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In the case of product service systems (PSS) it's important that products and services are designed as a combined value carrier, rather than merely adding services as by-product or using products only as a means to provide services. To support a valuable inclusion or transition, there is a need for new and adapted tools - based on service, interaction and (user) experience design - in order to explore the opportunities provided by emerging PSS concepts. Furthermore, besides the main challenge for the designer to manage the variety of underlying design processes, early representation and communication are key for the strategic rollout of the PSS concept. This paper describes a case visualization of an early stage product service system design project in a first master year for product developers. The results bring forward iterative support with the aim to revise, adapt and add to the current PSS design tools and to better support future generation designers for the challenges that come with designing these product service systems.

The most effective ways to mitigate the diffusion of the COVID-19 pandemic are social distancing and the use of face masks as barrier to avoid droplets and to filtrate exhalations coming from infected subjects. Currently used face masks are products developed to be used by workers, both in health care and other contexts, where their use is limited in time and the disposal scenario is properly managed. Their use in a pandemic situation can be thus considered a remedial action due to the emergency. New masks or mask families are needed based on the desirable requirements retrieved by the analysis of the current worldwide situation and covering the gap observed in the market. The present paper aims to describe the complete product development process of a new facial mask (or mask family) for a daily use on a pandemic situation. It challenges the time constraint of the COVID-19 pandemic by adopting a four-step approach and concurrent development of the first phases (definition of requirements and functional derivation). The engineering design process allows to derive two different solutions able to fulfil all the requirements (demands and wishes) of final users, by assuring high ergonomic performance, as well as environmental, economic, and social sustainability.

In factory design projects, several disciplines such as manufacturing system planning must exchange design information on an ongoing basis. Due to the high dependencies among the disciplines, this information must be carefully checked. While semi-automatic methods for checking geometric information exist, the validation of semantic information is often done manually and document-based causing errors and an inefficient, laborious collaboration process. In order to create an expert system for BIM-based factory design through automatic checking of semantic information, this paper lays the corresponding foundation regarding (i) domain-specific validation rules and (ii) software implementation possibilities by using a two-round Delphi study.

Lean Production Systems are widely used in manufacturing enterprises. Through the avoidance of waste on the shop floor, efficient processes are established. Another newer concept is Industry 4.0, which is one of the most promising approaches to handle future production problems like the high demand for customized products. Many types of research address the combination of both concepts to maintain an efficient and flexible production process. This paper addresses the mentioned issue by combining both ideas and systematically identifies the unification. The aim is to develop an approach that assesses the technologies of Industry 4.0 to suitable elements of a Lean Production System.

Assembly simulations in virtual reality were used increasingly in recent years. Even though a multitude of solutions exists in the commercial market, research is focusing on improving soft factors like usability and financial accessibility. Therefore, this paper proposes a holistic solution for animation and integration of functionality of a digital manikin in Unity. The solution is using Unity's integrated inverse kinematics system and physics engine. The result is a fully animated human manikin based on the tracking data of a head-mounted display and its two controllers, which is extrapolating motion data for untracked body parts. Furthermore, the solution enables the user to manipulate objects and explore the virtual environment. The solution is validated in comparison to established motion capture methods using joint error analysis.

This paper aims to develop concepts how digital shop floor management (dSFM) can be further enhanced by natural language processing (NLP) to bring a higher value to the shop floor team and decision makers. Based on the literature review on these two fields several valuable application of NLP in dSFM are theorized: recommender engines to improve knowledge management, text clustering to identify frequent problems, voice assistants to ease the interaction with the data base, chat log extraction to fill the database with unstructured written text from chats and spellcheck as well as auto fill to improve data quality. To show the feasibility for NLP in dSFM in industry, a case study for the document clustering is presented: A digital ticket system for shop floor issues used for two years and containing 2,735 entries is analysed with the “Graph”-feature from Elasticsearch to find the most frequent terms and intersections in the described problems. The approach is accurate, quick and detailed and will be established in the company and performed monthly.

Previous studies have shown that RC flat slab buildings are highly vulnerable to progressive collapse because no beams could help redistribute the loads previously carried by the lost columns. The necessary strengthening methods should, therefore, be adapted to reduce the occurrence of progressive collapse. In this paper, the progressive collapse behaviour of five-storey R.C flat slab building is assessed by removing columns from the first-storey and dynamic analysis is conducted in compliance with GSA guidelines (2016). The results are analyzed in terms of vertical displacement and chord rotation at the location of removed columns and compared with the allowable limits as specified in DoD guidelines (2009). Different sized perimeter beams are used as strengthening methods to increase the progressive collapse resistance of the studied flat slab building. Since building strengthening uses structural elements that consume natural resources, sustainability criteria should be explicitly included in the strengthening requirements. These performance enhancement methods are then evaluated from structural, cost and environmental aspects and the results are examined. A strengthening alternative is then proposed which not only satisfy the progressive collapse code requirements but also requires less cost and emits less CO2 gas.

