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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.

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

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.

In this paper a new process to manufacture unidirectional reinforcements for eco-compos ite materials, made of natural fibers, is presente d. Starting with flax rovings of different sizes, an apparatus was developed to feed and align the rovings over the wet-end section of a paper machine. The short kraft paper fibers are therefore mixed with the long flax roving as the machine is running, and at the end of the process, a sheet of the hybrid dry reinforcement is obtained and cut to size for impregnation with various resins, using different processes. This novel manufacturing process allows for high volume production of reinforcement. It is very flexible, and many different combinations of long and short fibers can be exploited for the production of a vast variety of dry reinforcements. In this paper, composite samples are obtained out of these reinforcements, using the resin infusion (RI) molding process with a commercial epoxy resin. The results are compared with those of usual glass fiber reinforcement. An interesting aspect is that the large variability, typical for natural fibers, is largely reduced when the short kraft fibers are present in the composite. In terms of permeability to resin, reasonably comparable values can be obtained compared to that of glass fabrics, if a low surface density of reinforcement is chosen. © 2014 The Authors. Published by Elsevier B.V. Selection and peer-review under responsibility of the International Scientific Committee of “The 47th CIRP Conference on Manufacturing Systems” in the person of the Conference Chair Professor Hoda ElMaraghy.

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

The railway industry is moving towards a complex socio-technological system that relies on computer control and human-machine interfaces. Digitization and convergence of information technology (IT) with operational technology (OT) becomes increasingly important. Therefore, enabling innovations, lowering costs, increasing safety systems, enhancing performance, and increasing flexibility gains. In this scenario, opportunities arise for Augmented Reality (AR) capabilities to enhance maintenance operations of digitized trains. Initial research and tests have enabled training technicians and assistance of train drivers during specific activities by facilitating interactions with machines that go beyond traditional training manuals. Despite the positive results AR has shown, there are no specific indications available regarding the 'deployability' of the technology related to maintenance tasks. This position paper presents arguable boundaries and challenges for a structural appraisal of AR on maintenance operations of IT/OT converged trains. Potential AR solutions and specifications were based on current maintenance operations. Semi-structured interviews and surveys were conducted with maintenance providers to determine existing challenges. Moreover, the paper comprises an analysis to reveal the potential application AR has in maintenance procedures of digitized trains. Finally, this paper provides future research directions of AR technologies related to maintenance procedures of trains.

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.

Carbon footprint was calculated for cassava starch product in Thailand. Objective of this study was to evaluate and compare the global warming impacts of cassava starch production in 3 factories located in North-Eastern Thailand. The system boundary was defined from cradle to factory gate, starting from cassava cultivation and harvesting until cassava starch production by using life cycle assessment (LCA) methodology based on ISO 14040 series. The selected functional unit was 1,000 kg of dry native starch, excluding packaging. Input and output data were analyzed and results were shown in terms of global warming potential (GWP) impact categories. The average greenhouse gases (GHGs) emission value based on LCA methodology of 3 factories was 594 kgCO2e per one ton of dry native starch. The results indicated that the cultivation and harvesting procedure was the major source emitting GHGs, around 40-59% of the total, if the factory used the biogas from wastewater treatment plant. Based on this result, the estimated total GHGs emission of cassava starch production in Thailand in the year 2013 was a total of approximately 10,500 million tons of CO2e.

Product Life-Cycle Management (PLM) defines the integration of different kinds of activities, from a technical, organizational and managerial point of view. These activities are performed by engineering staff throughout the entire life cycle of industrial products. To improve PLM, the information which is gained within a specific PL phase should be made available and used along the entire life cycle. This position paper describes how the No-fault-found (NFF) problem can benefit from transferring information from BOL into MOL. The application field of avionics is considered, in particular the generated information in the test process of BOL and the relevance of NFF. For this purpose, the article presents an approach by ARINC 672 for reducing NFF phenomenon. On basis of this approach the authors propose an extension to insert the information of test process’ results proactive into the ARINC 672 approach. The extension has a visionary nature and does not consider technical background of the BOL and MOL of aircrafts. Finally the position paper motivates the next necessary steps to bring the proposed extension to its applicability.

