industrial assembly of large equipment is often organized as site assembly. Information is provided on paper or stationary PC-terminals and employees have to walk long distances to receive information. An augmented dynamic projection system allows direct display of work-instructions and interaction on the component. The paper presents the results of an evaluation of a dynamic projection system compared to state-of-the-art information provision. The experiment is carried out using a demonstrator with a CFRP-layup site assembly for aircraft components. The results show that the augmented dynamic projection system brings significant advantages for process improvements and usability compared to PC-terminals.

Part design is the most important enabler to automate assembly processes. Design work done in companies’ development departments determines most of the product and production costs through material selection and number of components. Part design mostly focuses on functionality but there is still high potential in production processes. To optimize part design for manufacturing and assembly processes first approaches were made between 1960 and 1980. Nowadays, these systematic procedures for optimized component development with respect to production processes are known as ‘Design for Manufacturing and Assembly’ and ‘Design for Automatic Assembly’. Existing part design rules mainly focus on rigid parts. Limp components are avoided in automatic assembly processes, due to limp material characteristics such as strong deformations due to external force or momentum and shape change during manipulation. However, many products, for example automotive wire harnesses, consist of a considerable amount of limp components. A better design for limp components and their joining-partners might be a solution to simplify subsequent automated production processes. The first step therefore is limp component classification. This paper introduces a new scheme of limp component classification oriented towards an abstraction of automated assembly processes. Different limp material characteristics influence several stages of this process. Therefore, relevant limp material characteristics are assigned to the affecting stages. With this classification, the specific challenges in the automated assembly process become clear. This serves as a basis to define challenges for future automated assembly. In addition, the definition of general design rules for limp components concerning automatic assembly becomes feasible. This paper highlights challenges in limp part assembly automation, abstracts automated assembly processes, reviews and discusses first approaches in limp part classification, and introduces a new scheme for limp component classification. The paper concludes with necessary future research based on the proposed limp component classification.

The virtual reality technology (VR) has undergone a rapid development in recent years. The VR software and hardware have become powerful and affordable. Therefore, the VR counts to the key technologies for enabling the digital transformation. However, this significant technology has not achieved the expected breakthrough in the industrial engineering area yet. The potential of the VR to digitize and to improve the engineering process of industrial companies especially for SMEs is still undiscovered. This has led to a poor implementation and use of the VR applications with an engineering scope compared with other business sectors e.g. gaming, entertainments. This paper presents and discusses two case studies achieved within cooperation project between the University of Applied Sciences Karlsruhe and German manufacturers for fire trucks and systems for firefighting and disaster protection. The aim of the case studies is the implementation of VR applications and their evaluation in terms of using in the business processes of the involved industrial partner. Based on the experiences gathered during this cooperation project a CAD Data transformation process for an industrial VR application as best-practice approach will be outlined and discussed in this paper. © 2020 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license ( http://creativecommons.org/licenses/by-nc-nd/3.0/ ). Peer-review under responsibility of the scientific committee of the 27th CIRP Life Cycle Engineering (LCE) Conference.

In-situ metrology is essential for closed-loop control of machining processes to achieve zero-defect manufacturing: in this context, using inexpensive industrial cameras integrated in machine tools is a widespread solution for dimensional measurements, but has not yet been adopted for measuring surface characteristics such as roughness. This task is challenging because surface appearance can be complex and difficult to model by standard machine vision algorithms. Optical methods are preferred because they can be easily integrated in the machining process. This is useful for EDM machines, since if the measured surface roughness does not match the requirements after the process has concluded and the workpiece removed from the machine, there is no way of correcting the error: in fact, exact repositioning of workpiece and electrode, and recreating the microscopic gap conditions which are required to resume machining, is in many cases impossible. Motivated by these requirements, this paper presents a machine-integrated, inexpensive optical measurement system, measuring Ra values comparable to results originating from contact profilometers, and with the potential to deliver additional information on surface topography. In fact, characterizing surface roughness just by the Ra value is often insufficient in practice: different surface morphologies, can have the same Ra value but different topographies, which may lead to different optical, haptic or functional properties of the surfaces. The proposed approach relies on a Convolutional Neural Network (CNN) that given as input a small square picture representing a small portion of the surface, returns the Ra value of that part of the surface. The CNN is first trained using a collection of many training instances, where each training instance is a pair composed by one input patch and the corresponding expected output, i.e. the true Ra value of the surface visible in the patch. Once trained, the CNN is deployed in the EDM machine and used to predict the Ra for new surfaces that were not part of the training. The paper reports extensive qualitative and quantitative experimental results for a range of different roughness values (0.2