Approximately 22 % of the total German energy consumption is used to provide thermal energy in the industrial sector. Therefore, energy efficiency measures in this area offer a major lever for decarbonizing the energy system. Central heating networks can achieve high energy efficiency levels through economies of scale, the coupling of efficient technologies, or the integration of waste heat. Although energy efficiency of an industrial heating network is a key aspect to minimize operating cost, heating networks are mostly planned and realized with fixed control parameters (e.g., pressure, flow rate or flow temperature). An adjustment of control parameters especially in changing production systems and the integration of renewable energies or waste heat often holds immense energy efficiency potential. This potential can only be harnessed by achieving transparency in the operation of heating networks outlining inefficient behavior. This paper presents a method to continuously evaluate heat flows in industrial heating networks by deriving key performance indicators. The method focuses on linking heat demands as well as flow and return temperatures of production processes or building services to the CO2 emissions and energy efficiency of the heating network. The indicators include network and converter specific parameters such as part load behavior and temperature dependencies. To understand the effects on network efficiency in case of parameter changes (control parameters or network topology), both the generation and the demand side are included in the analysis. By that, operating behavior of an industrial heating network can be improved, e.g., by the integration of waste heat or the linkage to a district heating network. The method is conducted on real data of a complex heating network in an industrial site. An application for the reduction of return temperatures of a consumer showed that potentially 7.6 % of the total CO2 emissions could be reduced by the integration of waste heat.

In the face of challenges such as limited resources and climate change, aerospace companies strive for efficiency and sustainability in their manufacturing processes. By means of Additive Manufacturing (AM), new dimensions in terms of cost-effective and bionic lightweight structures are achievable. Even though AM results in the direct manufacturing of components, a variety of post-processing steps has to be implemented in the manufacturing line. By selecting a suitable automatic identification and data capture (Auto-ID) system, a production control system is capable of responding in real time to unforeseen events, while reducing waste caused, for exemple, through unnecessary processing steps. Thus, this paper focuses on an approach for a production control system for a manufacturing line including metal-based AM process.

This paper presents an intelligent cloud-based platform for workers healthcare monitoring and risk prevention in potentially hazardous manufacturing contexts. The platform is structured according to sequential modules dedicated to data acquisition, processing and decision-making support. Several sensors and data sources, including smart wearables, machine tool embedded sensors and environmental sensors, are employed for data collection, comprising information on offline clinical background, operational and environmental data. The cloud data processing module is responsible for extracting relevant features from the acquired data in order to feed a machine learning-based decision-making support system. The latter provides a classification of workers’ health status so that a prompt intervention can be performed in particularly challenging scenarios.

Higher customer requirements and increasing competitive pressure lead to a growing number of variants and smaller batch sizes in manufacturing. As a result, companies have to deal with challenges of small series manufacturing. A unidimensional set-up optimization cannot provide a satisfying solution. At the same time, production system-specific control algorithms are usually complex and require constant adaptation as customer requirements change continuously. To address these issues, this paper presents a hybrid genetic algorithm which allows modular sequence optimization in production scheduling. Hybrid elements are used to reach a high solution quality within a short runtime.

To handle the rising amount of plastic waste in the environment, new technologies are necessary to recover the value of this resource. Chemical recycling is one option to treat mixed and multi-layer plastic waste streams. Emerging technologies works e.g. with pyrolysis (via thermal or catalytic cracking) of the plastic waste streams and results in multi-material output streams. Membranes are a widely adopted and a relevant technology for bulk separations, within chemical recycling operations. Based on a different separation principle, they need less thermal energy than e.g. distillation columns. However, when it comes to their contribution to the environmental impact of the whole process chain, membranes need special attention. Compared to installed process units, such as pyrolysis and catalytic upgrading reactors, membranes have a shortened life time (non-permanent) compared to the whole process chain and need extra treatments to avoid fouling and maintain their efficiency. These points make it challenging to integrate a LCA model representing membranes within the whole LCA process model. In this paper a framework for the LCA of membranes will be presented regarding their impact in a chemical recycling process and how a shortened life time model can be integrated into a longer life LCA process model with focus on the handling of the different time scales.

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.

