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Ingenjörsmässighet today is a matter of being able to engineer complex and multifaceted systems, to empathize with users, clients, and stakeholders to know more (and know better) about the problems to be solved, and to judge the ‘goodness’ of proposed solutions across social, political, technological, cultural and environmental values. Challenge-Based Learning (CBL) has been promoted in recent years as a means for students to align the acquisition of disciplinary knowledge with the development of transversal competencies while working on authentic and sociotechnical societal problems. This paper aims to contribute to the CBL discussion by presenting the lessons learned related to conducting engineering design projects with a focus on issues and themes related to innovation in the cultural heritage domain. The paper describes how engineering design projects at the undergraduate level have been set up and conducted to cope with different innovation challenges related to the World Heritage Site of the city of Karlskrona in Sweden. It elaborates on how design projects with a cultural heritage theme can leverage how students learn about ‘softer’ and more intangible dimensions of ‘value’ (for products, systems and services).

The paper summarizes the experiences of working for a decade to promote the transition of courses toward the CDIO framework. It presents a list of lessons learned when applying the CDIO principles in master-level classes in the field of Mechanical Engineering. The paper aims to formalize operational guidelines, serving as a platform for further discussion and as a set of practical recommendations for teachers and educators initially redesigning their courses toward a more CDIO-oriented structure. The paper initially presents the rationale formoving toward CDIO and describes where and how data were collected. Further, it lists several lessons learned for teachers on (1) how to support students in maximizing their learning opportunities in a CDIO context, (2) how to manage the relationship and engagement with company partners, dealing with expectations, trust, flexibility, and visibility.

Ingenjörsmässighet today is a matter of being able to engineer complex and multifaceted systems, to empathize with users, clients, and stakeholders to know more (and know better) about the problems to be solved, and to judge the ‘goodness’ of proposed solutions across social, political, technological, cultural and environmental values. Challenge-Based Learning (CBL) has been promoted in recent years as a means for students to align the acquisition of disciplinary knowledge with the development of transversal competencies while working on authentic and sociotechnical societal problems. This paper aims to contribute to the CBL discussion by presenting the lessons learned related to conducting engineering design projects with a focus on issues and themes related to innovation in the cultural heritage domain. The paper describes how engineering design projects at the undergraduate level have been set up and conducted to cope with different innovation challenges related to the World Heritage Site of the city of Karlskrona in Sweden. It elaborates on how design projects with a cultural heritage theme can leverage how students learn about ‘softer’ and more intangible dimensions of ‘value’ (for products, systems and services).

Ingenjörsmässighet today is a matter of being able to engineer complex and multifaceted systems, to empathize with users, clients, and stakeholders to know more (and know better) about the problems to be solved, and to judge the ‘goodness’ of proposed solutions across social, political, technological, cultural and environmental values. Challenge-Based Learning (CBL) has been promoted in recent years as a means for students to align the acquisition of disciplinary knowledge with the development of transversal competencies while working on authentic and sociotechnical societal problems. This paper aims to contribute to the CBL discussion by presenting the lessons learned related to conducting engineering design projects with a focus on issues and themes related to innovation in the cultural heritage domain. The paper describes how engineering design projects at the undergraduate level have been set up and conducted to cope with different innovation challenges related to the World Heritage Site of the city of Karlskrona in Sweden. It elaborates on how design projects with a cultural heritage theme can leverage how students learn about ‘softer’ and more intangible dimensions of ‘value’ (for products, systems and services).

Ingenjörsmässighet today is a matter of being able to engineer complex and multifaceted systems, to empathize with users, clients, and stakeholders to know more (and know better) about the problems to be solved, and to judge the ‘goodness’ of proposed solutions across social, political, technological, cultural and environmental values. Challenge-Based Learning (CBL) has been promoted in recent years as a means for students to align the acquisition of disciplinary knowledge with the development of transversal competencies while working on authentic and sociotechnical societal problems. This paper aims to contribute to the CBL discussion by presenting the lessons learned related to conducting engineering design projects with a focus on issues and themes related to innovation in the cultural heritage domain. The paper describes how engineering design projects at the undergraduate level have been set up and conducted to cope with different innovation challenges related to the World Heritage Site of the city of Karlskrona in Sweden. It elaborates on how design projects with a cultural heritage theme can leverage how students learn about ‘softer’ and more intangible dimensions of ‘value’ (for products, systems and services).