One of the major trends in the 21st century is the trend towards digital transformation within Industry 4.0. In order to adapt to high volatile market conditions, especially in the context of COVID-19, companies do not only have to transfer their knowledge into digital systems. Furthermore, they have to be able to tailor their business processes rapidly to changing customer's needs. The implementation of efficient and transparent processes is thereby known as the process of digital transformation and requires its underlying software-architecture to be highly flexible and scalable. Especially a web-based platform represents a highly user-centered application for multi-user purposes and can quickly adapt to its customer needs. While e-commerce and video-streaming platforms, like eBay or Netflix, often already provide a highly flexible, microservice-based architecture, common tools and platforms within the engineering-domain are usually still based on rather monolithic or service-oriented architectures (SOA). Therefore, an adaption to changing market conditions and the integration of proprietary tools is often technically demanding and economically not feasible, especially for small and medium-sized enterprises (SME). In this context, the so-called Process-Driven-Approach (PDA) offers a sustainable and tool-neutral blue-print for a system's architecture strictly following the separations-of-concerns principle and allowing a user-centered layout. The PDA enables the integration of business processes and tools within an orchestrating framework, provided by a process engine. While the PDA within the engineering domain has been presented in a preceding paper, this paper supposes a reference architecture and agile development method for engineering-platforms via the PDA based on the BPMN-standard and process engines. These concepts are validated by a prototypical implementation of a platform-demonstrator for tool-supported planning of robot-based automation solutions. © 2021 The Authors. Published by Elsevier B.V.

This paper investigates the application of modern industrial internet protocols and network architecture in manufacturing companies, from the perspectives of productivity and sustainability, framed in the fourth industrial revolution paradigm. This is achieved by delving into the existing information systems and devices, their inter-operability and interconnections using industrial internet of things protocols. The paper details a study generating a standard model of data architecture to better unify the different layers of the information systems that typify most manufacturing companies, leveraging their existing digital infrastructure to establish a solid base for further digitalization.

Visual inspection-based quality control systems are deployed to meet the growing demand of high-quality products. In this paper, an inexpensive RFID (Radio-Frequency Identification) technology and a machine vision system have been integrated within an existing learning factory. RFID technology is used to trace a product/part from its origin, enabling the visibility of the product/part’s entire movement in the value chain. The goals of the paper are to track the workpieces in real time (i) to provide immediate feedback, through visibility, to operators and floor managers and (ii) to develop a database to trace parts, in future, backward through value chain. The system can be used to predict the probability of defective parts in the value chain using machine learning algorithms. This will help the manufacturers to trace the parts in value chain at any point of time including aftersales. Traceability has been a pain point for manufacturers during product recalls.

Modern version control strategies are highly capable at supporting the management of virtual artefacts. The process of developing a new product, however, is not limited to virtual artefacts. Today’s fast-paced industrial processes require a diverse range of both virtual and physical artefacts to explore, refine, and evaluate designs. These virtual and physical artefacts are interrelated, and the information they embody, the knowledge they generate, and the transfer of learning between are fundamental to the design history. Consequently, there is a requirement to capture and curate both virtual and physical artefacts, iterations thereof, and the interrelationships between. The Digital Twin paradigm couples physical and virtual artefacts throughout the product life-cycle, providing a means to capture an evolving design irrespective of the medium in which the designer is working. Recent literature has, however, raised questions about the concept of a Digital Twin early in the product life-cycle when the design in question is conceptual (a cognitive model) rather than physical or virtual. This paper reflects on the challenges of implementing Digital Twin-based version control in the early-stage of new product development, moving towards integrated version control of virtual, physical and cognitive models/artefacts. Firstly, by presenting an argument that current design practices capture cognitive models through stakeholder creation and evaluation of physical and virtual boundary objects, the ambiguity surrounding conceptual design and the Digital Twin is addressed. Secondly, the principles of the Digital Twin and current version control strategies are reviewed to determine how one can maintain digital/physical synchronicity as a design evolves. Finally, this paper reflects on the implementation of such an approach and the proposed future work.

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.

The design of Human-Robot-Collaboration (HRC) applications faces many challenges especially in the early design phase. One important issue is the different interests of system designer and safety engineers during the design. The interest conflict leads to high coordination effort and optimization loops to generate a solution, which meets the given process requirements. This paper presents a systematic approach for the system designer to consider safety in the early design phase of HRC applications. The described approach is basis for a future software tool, which supports the system designer to generate first HRC safety concepts. Therefore, the paper presents the general architecture and explains the three main modules of the tool approach: main safety principles definitions, safety capability taxonomy and the usage of look-up tables.