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

Industry 4.0 is associated with numerous technologies, which offer great potential for optimizing linear winding processes as used in electric motor manufacturing. To further increase flexibility and quality in the production of mass products like coils, data-driven techniques such as machine learning (ML) are increasingly moving into focus. To generate the required data, however, linear winding machines must be equipped with suitable sensors. Accordingly, this paper will first present all known influencing and quality parameters in linear winding based on earlier research work. Based on this, suitable data sources and sensor systems are identified and a concept for applying ML techniques is derived.

Industry 4.0 has enabled technological integration of cyber physical systems and internet based communication in manufacturing value creation processes. As of now, many people use it as a collective term for advanced technologies, i.e. advanced robotics, artificial intelligence, machine learning, big data analytics, cloud computing, smart sensors, internet of things, augmented reality, etc. This substantially improves flexibility, quality, productivity, cost, and customer satisfaction by transforming existing centralized manufacturing systems towards digital and decentralized one. Despite having potential benefits of industry 4.0, the organizations are facing typical obstacles and challenges in adopting new technologies and successful implementation in their business models. This paper aims to identify potential barriers which may hinder the implementation of industry 4.0 in manufacturing organizations. The identified barriers, through comprehensive literature review and on the basis of opinions collected from industry experts, are: poor value-chain integration, cyber-security challenges, uncertainty about economic benefits, lack of adequate skills in workforce, high investment requirements, lack of infrastructure, jobs disruptions, challenges in data management and data quality, lack of secure standards and norms, and resistance to change. Interpretive Structural Modeling (ISM) is used to establish relationships among these barriers to develop a hierarchical model and MICMAC analysis for further classification of identified barriers for better understanding. An analysis of driving and dependence of the barriers may help in clear understanding of these for successful implementation of Industry 4.0 practices in the organizations.

This paper presents a new modelling approach for liquid lubricant behavior in metal forming operations by focusing on the hydrodynamic pressure build-up in micro pockets. Theoretical and numerical fundamentals of the proposed approach are introduced, and upsetting of an aluminum cylinder with an artificial lubricant pocket is presented as a validation case. The proposed numerical framework splits the fluid-solid interaction model into a computational fluid dynamics and solid mechanics part. While the solid mechanics part employs the Lagrangian finite element flow formulation for plastic deformation, the fluid dynamics part is built upon the set of Navier-Stokes equations applying an Eulerian finite element method in combination with an Arbitrary Langagian Eulerian formalism. The latter enables the displacement based coupling from the solid to the fluid. The fluid-to-solid coupling is pressure based and enabled by the finite element flow formulation’s inherent velocity-pressure characteristics. The weak coupling avoids ill-conditioning of the system matrix and makes it possible to benefit from both Lagrangian and Eulerian meshes.

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.

Additive manufacturing (AM) is seen as a promising technology to improve the resource efficiency and environmental performance in manufacturing systems. However, recent studies have shown that the ecological advantages of AM are dependent on the design of the manufactured parts. This leads to the requirement of the implementation of eco-design for AM (Eco-DfAM). Therefore, this paper presents a case study in which an industrial part has been analyzed and optimized using the energy performance assessment (EPA)-based Eco-DfAM method. In the first step of the case study, generative design has been applied and different design solutions with varied lightweighting factors were proposed. The energy consumption of each design solution is then quantified using a simulation method. Based on the defined energy performance indicators, the energy performances of different solutions are evaluated and compared. Finally, the optimized part with the best evaluation result is produced using the selective laser melting (SLM) process. The results of this case study provide valuable experience for designers who are facing decision-making issues in Eco-DfAM.

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.

For the development of OPC UA servers for machine tools the complex architecture of numeric controls and programmable logic controllers is often depicted in a structure focused on data. The information models provide access to a wide set of available data points but lack intuitive functions, such as handling of NC programs and controlling auxiliary systems, which would be used by machine operators in manual mode. The information model presented in this paper shows a possible solution to combine data and functionality of machine tools in an intuitive structure in order to simplify navigation for clients and users.

The ecosystem of the Industry 4.0 involves many new technologies, such as autonomous mobile robots (AMR) and cobots (collaborative robots), these are characterized with higher flexibility and cost effectiveness which makes them more suitable for automated internal logistics systems. The evaluation of energy consumption of AMRs for a designed path in a real case scenario using analytical tools are challenging. This paper proposes a method of evaluation of the sustainability of new technologies of Industry 4.0 in internal logistics. The proposed framework demonstrates data management technique of the industrial robots. Since, the AMR with manipulator perform different tasks as a single system in logistics there is big demand to develop model of cyber physical system. During task execution measured robots’ physical parameters used as input data to perform analytics. Moreover, acquired data from different condition use cases have been used to monitor the battery behaviour of the AMR and preliminary results of the linear regression model is presented.