Ingenjörsmässighet today is a matter of being able to engineer complex and multifaceted systems, to empathize with users, clients, and stakeholders to know more (and know better) about the problems to be solved, and to judge the ‘goodness’ of proposed solutions across social, political, technological, cultural and environmental values. Challenge-Based Learning (CBL) has been promoted in recent years as a means for students to align the acquisition of disciplinary knowledge with the development of transversal competencies while working on authentic and sociotechnical societal problems. This paper aims to contribute to the CBL discussion by presenting the lessons learned related to conducting engineering design projects with a focus on issues and themes related to innovation in the cultural heritage domain. The paper describes how engineering design projects at the undergraduate level have been set up and conducted to cope with different innovation challenges related to the World Heritage Site of the city of Karlskrona in Sweden. It elaborates on how design projects with a cultural heritage theme can leverage how students learn about ‘softer’ and more intangible dimensions of ‘value’ (for products, systems and services).

The International Design Engineering Annual (IDEA) Challenge is a virtually hosted hackathon for Engineering Design researchers with aims of: i) generating open access datasets; ii) fostering community between researchers; and, iii) applying great design minds to develop solutions to real design problems. This paper presents the 2022 IDEA challenge and elements of the captured dataset with the aim of providing insights into prototyping behaviours at virtually hosted hackathons, comparing it with the 2021 challenge dataset and providing reflections and learnings from two years of running the challenge. The dataset is shown to provide valuable insights into how designers spend their time at hackathon events and how, why and when prototypes are used during their design processes. The dataset also corroborates the findings from the 2021 dataset, demonstrating the complementarity of physical and sketch prototypes. With this paper, we also invite the wider community to contribute to the IDEA Challenge in future years, either as participants or in using the platform to run their own design studies. © The Author(s), 2023. Published by Cambridge University Press.

The International Design Engineering Annual (IDEA) Challenge is a virtually hosted hackathon for Engineering Design researchers with aims of: i) generating open access datasets; ii) fostering community between researchers; and, iii) applying great design minds to develop solutions to real design problems. This paper presents the 2022 IDEA challenge and elements of the captured dataset with the aim of providing insights into prototyping behaviours at virtually hosted hackathons, comparing it with the 2021 challenge dataset and providing reflections and learnings from two years of running the challenge. The dataset is shown to provide valuable insights into how designers spend their time at hackathon events and how, why and when prototypes are used during their design processes. The dataset also corroborates the findings from the 2021 dataset, demonstrating the complementarity of physical and sketch prototypes. With this paper, we also invite the wider community to contribute to the IDEA Challenge in future years, either as participants or in using the platform to run their own design studies. © The Author(s), 2023. Published by Cambridge University Press.

The International Design Engineering Annual (IDEA) Challenge is a virtually hosted hackathon for Engineering Design researchers with aims of: i) generating open access datasets; ii) fostering community between researchers; and, iii) applying great design minds to develop solutions to real design problems. This paper presents the 2022 IDEA challenge and elements of the captured dataset with the aim of providing insights into prototyping behaviours at virtually hosted hackathons, comparing it with the 2021 challenge dataset and providing reflections and learnings from two years of running the challenge. The dataset is shown to provide valuable insights into how designers spend their time at hackathon events and how, why and when prototypes are used during their design processes. The dataset also corroborates the findings from the 2021 dataset, demonstrating the complementarity of physical and sketch prototypes. With this paper, we also invite the wider community to contribute to the IDEA Challenge in future years, either as participants or in using the platform to run their own design studies. © The Author(s), 2023. Published by Cambridge University Press.