Since 2016, the “Digital in NRW” Competence Centre has been supporting SMEs in the manufacturing industry in designing their individual digital transformation. With an Industry 4.0 maturity assessment, we define the status quo of SMEs, derive SME-specific measures from this, develop a digitalization roadmap and accompany the SME transformation. This paper presents the results of the four-year SME support. By analyzing the results of all maturity assessments, potential analysis and design workshops, we present the most frequent and most effective measures for a successful digital transformation of SMEs. The result of the paper is an action guideline for SMEs to initiate their own digital transformation based on formalized experience.

The manufacturing industry faces immense challenges for maintaining and increasing their productivity and flexibility. In this context, it is important for companies to ensure that their employees have the relevant data, information and knowledge necessary to make well-informed decisions. Due to recent development with Industry 4.0 enabling technologies that create new possibilities, the amount of available data, information and knowledge increase rapidly, but the insights into how to utilize it to its full potential are still lacking. In this paper, a human-centred perspective has been applied, aiming at improving how to cognitively support humans at work with new Industry 4.0 enabling technologies. Heavy emphasis is placed on people’s requirements and preferences of data, information and knowledge for enhancing their performance and satisfaction at work. This paper examines the relationship between existing literature on dissemination of data, information and knowledge within the manufacturing industry with state-of-the-art research on Industry 4.0. The outcome of the research recognizes the increased importance of utilizing data, information and knowledge for people at work, facilitated by exploiting the new possibilities from Industry 4.0. To accomplish this, it is concluded that there exists an urgency to design: both a holistic framework for identifying and accommodating individuals’ needs and expectations of relevant data, information and knowledge; and demonstrators and concepts to simplify the implementation of Industry 4.0 enabling technologies that support the aforementioned dissemination of data, information and knowledge.

This paper discusses the performance measurement of an envisaged flexible production station, where automation devices, updateable tools and robotized resources have been introduced, that has been implemented in laboratory environment. The production station has been inspired by an existing one from the consumer goods industry, where hard automation solutions have been selected to achieve high productivity times. The aim of this paper is to prove how the combination of the aforementioned technologies can improve the reconfigurability and adaptability levels of an assembly station. In order to achieve this, a number of time values has been extracted from the real demonstrator, such as manipulation time, recognition time and transportation time of parts. Additionally, production information such as the number of failures after a successful number of assemblies has been measured as well. The design of the proposed system as well as the above information have been added in simulation environment and more specifically in the Witness Horizon Simulation tool in order to simulate its function using discrete event simulation approach so that the response of the suggested system is clear. After the simulation process, conclusions have been reached for the long-term behavior of the suggested flexible station, highlighting its advantages in terms of flexibility and adaptability, compared with the existing, hard-automated station that has already been installed in a consumer goods industrial production line.

Resources are closely linked to the environment. Each year, the motor consumes 60% of the industrial power resources. The manufacture and operation of the motor have a great negative impact on resources and the environment. Therefore, it has become a research hotspot to alleviate this negative impact in the motor product service system and to improve the sustainable production capacity of the motor product. Based on this, this paper analyzes the impact on the resource environment from the micro and macro perspectives of motor product manufacturing, and reduces resource cost, environmental pollution treatment cost and economic benefit cost in the field of motor product production and inventory control system. On the micro level, this paper studies the process technology of manufacturing, testing, evaluating, maintaining and re-manufacturing the mechanical structure of electrical machinery to improve the efficiency of the motor and the sustainable production capacity of the product itself. On the macro level, the article studies the analysis of the motor by the big data method. The quality of the related factors, optimize the upgrade of the inventory management system, improve the forward quality of the motor products and the backward quality consistency, and combine the products and services into a product service system, saving resources and costs.It enhances the sustainable development of product service systems in the field of motor control.