Additive Manufacturing (AM) allows for the manufacturing of complex geometry parts as a single object regardless of their geometric complexity. However, manufacturing a consolidate object is not always to be better than the assembly approach. There are several practical scenarios which often require an assembly approach, i.e., (1) to achieve specific mechanical functionality in the product and aesthetic aspects; (2) to fit a product component in a predefined buildable space, particularly the case where large-scale products are required to be fitted with a developing platform space; and (3) to improve the additive manufacturing productivity (by minimizing processing time, improving product quality, surface finish and reducing material/support cost). In this paper, a computational framework is proposed to decompose the initial object design into several assemblies with the aim to minimize the total AM processing time. The decomposed components can be arranged into a minimum printing height space, thus it reduces the number of printing layer significantly, yielding to a build-up time reduction even if some time is spent in required assembling operations. Computation framework is implemented for three medium size object geometries. The presented study points out that the part decomposition and assembling approach can considerably reduce the total AM processing time. The methodology supports AM to be extended for a broad range of medium size products, and leads to strengthening the customizability of the products.

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.

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.

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 surface topography often is an important quality criterion for the manufacturing of milled workpieces as it often defines their functional behaviour. In machining both, the kinematics of the process and the stochastic influences deriving from the machine tool, workpiece and the surrounding environment affect the workpiece’s surface roughness. This paper presents a simulation-based method for flank milling, which considers kinematic and stochastic influences including run-out errors and tooth length variations. The simulation results are used in combination to predict the surface roughness depending on the chosen process parameters. Hence, also making in possible to choose appropriate process parameters to achieve a defined surface roughness.

In times of large-scale crises, seemingly streamlined supply chains could become prone to unforeseen disruptions, leading to interruption in the provision of vital supplies This could lead to severe consequences if such interruptions include vital products, such as lifesaving medical supplies or healthcare workers' protective gear Shortages of vital supplies could occur due to unexpected sharp spike in demand, where manufacturers are unable to produce the necessary quantities required to meet the unusual demand They could also be the result of a disruption in the product's supply chain, originating in another country, or even continent, worse affected by the crisis In either case, localized production, enabled by efforts and resources of local establishments and individuals, could provide a contingency means to produce such vital products to serve local needs, temporarily Motivated by the growing availability of advanced manufacturing technologies, in particular additive manufacturing (AM), this paper aims to develop a decision-making framework for the design of AM enabled local manufacturing networks in times of crises The framework consists of complementing interrelated optimization and simulation models that operate iteratively in an uncertain environment, until a local production network, producing the desired performance targets, emerges Finally, a case study inspired by the shortages of medical supplies, and healthcare workers' personal protective equipment (PPE), during the worldwide 2020 outbreak of the COVID-19 coronavirus is employed to demonstrate the applicability of the framework © 2020 Elsevier B V All rights reserved

New strategies in factory management focus on shopfloor data as a mean to achieve more flexible mass productions. In reality, the value adding usage of smart manufacturing approaches depends on the effort it takes to provide sensor and actuator data for further analysis. The concept of digital twins, that are based on a physical, a virtual and a communication component, promises a scalable solution to read and standardize shopfloor data. So far, there exists no generic method describing the implementation of the data acquisition as part of the communication component of digital twins. This contribution therefore focusses on necessary steps to consider when realizing a scalable data acquisition and examines how existing standards and open source solutions can support the implementation. The first part of the paper derives the necessary steps for acquiring shopfloor data. It starts by defining the architecture and aim of digital twins. Afterwards, a method to implement the generic data acquisition of digital twins is introduced. The approach is inspired by the Plug-and-Produce paradigm of the control engineering field and is adapted to the concepts of data acquisition and management. The second part examines the technical implementation of the proposed method. Identified approaches from a literature review are structured within the generic method. This describes the realization of the individual steps but also systematically differentiates existing approaches to build Digital Twins. With the aim of creating scalable architectures, a special focus is set on available open source solutions and standards when presenting the implementation part of the generic method.