The International Design Engineering Annual (IDEA) Challenge is a virtually hosted hackathon for Engineering Design researchers with aims of: i) generating open access datasets; ii) fostering community between researchers; and, iii) applying great design minds to develop solutions to real design problems. This paper presents the 2022 IDEA challenge and elements of the captured dataset with the aim of providing insights into prototyping behaviours at virtually hosted hackathons, comparing it with the 2021 challenge dataset and providing reflections and learnings from two years of running the challenge. The dataset is shown to provide valuable insights into how designers spend their time at hackathon events and how, why and when prototypes are used during their design processes. The dataset also corroborates the findings from the 2021 dataset, demonstrating the complementarity of physical and sketch prototypes. With this paper, we also invite the wider community to contribute to the IDEA Challenge in future years, either as participants or in using the platform to run their own design studies. © The Author(s), 2023. Published by Cambridge University Press.

The International Design Engineering Annual (IDEA) Challenge is a virtually hosted hackathon for Engineering Design researchers with aims of: i) generating open access datasets; ii) fostering community between researchers; and, iii) applying great design minds to develop solutions to real design problems. This paper presents the 2022 IDEA challenge and elements of the captured dataset with the aim of providing insights into prototyping behaviours at virtually hosted hackathons, comparing it with the 2021 challenge dataset and providing reflections and learnings from two years of running the challenge. The dataset is shown to provide valuable insights into how designers spend their time at hackathon events and how, why and when prototypes are used during their design processes. The dataset also corroborates the findings from the 2021 dataset, demonstrating the complementarity of physical and sketch prototypes. With this paper, we also invite the wider community to contribute to the IDEA Challenge in future years, either as participants or in using the platform to run their own design studies. © The Author(s), 2023. Published by Cambridge University Press.

The International Design Engineering Annual (IDEA) Challenge is a virtually hosted hackathon for Engineering Design researchers with aims of: i) generating open access datasets; ii) fostering community between researchers; and, iii) applying great design minds to develop solutions to real design problems. This paper presents the 2022 IDEA challenge and elements of the captured dataset with the aim of providing insights into prototyping behaviours at virtually hosted hackathons, comparing it with the 2021 challenge dataset and providing reflections and learnings from two years of running the challenge. The dataset is shown to provide valuable insights into how designers spend their time at hackathon events and how, why and when prototypes are used during their design processes. The dataset also corroborates the findings from the 2021 dataset, demonstrating the complementarity of physical and sketch prototypes. With this paper, we also invite the wider community to contribute to the IDEA Challenge in future years, either as participants or in using the platform to run their own design studies. © The Author(s), 2023. Published by Cambridge University Press.

The paper presents an approach, based on the development of digital twins, to support the transition toward electromobility and autonomy in the mining industry, by supporting the design space exploration of future operational scenarios based on different construction equipment and mining site configurations. With such an intent, the paper presents an approach combining systems and systems-of-systems simulations to run trade-off analysis based on different product-service systems (PSS) configurations. Additionally, the paper integrates the "operational context"variables in PSS design simulations to create a digital twin of a "mining operational scenario"customizable for the specific configurations of each mine. The paper exemplifies the proposed approach by contextualizing in a reference mining site describing how the multi-dimensional simulations have enabled PSS trade-off analysis and PSS sensitivity analyses, and how operational context variables are integrated into the digital twin of the operational scenario. © 2022 The Authors., Open access

The design of a circular PSS solution goes beyond the traditional perspective of circular product design encompassing multiple complexity dimensions that need to be considered and addressed in the early stages of design. The paper provides a literature-based outlook on the levels of complexity to be faced when making decisions in early PSS design by positioning the concept of changeability inherited from the systems engineering literature into the context of the design of circular PSS. The paper ultimately stresses the need to consider the changeability of PSS as a relevant dimension in the assessment of its circularity potential. It does so by proposing a framework for the design of circular PSS solutions, summarizing the main design strategies and approaches currently described in the literature to mitigate the uncertainties generated by PSS complexity.