Product development (PD) faces uncertainties caused by rapidly developing technologies, shifting market demands and the changes occasioned by these developments, including new requirements for products being developed and increased difficulty for companies to reliably execute state-of-the-art processes. In this paper, we argue that classic PD risk management methods and tools are based on the ‘predict and plan’ paradigm, which assumes that organisation members involved in PD have sufficient time and resources to identify, analyse and mitigating technology risks and organisational risks. However, the reality of product development is that time and resources are rarely sufficient, and uncertainty is currently being introduced to the development process at an accelerated rate by trends such as pervasive digitalisation. This paper therefore investigates a resilience-inspired approach to PD risk management that abandons the predict and plan paradigm in favour of a ‘monitor and adapt’ approach. We argue that, in industrial practice, predict and plan approach is the de facto risk management baseline, and suggest deliberately tailoring risk management and PD processes to incorporate resilience-based practices. To that end, we provide suggestions for process frameworks and tools organisations may adopt and discuss how resilience and risk management are complementary approaches to traditional PD. Our arguments are supported by a case study of an engineering organisation and additional interviews conducted with members of similar organisations for purposes of validation. © 2019 The Author(s). Published by Elsevier B.V.

This work aims at developing an original numerical model to predict residual stresses induced by a grooving operation. This model is inspired by the hybrid approach consisting in modeling the thermomechanical loadings applied on machined surfaces. The paper will start to present the experimental investigations which have enabled to define the shape and intensity of the thermomechanical loadings during a tool plunging operation. The second part of this paper relates to the modeling strategy. Finally experimental residual stress profiles have been made by means of the X-Ray diffraction on 15-5PH samples and compared with numerical results.

Electric vehicles are seen as a solution towards mitigating the environmental impacts of the transportation sector as they do not produce tailpipe emissions. However, this technological shift in the transportation sector brings new environmental challenges. Particularly, the production of the battery system has been estimated to potentially contribute to around 50% of the total cradle to gate environmental impact of the production of an electric vehicle. In this regard, the energy required for the manufacturing of battery cells has been identified as one of the largest environmental and economic hotspots. While the emerging battery manufacturing industry is still highly unconstrained, the variability of product and processing parameters is difficult to capture and compare. Even more, battery cell manufacturing consists of a complex and dynamic combination of numerous continuous and discrete processes as well as technical building services which account for a high share on energy demand. This has led to a large uncertainty in the values reported in the current literature and also hinders the derivation of improvement measures. Against this background, the paper presents a simulation-based assessment of the energy demand in battery cell manufacturing. Based on collected field data, a multi-paradigm simulation of the battery manufacturing process chain has been built. Multiple simulations scenarios were subsequently run allowing to analyze the factors and circumstances driving energy demand. This paper aims to contribute towards enhancing the knowledge on the energy related impacts of traction battery systems and to provide a frame of reference for the variability of the required manufacturing energy that might be used as input 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).

Industry 4.0 and the Smart Manufacturing paradigm are rapidly transforming the way business is conducted in the manufacturing industry. The underlying core technologies of the emerging digital transformation include the Internet of Things (IoT) and data analytics. While the shop floor is the main focus of these paradigms in the narrow sense, it is becoming more prevalent for other companies or business units along the digital supply network to investigate the potential to improve their processes by leveraging these technologies as well. One area that is traditionally reliant on human operators and optimization is the trucking industry. In recent years, this industry has started to explore how the above-mentioned technologies can be utilized to improve their operations, and new policies are further amplifying this process, e.g., by mandating Electronic Logging Device (ELD) to be used by commercial drivers in the USA. This development forces companies to collect digital, high-resolution data – and the question emerges how to utilize this data in the most value-adding way. In this paper, we analyze the current state of the art of digital transformation in the trucking industry by conducting a bibliometric study of recently published papers to identify the main research areas and themes in this field, the leading authors, countries, and journals in this domain. This work will provide the basis for further in-depth studies on optimization techniques and available case studies to provide theoretical input for practitioners and scholars.

Digital twins offer great opportunities in various domains of the product engineering process. However, current approaches to the use of digital twins only focus on different separated disciplines. In contrast to that, it is expected that the holistic use of digital twin models in product development and production will dominate future product generations, because they allow to create high-performance products competitively. This paper explores important challenges and future potentials of digital twins and Industry 4.0 for the seamless integration of product specification and production. In this regard, approaches of linking digital twins to other domains open up new possibilities in tolerance allocation and production integration. Thereby, the most efficient product specifications in technical and economic terms are achieved for the manufacturer. In addition, the connectivity of Industry 4.0 broadens the scope and enables the evaluation of alternative approaches in production planning and control. Approaches at the organizational level, product functions with specifications beyond the technological limits and production control strategies (e.g. order dispatching) ensure high performance operations. Simulations with a digital production twin with integrated digital product twin allow early estimations even before the actual ramp-up of the product. The future challenge addressed in this paper is to define a consistent framework for the holistic use of digital twins in the entire product development process, which requires the integration of product designers and production planner concepts.