With the increase in the number of avenues for production of data everywhere including logistics, using this data has become an obvious next step. In this paper, we look at the major logistics problems in details as discussed in a seminal work in highlighting the problems in logistics by X. Li in 2014. Together with discussing the problems, we also look at the trends in solving these problems using machine learning. We look at the recent work done in the particular fields using machine learning (ML) and develop the correlation of the trends of using supervised, unsupervised and reinforcement learning in solving these problems. This correlation is developed using a table and a diagram showing ML techniques and the trends in the fields of the problems are discussed. COVID-19 has greatly accelerated these trends. This paper serves as a gentle introduction to ML techniques in the field of logistics for researchers who are new to the field.

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.

In a high-mix low volume production environment, time to market is a key factor. However, one bottleneck lies in the often times manual parametrization of machines for new or modified designs. A truly flexible manufacturing environment therefore requires a continuous data flow from the design stages to the shop floor. This paper presents a concept for the automated parameterization of machines at a large automotive plant. It describes the interface requirements of the stakeholders involved as well as the overall system architecture. Finally, the paper presents first results of a prototype tested in serial production, spanning more than 100 machines.

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.

Many research studies have been done to investigate different aspects of environmental challenges. In the product development context, there have been attempts to reduce the environmental impacts of associated activities including the production processes themselves. As a result, various technological improvements have been made and new production processes e.g. powder metallurgy technologies have been introduced that may demonstrate a lower environmental footprint compared to conventional processes e.g. cutting machining technologies. However, a production process with a lower environmental impact may lead to reducing the quality/performance attributes of the produced parts (gear) and probably of final products (transmission box). Therefore, the efforts intended to produce more sustainable products by using production processes with less environmental footprints may lead to some unintended consequences such as increased maintenance requirements and/or demand. This is a rebound effect from a system of systems (SoS) perspective. This paper aims to analyze the impacts of more environmentally friendly production processes on the total environmental impact associated with a product given they may have a negative impact on a part/product's quality and performance or usage. This paper introduces a methodological approach to analyze the environmental sustainability of a production process at a SoS level. This is done by including the entire supply chain of a product-system and its interacting systems by using the results of the attributional/consequential life cycle assessment (LCA) method. The approach validity approach is demonstrated in the case of gear manufacturing in the automotive industry. (Less)

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.

Stereolithography (SLA), additive manufacturing (3D printing) technique, is widely used nowadays for rapid prototyping and manufacturing (RP & M). This technique is driven by photo-polymerisation, which is an exothermal process. This may lead to thermal stresses significantly affecting the final quality of printed parts/products. To guarantee high-quality parts printed with the SLA technique, understanding the thermal behaviour is therefore crucial for optimizing the process. In this paper, the recent physics-informed neural network (PINN) methodology was employed to improve a physics-based model for predicting the thermal behaviour of SLA processes. The accuracy of the improved thermal model is demonstrated in this paper by comparing the predicted 2D temperature field with the 2D temperature field measured by a high-speed infrared thermal camera on parts printed on a production machine.

Prototyping is an indispensable activity in product development that facilitates the generation of knowledge in the design process. It is crucial that this knowledge is the right knowledge (e.g., type, fidelity, and accessibility) to ensure stakeholders can evaluate and decision-make effectively. While this is well-recognised, prior work has focused on classifying prototyping/prototypes by their attributes rather than the knowledge they generate. As prototyping methods have a significant impact on the knowledge generated, both in the activity and resulting prototype, a means by which methods can be classified against knowledge generated would enable support of prototyping method selection, equivalence, and substitution. To this end, this paper presents the existing concept of fungibility – a context-specific value judgement as to whether two ‘objects’ are the same, and therefore capable of mutual substitution – and considers it in the context of prototyping and knowledge generation. In so doing, the paper provides a framework by which prototyping processes may be compared against their knowledge contributions to determine if they are fungible. This is then shown to support prototyping method comparison, selection and direction. After defining the fungibility framework, value is demonstrated via an illustrative example where it has been applied to the comparison of two prototyping instances. The framework is then appraised for its utility and limitations.