The paper presents an approach, based on the development of digital twins, to support the transition toward electromobility and autonomy in the mining industry, by supporting the design space exploration of future operational scenarios based on different construction equipment and mining site configurations. With such an intent, the paper presents an approach combining systems and systems-of-systems simulations to run trade-off analysis based on different product-service systems (PSS) configurations. Additionally, the paper integrates the "operational context"variables in PSS design simulations to create a digital twin of a "mining operational scenario"customizable for the specific configurations of each mine. The paper exemplifies the proposed approach by contextualizing in a reference mining site describing how the multi-dimensional simulations have enabled PSS trade-off analysis and PSS sensitivity analyses, and how operational context variables are integrated into the digital twin of the operational scenario. © 2022 The Authors., Open access

The paper presents an approach, based on the development of digital twins, to support the transition toward electromobility and autonomy in the mining industry, by supporting the design space exploration of future operational scenarios based on different construction equipment and mining site configurations. With such an intent, the paper presents an approach combining systems and systems-of-systems simulations to run trade-off analysis based on different product-service systems (PSS) configurations. Additionally, the paper integrates the "operational context"variables in PSS design simulations to create a digital twin of a "mining operational scenario"customizable for the specific configurations of each mine. The paper exemplifies the proposed approach by contextualizing in a reference mining site describing how the multi-dimensional simulations have enabled PSS trade-off analysis and PSS sensitivity analyses, and how operational context variables are integrated into the digital twin of the operational scenario. © 2022 The Authors., Open access

When a Product-Service System (PSS) has a longer lifecycle, it is subjected to several internal and external changes along its path that may deteriorate its value. A PSS capable of delivering value despite the circumstances is called a value robust PSS, and one way of achieving value robustness is by incorporating changeability. A lot of uncertainties prevail in decision-making concerning changeability as change is a phenomenon not clearly understood in PSS design. To bridge this gap, this paper presents a reference framework to support the definition and quantification of changeability in PSS design to enable the delivery of a value robust PSS. The paper is mainly focused mainly on the realization of the concept of changeability in PSS design, crossing the boundary between rich literature in the field of PSS and Systems Engineering., eTwin - Strategic Innovation Program Swedish Mining Innovation

The paper presents an approach, based on the development of digital twins, to support the transition toward electromobility and autonomy in the mining industry, by supporting the design space exploration of future operational scenarios based on different construction equipment and mining site configurations. With such an intent, the paper presents an approach combining systems and systems-of-systems simulations to run trade-off analysis based on different product-service systems (PSS) configurations. Additionally, the paper integrates the "operational context"variables in PSS design simulations to create a digital twin of a "mining operational scenario"customizable for the specific configurations of each mine. The paper exemplifies the proposed approach by contextualizing in a reference mining site describing how the multi-dimensional simulations have enabled PSS trade-off analysis and PSS sensitivity analyses, and how operational context variables are integrated into the digital twin of the operational scenario. © 2022 The Authors., Open access

The paper presents an approach, based on the development of digital twins, to support the transition toward electromobility and autonomy in the mining industry, by supporting the design space exploration of future operational scenarios based on different construction equipment and mining site configurations. With such an intent, the paper presents an approach combining systems and systems-of-systems simulations to run trade-off analysis based on different product-service systems (PSS) configurations. Additionally, the paper integrates the "operational context"variables in PSS design simulations to create a digital twin of a "mining operational scenario"customizable for the specific configurations of each mine. The paper exemplifies the proposed approach by contextualizing in a reference mining site describing how the multi-dimensional simulations have enabled PSS trade-off analysis and PSS sensitivity analyses, and how operational context variables are integrated into the digital twin of the operational scenario. © 2022 The Authors., Open access

The paper presents an approach, based on the development of digital twins, to support the transition toward electromobility and autonomy in the mining industry, by supporting the design space exploration of future operational scenarios based on different construction equipment and mining site configurations. With such an intent, the paper presents an approach combining systems and systems-of-systems simulations to run trade-off analysis based on different product-service systems (PSS) configurations. Additionally, the paper integrates the "operational context"variables in PSS design simulations to create a digital twin of a "mining operational scenario"customizable for the specific configurations of each mine. The paper exemplifies the proposed approach by contextualizing in a reference mining site describing how the multi-dimensional simulations have enabled PSS trade-off analysis and PSS sensitivity analyses, and how operational context variables are integrated into the digital twin of the operational scenario. © 2022 The Authors., Open access