Current developments in production systems and production control will lead to a significant change of their connection to product development. A representative example of these developments is the concept of self-organizing production systems. This paper focuses on appropriate changes of the development process evoked through recent developments in production. The paper summarizes process changes and opportunities for the development process, which include the vision of self-organizing production systems, its challenges and opportunities. Consequences are estimated for established development methodologies like the V-model and aspects of Systems Engineering.

The demand for customised products leads to an increasing diversity of variants and batch sizes in production. Therefore, established high volume production technologies have become less profitable. This loss results primarily from high product-specific investments in mould-based manufacturing systems such as injection moulding and sheet metal forming whose amortisation is challenged by smaller batch sizes. The increasing number of components in multi-material design, especially from metal and plastics, calls for flexible production technologies. This paper introduces a new robot-based process chain for metal-plastic parts to close the existing gap between manual production of small batches and mould-based high volume production. The idea is to combine surface treatments with additive and subtractive manufacturing processes in a single workspace implementing an incremental process chain to realise a scalable production system. This will increase the manufacturing flexibility for customised products and reduce the cost per unit. As a first step in the process chain, the surface of a pre-produced metal part is structured for mechanical interlocking and high strength joining. Subsequently, an additive process is used to directly apply a plastic section onto the pre-structured metal part, thus producing a hybrid component. Finally, the hybrid component is machined to add individual holes or pockets and to finish the surface of the additively manufactured area. This paper presents a scalable process chain for metal-plastic components as well as concepts for its technological implementation. Furthermore, first experimental results from the fabrication of a metal-plastic part using additive and conventional processes are presented.

With the availability of low cost, markerless motion capture systems, in manufacturing industry more and more companies apply this technology for ergonomics assessments without knowing the overall performance and limitations in advance. This paper presents an applicability evaluation of Kinect sensor’s motion capture performance to be used for ergonomics assessments. Even though literature has already presented application papers in manufacturing industry and ergonomic analysis, few papers are giving practical insights on the overall limitations. In particular, the European Assessment Work Sheet (EAWS) is applied as a reference, standardized method. EAWS working postures are systematically carried out in virtual domain and physical domain and are cross-checked with respect the results in EAWS. The results show, that 9 out of 11 working postures can be tracked well and tracking limitations are revealed. For several working postures, passive haptic feedback is required. Therefore, visually low occluding physical mock-ups are proposed.

Fibre reinforced plastics (FRP) are considered as a modern high-tech material. The enormous lightweight potential, especially of carbon fibre reinforced plastics, accelerates its spreading in many industrial applications. However, as good as the lightweight potential of FRP is, as difficult is the optimal use in products because of its anisotropic material properties. Recently, methods considering the anisotropic material behavior during the product development process have been developed. These result in optimized nominal laminate layouts with defined laminate parameters like fibre orientation, ply thickness and ply stack-up order. Yet, during the manufacturing process, variations of these parameters, compared to the nominal design, occur. The variations result in asymmetric laminates, which causes couplings in the stiffness matrix and consequently undesired deformations. The effects of varying laminate design parameters have so far mainly been investigated with focus on manufacturing activities. However, to support the design engineer in considering the influence of varying laminate parameters on product behavior, this paper studies the effects on the structural behavior of optimized FRP-parts. The main novelty and contribution of the paper can be found in providing a new approach for the consideration of uncertainties in the ply thickness on FRP parts with optimized fibre layout. For this purpose, sensitivity analyses are performed with the aim of investigating the importance of the parameters on the structural behavior. With this knowledge, the product developer is able to distinguish between critical and non-critical laminate parameters and consider their variations in the product development process as early as possible.

International audience; This paper presents a novel scan-based method for the real-time monitoring of additive manufacturing processes. Most traditional scanning techniques used for generating 3D models capture the only outer shape of the object after completion of the printing process. The method proposed in this paper differs as it relies on a layer-by-layer scanning of the 3D object directly during the printing process. This strategy has been successfully implemented with a fused filament 3D printer (PRUSA i3 MK3). Furthermore, in order to offer an increased interaction between the obtained 3D model and the user, a virtual environment has been developed for the augmented reality glasses HoloLens. The novelty of this method lies in the layer-by-layer 3D model reconstruction of both the outer shape and the inner layers of the printed part. It enables the user, directly during the printing process, to view and detect potential defects, not only at the surface but also in the inner layers of the printed object. Therefore, it can provide detailed information about the build quality and can be used as the basis of a decision-making tool.