Manufacturing evolved substantially over the years and with it the inherent complexity increased well. Currently organizations are transitioning towards smart manufacturing and implementation of industry 4.0 paradigms. Artificial intelligence and machine learning can help organizations of any size on this journey. The physics of manufacturing processes have been extensively studied and documented as relationships and equations. Machine learning algorithms rely on quality data to enable inferences and smart actions. At the intersection of these two methods lies hybrid analytics which captures the knowledge in the defined physics-based relationships and uses data-driven methods to fill the gaps that the physics-based model may have. The goal of this paper is to identify approaches and use cases of hybrid modeling wherein the term hybrid is used to indicate the combination of a physics- or first-principle-based modeling component and a data-driven modeling component. VOSViewer, Voyant Tools, and Microsoft Excel have been used to understand the relationships between various attributes associated with the articles on hybrid modeling. The papers cited the most, the organizations/regions that funded the research in this domain, the contents of recent papers, and what categories and keywords are frequently used are discussed. This work is the basis for an extended literature review to understand in detail the algorithms, use cases, and implementation issues of hybrid analytics. This bibliometric analysis uses the state of the art references and procedures mentioned in the documentation of VOSViewer and Voyant Tools.

During the product development process, the engineer needs to design mechanical structures regarding package and design surface restrictions. Accordingly, the use of package definitions and design surfaces results in a constraining volume. Shape optimization algorithms can be applied for component or assembly optimization. By variation and setting of the upper and lower boundaries, the shape of the optimized part is restricted. The optimization result needs to stay within this design space. Hence, the engineer or designer sets the design variables in such a way, that none of the created designs reaches beyond the design variable boundaries. This manual method is not able to prevent every infeasible design, due to the vast amount of possible designs in the solution space. As well, the effort to set the design variables by hand is high for the designer. To avoid this manual and vulnerable process, the authors introduced and implemented a method for the automated detection of geometric infeasibility. The process is also named optimization under geometric infeasibility. This paper applies this method to a more complex example, which includes the detection of complex constraining geometries. It demonstrates the benefits of the method and how it can be applied in the product development process. Therefore, a generic b-pillar has been designed in SFE CONCEPT. The structural performance of this b-pillar is thought to be increased by a shape optimization. From a literature review, the dimensions of a parametric package and design surface are derived. In-between the surface definition, the algorithm creates feasible designs. Penalized designs are detected if a surface penetration of the assembly/component and the enveloping surface takes place. Hence, this method enables the optimization of structural performance with an NSGA-II, while the designs are geometrically feasible. Besides the validation of the method for population-based algorithms, this paper introduces and discusses the use of surrogate models for optimization under the geometrical constraint.

The functional model is one of the key development foundations to understand, anticipate and reflect products and technical systems, since the interplay of functions directly reflect the intended and their expected behaviors. Furthermore, with the help of functions, it is possible to implicitly describe a significant portion of the characteristics of the product under development. The development of a functional structure requires significant knowledge and experience; often engineers have a difficult task to describe the functionality of a product in a solution neutral form. During such development phases, questions arise about the possibility of exchanging functions between products, which is a nontrivial task. To explore the nature of exchanging functions, the authors conducted research about functional flows of two complex mechatronic products. Functional flows have shown the ability to establish relationships between functions of two products. The analysis reveals aspects of functional modeling in terms of the hierarchy of functions and their interrelationships. The paper considers an approach to build a common functional model, determining the structure of functional decomposition and determining the functional flow of a product. A clear decomposition and structure of product functions show the possibility of establishing links between products, in order to exchange functions. The constructed concept of the common functional model demonstrates its theoretical and practical use as a framework for building the individual functionality of a new product. This paper concludes with an outlook to future steps and directions to explore the potential for function exchanging.

A novel design for a minimum loss reluctance magnetic bearing, with only one active control loop, will be presented in this paper. On the basis of extensive numerical simulations, the suitability of the bearing concept for machine tools and other possible applications are discussed. The results of the mostly finite element method simulations are being validated and verified by means of tests on a magnetic bearing test rig. At the end of the paper an outlook on possible improvements and further developments will be given.

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.