The paper outlines a vision for how XR technologies can be utilized to support PSS design decisions concerning changeability with an emphasis on perceptual limitations. An XR-enabled PSS changeability incorporation method is proposed based on the extension of the Responsive Systems Comparison method by integrating XR technologies to leverage the potential of information visualization for decision-making. The proposed method is theoretically described and applied to a demonstrative case in the construction machinery industry facing the problem of designing the next-generation autonomous and electrical vehicles for quarrying and mining. © 2023, IFIP International Federation for Information Processing.

As manufacturing firms move from pure product-based business models toward Product-Service Systems offerings, value creation becomes more complex and requires more active cross-disciplinary collaboration. Especially non-functional values, those connected to the overall PSS behavior, depend on the successful alignment and coordination between perspectives. However, understanding the value creation itself, particularly among different departments or stakeholders, can be challenging. This paper conceptualizes a value model and a method based on Group Model Building to support value exploration of non-functional design objectives in early-stage Product-Service System design. The value model is constructed based on three layers of granularity to visualize how value is created holistically. Further, the proposed method allows participants to jointly explore and identify design objectives and put these in the context of value. The method and model were tested at two companies, showing that the proposed method and model successfully supported a collaborative value exploration in the non-functional domain. © 2023, IFIP International Federation for Information Processing.

Today, the Product-Service Systems (PSS) methodology is challenged by the emerging trend of digital servitization in supporting the development of the primarily product-oriented companies. Value co-creation strategy and global collaborative innovation are important for product-oriented industry to explore new ways of designing of smart PSS. In this paper, the Future Innovation Framework (FIF) is proposed as a mechanism to facilitate the adoption and use of value co-creation of smart PSS design in global manufacturing companies. Through qualitative research and literature review, the research focused on the collaboration between typical stakeholders in manufacturing industry, defining a smart PSS design process, and explaining how value co-creation could more effectively support the PSS design. This study proposes the FIF for smart PSS design from the perspective of value co-creation, to support the collaborative innovation teamwork, customer involvement and decision-making in the early design phase of smart PSS. The paper explains each of the design processes in which the product-oriented industry can use the FIF framework when designing smart PSS. The FIF was applied to an industrial smart PSS case – the design of the new product and service offering by automotive manufacturer Volkswagen. The FIF was used to verify the design of customer solution proposals for smart PSS. This paper discusses the implications, opportunities and challenges of the FIF for industrial smart PSS design. The paper concludes with a discussion on the possibility of using FIF in different industry domains in the future.

The paper presents an approach where the iterative replication of Discrete Event Simulations on future operational scenarios is used to derive data-driven design merit functions. The presented contribution proposes an extension of the EVOKE (Early Value Oriented Design Exploration with Knowledge Maturity) approach determining when and how the experience-based judgment about maximization, minimization, optimization, and avoidance functions, correlating value drivers and quantified objectives, can be substituted by data-driven mathematical functions obtained by scenarios simulations. The approach is described through a simplified case concerning the development of autonomous electric vehicles to complement the public transport system in the city of Karlskrona in Sweden. The consideration of value drivers and quantified objectives presented is meant to support a preliminary screening of potential design configurations to support the definition of high-level product and system-related functional requirements, to be run before a more detailed conceptual design analysis. © The Author(s), 2023. Published by Cambridge University Press., TRUST-SOS

The design of Product-Service Systems (PSS) is challenging due to the inherent complexities and the associated uncertainties. This challenge aggravates when the PSS being considered has a longer lifespan, is expected to encounter a dynamic context, and integrates many novel technologies. From systems engineering literature, one of the measures for mitigating the risks associated with the uncertainties is incorporating means in the system to change internally as a response to change externally. Such systems are referred to as value-robust systems, and their development largely relies on Tradespace exploration and synthesis. Tradespace exploration and synthesis can be challenging and a time-consuming task due to dimensionality. In this light, this paper aims to present an approach that enables the population of the Tradespace and then, supports the synthesis of such a Tradespace using a clustering algorithm for support changeability quantification in PSS. The proposed method is also implemented on a demonstrative case from the construction machinery industry., TRUST-SOS, eTwin