The operation of drying rooms is an essential part of battery cell production, in order to provide a save and well conditioned environment during the cell assembly. A specially dimensioned Heating, Ventilation and Air Conditioning (HVAC) system is required to operate a dry room, which depends on a large number of parameters and accounts for a substantial part of the total energy demand in battery cell production. Therefore, a dry room significantly contributes to the energy embodied in battery cells and affects their cost and environmental footprint. In this context, model-based, quantitative analysis are of interest in order to dynamically evaluate the effects of changed of ambient conditions at different locations. In this paper, we investigate the operation of an existing drying room through a case study at the Battery LabFactory Braunschweig with a physical simulation model. We validate the model against recorded measurement data in high temporal resolution. The model is able to represent the measured data of the total energy demand over one month at an hourly time step with only 3.27 % deviation. Using the validated simulation model of the HVAC system, we examine the operation of the system at different locations regarding their economic and ecological footprint. To achieve this, we virtually relocate the system to five different locations around the world and operate it over a typical year at each location. We carry out an economic and environmental assessment for each site under consideration and for each location we report relevant KPIs that are independent from production throughput and potentially transferable to other use cases. Such investigations allow interesting findings to be derived for practical applications in brown-field applications, but also for the planning of new systems at different locations.

An increasing demand for products and services in the aviation industry is attended by a rising demand for aircraft engines. This requires an increased productivity combined with highest quality and safety standards. In this paper, a new approach of material defect detection is presented which is based on force and acoustic-emission measurements during machining processes. Representative synthetic defects in titanium alloy Ti6246 were investigated. In order to use scale effects, the experiments were conducted in micro-milling processes. This approach could be used for an automated, non-destructive evaluation of the manufactured components in the future and thus for reducing testing effort and related auxiliary times.

With the increasing degree of digitalization in manufacturing industry, advanced data analysis techniques such as machine learning (ML) are moving into focus. In several production processes like machining or welding, ML applications already show high potential for process monitoring, optimization, or control. Although screw fastening and press-in processes are frequently employed in modern assembly lines, there are only few research approaches addressing the application of ML so far. Therefore, this paper starts with an overview of conventional and first ML-based approaches to process curve monitoring. Following this, it is shown how ML can be used to detect and classify errors in screw fastening by analyzing the resulting torque curve. In addition, an assembly line consisting of several press-in processes is used to demonstrate that ML also allows the analysis of cross-process interactions by evaluating several process curves at once. In this way, defective parts can be detected and rejected at an early stage, eliminating further processing and testing steps while simultaneously reducing costs.

In an ever-increasing competitiveness in the engineering landscape, increased complexity of products and processes and reduction of time-to market for products are key competitive factors. To this end, engineers seek the most efficient utilization of digital tools, such as Computer-Aided (CAx), to enable virtual prototyping and reduce the time invested in physical testing of a system after it is built. However, the introduction of digital tools in the development process cannot guarantee a successful design on its own and requires effective utilization of these tools from the engineer, in the form of modeling the system, selecting the optimal compromise in detail to reduce the complexity of the model and interpretation of results. In this study, the virtual prototyping of a cryogenic cooling system for hybrid manufacturing is investigated. Hybrid manufacturing, incorporating additive and subtractive capabilities in the same machine tool, is one of the most promising technologies that can ramp up the use of Additive Manufacturing in many high-value industrial components. The use of cryogenic cooling is a sustainable alternative to conventional emulsion cooling, which can have detrimental effects on the part and machine tool components, without compromising the heat dissipation performance. The paper presents the design considerations during the conceptual design phase, the design variables to be investigated for the parametric design phase are selected, a physics-based model is utilized for the virtual optimization of the design and the results are interpreted and discussed.

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.

Cyber-physical systems (CPS), like autonomous vehicles, are intelligent and networked. The development of such systems requires interdisciplinary cooperation between different stakeholders. A lack of system understanding between stakeholders can lead to unidentified security threats & safety hazards, resulting in high costs in product development. In particular, a lack of an integrative consideration of security threats & safety hazards can compromise safety compliance for CPS. Model-based systems engineering (MBSE) improves the understanding of systems between stakeholders by additionally creating supporting models. However, MBSE approaches only partially address security threats & safety hazards. In particular, their integrative consideration is not taken into account. Established security & safety approaches either are only applicable to specific disciplines or only partially consider security threats & safety hazards. In this paper we present a method that enables the early identification of safety relevant security threats. The method is designed to be applied in workshops with an interdisciplinary team of stakeholders and is used to determine initial results for the system architecture design phase. We illustrate our approach with the example of the automotive sector. To build a realistic system architecture we identified 18 architectural vehicle components including relevant architectural constraints. Finally, we present an evaluation of the method, based on a workshop with 30 master students.