This study presents and discusses the possibility of using 3D printed polymer tools to achieve a flexible metal forming production line. An example is given of the design of a sheet metal part from tool printing to the actual forming and evaluation of the achieved precision of the formed part. The time spent by the engineer/operator during the full process cycle is documented and compared to the time it would take to make the corresponding tool by conventional metal machining. The time savings revealed in the paper are relevant for a low number of parts to be formed in a flexible manufacturing environment, while it is less relevant in mass production where metal forming is conventionally an attractive process. Besides the time invested by the engineer/operator, it is also important to keep the costs of the tools at a minimum in flexible manufacturing. Therefore, the paper also presents a comparison of the costs for printed tools as compared to conventional tools made from metal machining. This analysis considers the cost of raw materials, machine time and electricity consumption. The environmental aspects of metal forming are also considered, with the lubricating oils and greases typically used to improve frictional conditions being replaced by the polymers, which themselves have anti-friction properties. Finally, the recyclability of the tools is significant when they are only used to form a few parts. Different types of polymers and printing strategies are discussed in relation to recyclability and compared to the alternative metal tools.

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.

In the automotive sector, panel fitting operations represent a delicate phase of the assembly process, during which car bodywork components are manually aligned attempting to comply with spacing tolerances. Digital technologies currently promoted by the Industry 4.0 approach, such as collaborative robots and augmented reality (AR), could assist workers for reducing execution times of this “trial and error” procedure. On the one hand, human-robot collaboration (HRC) can enhance assembly process efficiency, by combining human dexterity and cognitive capabilities with robot repetitiveness and support in heavy load handling; on the other, AR can provide interactive instructions to guide workers during the process. In this regard, the present paper is focused on the development of a novel assembly workstation using HRC and AR to support workers during car panel fitting operations. The system is based on the implementation of an algorithm able to convert the values on panel spacing, which are measured by laser gauge positioned on the collaborative robot, in the instructions for the worker to correctly carry out the panel fitting operation. The paper finally presents the application of the system to a real assembly example.

In this paper a framework is introduced to elicit requirements of Artificial Intelligence (AI) towards the System Model in order to support engineers in Virtual Product Development (VPD). The framework supports to shape the evolution necessary in system modelling to provide the right data in the right quality for AI. Depending on the business benefit that a company wants to realize, AI can provide capabilities and solutions to be implemented in VPD. Therefore, differing requirements need to be fulfilled for each business benefit that a company pursues. This framework is applied in a case study in the Medical Device Industry, where a marked leader wants to improve their capability to create innovations by automatically increasing their market knowledge. Therefore, a Natural Language Processing system is applied to automatically enhance the company knowledge base with an external source. This is realized in an initial prototype by analyzing Tender Documents and automatically connecting the new knowledge generated to the company internal knowledge in the system model. This paper is part of research activities within the Research and Development department of a global Medical Device Company. The Objective of these research activities is to explore the use of Artificial Intelligence to analyze and support the Virtual Development Process.

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.

This paper summarises current industrial practices and standards promoting Human Factors Engineering (HFE) at design stage and revise them with an action research approached based on the concrete case studies performed during a European project called TOSCA. The paper highlights how HFE can significantly impact the costs and risk associated with a plant lifecycle and the current gaps and issues encountered. The gaps identified are used to guide industrial practices and standards towards a more valuable inclusion of Human Factors knowledge in structured system design processes to support human performance and reduce the potential for human errors in operations and maintenance.

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.

This paper presents machining results for dry-ED milling of micro-scale contours by use of a new machine tool, including a relaxation generator, designed for dry-EDM. To implement the dry-ED milling process tungsten tube electrodes were used. The gas is injected through the tubular tool electrode under high pressure. Additionally, a high-speed EDM spindle was used to overcome process instabilities and increase the material removal rate due to better flushing conditions. Further, fluid simulations of the flushing conditions are presented in this paper. It is found that the use of gaseous dielectrics enables much better machining results in terms of shape accuracy and tool wear. The simulations provide new insights in the flushing conditions when using gaseous dielectrics. The calculated fluid flow pattern shows a great agreement with the observed depositions of reattached molten material on the tool and workpiece surface. The results presented as well as the hardware introduced enable the industrial application of dry-EDM for the first time.

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.