The current efforts to reduce the carbon footprint throughout the chain in the automotive industry by increased use of recycled materials poses new challenges for materials production and their use. The increase of steel scrap fraction in the current primary steel making processes, used for producing steel sheet metal for automotive components, possibly affects the material properties variability beyond the limits observed in the materials produced today despite mitigating actions in steel production. In this paper material variability increase was modelled by selecting deterministic values outside the range of the material grade used to design and manufacture an automotive part. The values were selected from an experimental data set representing the cold rolled mild steels material class range. The effects were studied numerically on a reverse engineered model of an existing automotive part production process. It was found that the manufacturing feasibility in this particular case is mainly affected by the weighted average plastic strain ratio and less by the degree of planar anisotropy.

Academic institutions are increasingly required to prepare future practitioners to face complex sustainability challenges. The need to foster the development of different skills, attitudes, and multidisciplinary collaboration raised the interest in alternative learning approaches. Game-based learning can be a tool to achieve a variety of desirable learning outcomes, including sustainability and collaborative attitudes change. We present a pilot study investigating the potential of a board game on sustainability risks and opportunities in product development and life cycle, performing a test with different student audiences. The paper discusses our results of the experiment, including a survey following the game, qualitative analysis of students' feedback, and observations during the game sessions. Additionally, we relate insights from students' reflections to CDIO learning objectives. We then illustrate lessons learned and the potential advantages of using the game compared to other teaching approaches and as a complementary tool. Finally, we propose future directions and recommendations for the use of the board game and game-based learning in sustainability education with different student audiences. © The Author(s), 2023. Published by Cambridge University Press.

To improve the accuracy of forming simulations, advanced friction models are increasingly used in the industry. These models account for the physical properties of the sheet, tool and lubrication and describe the tribological conditions during the forming operation. One of the main influencing factors on the tribology system, and therefore the friction coefficient, is the surface roughness of the tools. Until now, it is often assumed all tools have the same uniform surface roughness. In reality however, the tool might have different surface conditions dependent on the type and location of the tool. That is, the blank holder might be differently polished than the punch, and sharp radii might have a different tool roughness compared to flat areas. This paper investigates a significant number of tool measurements from different tool sets from Volvo Cars, and quantifies the effect of local surface conditions on product quality.

Cost and time for stamping die tryouts are significant within the car industry. A major contributing factor is that elastic deflections of stamping dies and presses are usually not considered during the virtual die design and forming simulation phase. Active surfaces of stamping dies are only cambered based on previous experiences of tool types and stamping presses. However, almost all stamping dies and presses are unique, and available experiences are not valid for new sheet materials. This leads to component deviations and often several loops of tool adjustments are needed. Previously partners within the SMART Advanced Manufacturing research project CAMBER have developed advanced deflection measuring devices to quantify the elastic deformations of stamping presses. Using these measurements, cambering methodologies can be utilized in sheet metal forming simulations. In this paper numerical substitutive stamping press models are described which are capable of compensating for measured stamping press dynamics. The result show that a numerical compensated tool can improve the contact by over 80% compared to the corresponding contact without compensation., This work has been conducted under the CAMBER European research project; a project funded under the SMART EUREKA CLUSTER on Advanced Manufacturing programme, Camber

Previous research and experience points to many advantages if sheet metal forming is simulated with elastic dies. Some areas that are enabled by simulations with elastic dies are virtual spotting, improved digital twins, and improved production support. A promising method was selected from the literature, and after important modifications it is deemed to be fast and robust for simulating industrial sized dies. The method consists of meshing die solids with a coarse mesh to represent the structural behaviour of the die. The forming surfaces are then represented by a fine shell mesh connected to the solid mesh by tied contacts with an offset. With additional modifications to reduce solver time this yields a robust and flexible way of modelling sheet metal forming with elastic dies. There is an increase in preprocessing and simulation time compared to using rigid tools, but industrial dies can now be modeled within an hour and solved within a working day. It is also easy to update the model by replacing separate parts such as die solids or forming surfaces. One of the main criteria in favor of the selected approach is the realistic modeling of blankholder and cushion systems. In this paper simulations of three industrial cases are demonstrated: one case of virtual die spotting and two cases of production support. The three cases demonstrate the importance and potential of using elastic dies during virtual die tryout, production support, and for cases like digital twins and production control., Eureka Smart Camber