With as much as one third of all food wasted, the highly reported environmental footprint of global food supply provides an urgent driver for changes to be made to food provision and consumption. In developed countries the main point of waste generation is at the consumption level (typically over half of the waste by mass), but it is too easy to attribute the problem to consumer behavior \x96 it is a systematic problem of the entire supply chain. This current research addresses the issues of supply and demand balancing, down to the consumer level, through the development of a product service system (PSS) that enables consumers to better plan and purchase items for consumption. As part of the PSS, an inventory management system allows intelligent and highly convenient purchasing based on available food (e.g. leftover inventory from the previous week) and on consumer preferences and requirements. This agile planning is based on an artificial intelligence driven system. In this respect, a smart platform is conceptualized to automatically match recipes from an existing database with available ingredients via machine learning, with the aim of providing solutions in terms of shopping order recommendations for the following week. This mass customization of food supply further enables producers and providers to manage demand by promoting or restricting foodstuffs that are in excess or limited supply, respectively. The platform thus allows reduction of food waste at the consumer level and further up the supply chain. This paper demonstrates the need for better matching of supply and demand within the food supply chain, describes the basis for a PSS for food provision including optimization of recipe selection. A case study is developed to exemplify how the proposed platform could be implemented within modern commerce systems.

This paper aims to reveal the role of e-waste management practices for environmental sustainability and explores people’s awareness level about e-waste management, its generation, and primary treatment practices in educational institutions. The empirical study is conducted in India through a well-structured questionnaire distributed among engineering students. Six critical aspects of e-waste, i.e., generation, management, nature of e-waste components, progressive use of e-waste management, control, and training, are studied. This study’s outcome reveals that the awareness level of e-waste generation and its processing practices are deficient, but the extended producer responsibility concept is becoming influential. Despite appreciable steps taken by the competent authorities, only a small population is aware of e-waste management. The respondents are aware of the hazardous effects of e-waste and its progressive usage. The ‘inclusion of e-waste in study programs’ is desired by the prospective engineering graduates.

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.

The evolution of production systems encloses continuous adaptation of workplaces with changing levels of technologies and automation. This research paper examines scenarios of multi-skilled operators, cooperating to accomplish a common goal. The under-consideration environment consists of a distributed network of workstations, each one assigned with a set of pending jobs. Therefore, the production engineer faces the challenging task of job allocation to suitable operators and appropriate workstations. As such, the formulation of the problem is based on the evaluation of each individual operator's job-related skills and the provision of an intelligent decision-making algorithm for the human resources allocation. The framework addresses the optimization of the allocated operators-jobs correlation and the workforce cost as the model's decision criteria. The model was implemented in a cross-platform planning application and tested in a real-life industrial scenario.

Structural Health Monitoring (SHM) has been researched for a long time and continues to be an active area of research. Initial work on SHM involved identification of hand-crafted features and predictive models relied on statistical methods. The recent improvements in computing capabilities, coupled with better integration of sensor data, has led to the emergence of more effective techniques in terms of scalability and predictive power. Machine learning offers a solution through automatic feature extraction algorithms, and scalable and noise robust models. Convolutional Neural Networks (CNN) have been used as state-of-art classifiers for images as well as for text. This paper proposes the use of the monitored structure’s transmissibility functions for the structure under observation, which can be fed into a novel composite architecture consisting of Deep CNN followed by multivariate linear regressors to detect, localize, and quantify the damage extent in a system. The proposed method was tested on the Los Alamos’ Eight degree-of-freedom (DOF) structure, and the Structural Beam Data from Laboratory of Mechanical Vibrations and Rotor Dynamics, University of Chile. This study on damage localization and quantification can be leveraged to comment on the safety and soundness of the structure under inspection and can help in making more informed inferences. It is expected that, in general, this will lead to extended structure life, which not only improves the resource utilization in terms of structure maintenance and its longevity but also decreases the carbon footprint and capital expenditure.

Digitization within the framework of Industry 4.0 is considered the biggest and fastest driver of change in history of manufacturing industry. While the size of a company is becoming less essential, the ability to adapt quickly to changing market conditions and new technologies is more important than ever. This trend particularly applies to the companies’ software landscapes, where individual sub-processes and services must be orchestrated, seamlessly integrated, and iteratively renewed according to the ever-increasing user requirements. However, inflexible, closed monolithic software applications as well as self-programmed stand-alone tools that are difficult to integrate are still predominant in the engineering domain. A complete reimplementation of existing, proprietary engineering tools and their integration into monolithic applications of large software providers is often not economically feasible, especially for small and medium-sized machinery and plant manufacturers. In this context, the so-called Process-Driven Approach (PDA) offers a sustainable and tool-neutral opportunity for process and tool orchestration, enabling an easy integration of individual software applications by consistent utilization of the separation of concerns principle. The PDA, originating from business informatics, is mainly based on the standardized and machine-executable visual modeling language Business Process Model and Notation (BPMN). Using the semantic enhancements found in version 2.0, BPMN is not just used to model the business processes but also to model and execute the integration processes between different systems. After the PDA has already been successfully applied to large-scale projects in business informatics, it is now being transferred to the engineering domain. As shown in this paper, PDA allows to orchestrate the different processes in engineering and to integrate the underlying software tools, such as e-mail or spreadsheet applications, engineering tools, or custom microservices, using standardized interfaces like REST API. In doing so, engineering processes can be made more transparent, monitored, and optimized by means of appropriate key figures. The concept is validated by a prototypical implementation of a minimum functional PDA architecture for the engineering domain.

Industry 4 0 concept is one of the growing concepts worldwide for the last five years It combines the physical and digital world, thus creating new capabilities which affect not only the manufacturing industry but to all other sectors and organizations Furthermore, many experts worldwide declare that Industry 4 0 will have a substantially positive impact, especially in developing countries, where low-paid jobs are concentrated After facing the social and economic effects of Coronavirus (Covid-19) global pandemic, the introduction of digitalization and moving toward Industry 4 0 has become not only emerging but essential for every industry, including SMEs It is expected that it will help to improve the flexibility, productivity, and sustainability of the industries, to develop a knowledge-based economy and society, and, thus, enhance the quality of life Therefore, this paper aims to analyze the emerging concept of Industry 4 0, impacts and applicability on SMEs, and, in particular, the readiness of SMEs in Kazakhstan to digitization and Industry 4 0 The gap in research, which exists currently about drivers, challenges, barriers, and opportunities specific for SMEs in Kazakhstan for moving towards Industry 4 0, will also be investigated © 2020 Elsevier B V All rights reserved

Life cycle engineering (LCE) methods are essential in the development of next generation energy storage systems in order to avoid harmful unintended environmental consequences. Moreover, the integration of life cycle knowledge early in the development stage of such technologies can lead to improved performance in regard to their environmental impact. Extensive information from all stages is needed to understand interdependencies along the life cycle, e.g. between required raw materials, applied processing parameters and lifetime behaviour of the battery. From an LCE perspective, the development of detailed and representative inventories for the assessment of traction batteries is a time and resource intensive process, which requires a difficult synchronization with product development activities. A cyber and physical integration of Life Cycle Technologies in a product system offers the potential to reduce data collection efforts while enhancing its robustness and reliability. Life Cycle Technologies can refer to any product- or process- integrated data acquisition technology, e.g. sensors, which can be leveraged to provide knowledge about a product’s life cycle performance. In this paper, we introduce the concept of Life Cycle Technologies and their potential to maximize the functionality of traction batteries while reducing their life cycle environmental impact. Furthermore, we present a reference architecture aiming at the development and integration of such technologies. Finally, we illustrate its application in a case study of All Solid State Batteries and discuss its usability in the development of new generation battery systems.

Assembly processes are characterized by frequent changes and a high density of information being displayed to workers. These challenges necessitate cognitive worker assistance solutions that are individually and situatively adaptable. Adaptation in digital assistance is primarily based on the type of human-technology interaction, which essentially can be deterministic, adaptive or adaptable. Hence, this paper presents a method for determining the type of adaptation appropriate for functionalities of cognitive worker assistance in series production. Adaptivity and adaptability function as design parameters concerning dialog initiatives in system interaction. While adaptivity is characterized by system-initiated changes, adaptability implies an interaction initiative of the assembly operator. Based on defined functionalities according to product- and process-specific framework conditions, scenario-based characteristics are identified and categorized. Beside different user perspectives, information technology efforts for implementing assistance systems are examined to evaluate the potential for enabling agile assembly systems.

Manufacturing has evolved over the past few centuries, both in technological and managerial terms; for the latter, Lean Manufacturing (LM) has been widely used to maximize value for customers and minimize waste. The broad use of Information and Communication Technologies (ICT) in manufacturing forms the concept of Industry 4.0 (I4.0), connecting physical and digital systems and integrating value chains. Although with different approaches, LM and I4.0 share the same goals of increasing flexibility and productivity. In fact, two views on their interaction can be extracted from literature: LM serves as a basis for I4.0 and I4.0 improves LM effectiveness. The novelties presented by I4.0 comprise not only the technical scope, but also the behavioral one. It has been unclear, however, how I4.0 affects LM soft practices, especially when it comes to the skills required for Lean professionals to face this new environment. In this regard, this paper addresses the impact of I4.0 on the skills of Lean professionals. One could observe that in addition to technical skills linked to programming and data science, analytical and interpersonal skills make up the sociocultural skills necessary for the professional of the future in the context of I4.0.