Sheet metal forming (SMF) simulations are traditionally carried out with rigid active forming surfaces. This means that the elasticity and dynamics of presses and die structures are ignored. The only geometries of the tools included in the simulations are the active forming surfaces. One reason for this simplification is the large amount of computational power that is required to solve finite element (FE) models that incorporates elastic stamping dies. Another reason is the lack of die CAD models before the later stages of stamping projects. Research during the last couple of decades indicated potential large benefits when including elastic dies in SMF simulations. For example, for simulating die try-out or for Digital Twins of presses and dies. Even though the need and potential benefits of elastic dies in simulations are well known it is not yet implemented on a wide scale. The main obstacles have been lacking data on presses and dies, long simulation times, and no standardized implementation in SMF software. This paper presents an overview of existing methods for SMF simulations with elastic dies and discuss their respective benefits and drawbacks. The survey of methods shows that simulation models with elastic tools will be needed for detailed analyses of forming operations and also for purposes like digital twins. On the other hand, simplified and robust models can be developed for non-FEA users to carry out simple one-step compensation of tool surfaces for virtual spotting purposes. The most promising and versatile method from the literature is selected, modified, and demonstrated for industrial sized dies., Eureka Smart Camber

Self-piercing riveting (SPR) has been widely used in automobile body jointing. However, the riveting process is prone to various forming quality failures, such as empty riveting, repeated riveting, substrate cracking, and other riveting defects. This paper combines deep learning algorithms to achieve non-contact monitoring of SPR forming quality. And a lightweight convolutional neural network with higher accuracy and less computational effort is designed. The ablation and comparative experiments results show that the lightweight convolutional neural network proposed in this paper achieves improved accuracy and reduced computational complexity. Compared with the original algorithm, the algorithm's accuracy in this paper is increased by 4.5[Formula: see text], and the recall is increased by 1.4[Formula: see text]. In addition, the amount of redundant parameters is reduced by 86.5[Formula: see text], and the amount of computation is reduced by 47.33[Formula: see text]. This method can effectively overcome the limitations of low efficiency, high work intensity, and easy leakage of manual visual inspection methods and provide a more efficient solution for monitoring the quality of SPR forming quality. © 2023. The Author(s).

Academic institutions are increasingly required to prepare future practitioners to face complex sustainability challenges. The need to foster the development of different skills, attitudes, and multidisciplinary collaboration raised the interest in alternative learning approaches. Game-based learning can be a tool to achieve a variety of desirable learning outcomes, including sustainability and collaborative attitudes change. We present a pilot study investigating the potential of a board game on sustainability risks and opportunities in product development and life cycle, performing a test with different student audiences. The paper discusses our results of the experiment, including a survey following the game, qualitative analysis of students' feedback, and observations during the game sessions. Additionally, we relate insights from students' reflections to CDIO learning objectives. We then illustrate lessons learned and the potential advantages of using the game compared to other teaching approaches and as a complementary tool. Finally, we propose future directions and recommendations for the use of the board game and game-based learning in sustainability education with different student audiences. © The Author(s), 2023. Published by Cambridge University Press.

The current efforts to reduce the carbon footprint throughout the chain in the automotive industry by increased use of recycled materials poses new challenges for materials production and their use. The increase of steel scrap fraction in the current primary steel making processes, used for producing steel sheet metal for automotive components, possibly affects the material properties variability beyond the limits observed in the materials produced today despite mitigating actions in steel production. In this paper material variability increase was modelled by selecting deterministic values outside the range of the material grade used to design and manufacture an automotive part. The values were selected from an experimental data set representing the cold rolled mild steels material class range. The effects were studied numerically on a reverse engineered model of an existing automotive part production process. It was found that the manufacturing feasibility in this particular case is mainly affected by the weighted average plastic strain ratio and less by the degree of planar anisotropy.