A digital twin is a digital representation of an unique product or product service system that is sufficient to meet the requirements of a set of use cases. Following this definition, the implementation of a digital twin must be derived from concrete use cases along the life cycle of a product. However, the involvement of a variety of different stakeholders and frequent changes of ownership in the value chain complicate the identification of use cases as well as the specification of the digital twin and its governance. This leads to a need for a systematic approach that enables various partners to implement the digital twins needed for their specific use cases. This paper presents a specification technique that combines aspects from Model-Based Systems Engineering with Product Lifecycle Management to support a collaborative front-end planning and specification of digital twins. It integrates the different views of various digital twin stakeholders (product manager, systems engineer, developer, data scientist etc.) in a collaborative model across the value chain. As part of the systematic approach, a digital twin specification grid that structures guidelines for the aspects of a digital twin (e.g. the data sources) across the product life cycle is provided. The presented approach also supports the derivation of a digital twin by integrating different use cases. This specification technique is the first step towards a holistic, Model-Based Digital Twin Design Framework.

Sustainable manufacturing has recently gained great attention in the manufacturing industry. However, there is still a lack of a comprehensive framework for enterprise level sustainability assessment of manufacturing companies making sustainability assessment time consuming to be implemented. This paper presents a newly developed framework for sustainability assessment of manufacturing organizations. In this context, a composite sustainability index is introduced, which covers the triple bottom line approach, the total life cycle stages and the 6R principles. The framework consists of five main steps, which are the selection of suitable indicators, data collection, data normalization, the application of the fuzzy analytic hierarchy process to weight the identified indicators and finally the aggregation step to calculate the sustainability index. Apart from pure methodical issues, a concrete application of this newly developed framework is also presented. In this context, the sustainability index is calculated for a manufacturing organization considering the years 2014 to 2018. © 2020 The Authors, Published by Elsevier B.V. Peer review under the responsibility of the scientific committee of CIRP.

Challenges of the development of mechatronic systems and corresponding production systems have increased steadily. Changes are primarily due to increased product complexity and the connection to the internet of things and services, enabling Cyber-Physical Systems (CPS) and Cyber-Physical Production Systems (CPPS). Major innovations of the revised VDI guideline 2206 for developing mechatronic systems are systems thinking as a core element and six checkpoints for structuring deliverables along the V-Model. These checkpoints serve for orientation in result progress and thus enable a structured and complete development process. However, tasks and checkpoints of the new guideline focus on the product development itself without integrating the development of related CPPS, enabling optimization simultaneously to system development. Implications are derived by a three-step analysis. The paper at hand contributes fundamental extensions of the checkpoint questions regarding integrated CPPS development. These questions provide methodical support for system developers of CPPS for CPS by enabling the project manager to check the status, schedule further development steps and evaluate the maturity of the whole, integrated development.

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.

Currently and in view of technological development, mechatronic systems have become more and more complex. This will undoubtedly influenc the design of products. For this reason, designers do not stop developing new solutions to best ensure customer satisfaction on functional dimensional and geometrical requirements. However, to achieve a finished product satisfying all stakeholders, designers should face a multitud of problems. Among them, the difficulty of data exchange between the different developers contributing to the design process of a finishe product. Several research works have focused on data exchange between Computer Aided Design (CAD) and Computer Aided Engineering applications (CAE) or at the CAD development and manufacturing levels. However, few works study the interoperability between all the produc development levels and the system engineering one. For instance, the lack of continuity between the system-engineering model which has global view on the product, and the CAD model which integrates the parts and assembly CAD data, causes an enormous lost time in th verification and validation process of requirements. In this paper a methodology allowing the interoperability between the system leve specifications and the CAD model of a product being designed is proposed. Based on SysML diagrams, the customer needs and the functiona analysis of the product lead to the definition of the initial structure of the product assembly. The results of this step are used as an input by th CAD designer to detail the architecture of the mechanical assembly. Finally, the product assembly data required for verification and validatio are extracted from the CAD model and then used back to enrich the preliminary SysML model. The system engineer uses the collected data i the SysML model to check and validate the product requirements according to a V model. A pedal of a bicycle was used as an example to illustrat and prove the effectiveness and efficiency of the proposed approach.