Sheet metal forming (SMF) simulations are traditionally carried out with rigid active forming surfaces. This means that the elasticity and dynamics of presses and die structures are ignored. The only geometries of the tools included in the simulations are the active forming surfaces. One reason for this simplification is the large amount of computational power that is required to solve finite element (FE) models that incorporates elastic stamping dies. Another reason is the lack of die CAD models before the later stages of stamping projects. Research during the last couple of decades indicated potential large benefits when including elastic dies in SMF simulations. For example, for simulating die try-out or for Digital Twins of presses and dies. Even though the need and potential benefits of elastic dies in simulations are well known it is not yet implemented on a wide scale. The main obstacles have been lacking data on presses and dies, long simulation times, and no standardized implementation in SMF software. This paper presents an overview of existing methods for SMF simulations with elastic dies and discuss their respective benefits and drawbacks. The survey of methods shows that simulation models with elastic tools will be needed for detailed analyses of forming operations and also for purposes like digital twins. On the other hand, simplified and robust models can be developed for non-FEA users to carry out simple one-step compensation of tool surfaces for virtual spotting purposes. The most promising and versatile method from the literature is selected, modified, and demonstrated for industrial sized dies., Eureka Smart Camber

The design of Product-Service Systems (PSS) is challenging due to the inherent complexities and the associated uncertainties. This challenge aggravates when the PSS being considered has a longer lifespan, is expected to encounter a dynamic context, and integrates many novel technologies. From systems engineering literature, one of the measures for mitigating the risks associated with the uncertainties is incorporating means in the system to change internally as a response to change externally. Such systems are referred to as value-robust systems, and their development largely relies on Tradespace exploration and synthesis. Tradespace exploration and synthesis can be challenging and a time-consuming task due to dimensionality. In this light, this paper aims to present an approach that enables the population of the Tradespace and then, supports the synthesis of such a Tradespace using a clustering algorithm for support changeability quantification in PSS. The proposed method is also implemented on a demonstrative case from the construction machinery industry., TRUST-SOS, eTwin

The paper presents an approach where the iterative replication of Discrete Event Simulations on future operational scenarios is used to derive data-driven design merit functions. The presented contribution proposes an extension of the EVOKE (Early Value Oriented Design Exploration with Knowledge Maturity) approach determining when and how the experience-based judgment about maximization, minimization, optimization, and avoidance functions, correlating value drivers and quantified objectives, can be substituted by data-driven mathematical functions obtained by scenarios simulations. The approach is described through a simplified case concerning the development of autonomous electric vehicles to complement the public transport system in the city of Karlskrona in Sweden. The consideration of value drivers and quantified objectives presented is meant to support a preliminary screening of potential design configurations to support the definition of high-level product and system-related functional requirements, to be run before a more detailed conceptual design analysis. © The Author(s), 2023. Published by Cambridge University Press., TRUST-SOS

Cost and time for stamping die tryouts are significant within the car industry. A major contributing factor is that elastic deflections of stamping dies and presses are usually not considered during the virtual die design and forming simulation phase. Active surfaces of stamping dies are only cambered based on previous experiences of tool types and stamping presses. However, almost all stamping dies and presses are unique, and available experiences are not valid for new sheet materials. This leads to component deviations and often several loops of tool adjustments are needed. Previously partners within the SMART Advanced Manufacturing research project CAMBER have developed advanced deflection measuring devices to quantify the elastic deformations of stamping presses. Using these measurements, cambering methodologies can be utilized in sheet metal forming simulations. In this paper numerical substitutive stamping press models are described which are capable of compensating for measured stamping press dynamics. The result show that a numerical compensated tool can improve the contact by over 80% compared to the corresponding contact without compensation., This work has been conducted under the CAMBER European research project; a project funded under the SMART EUREKA CLUSTER on Advanced Manufacturing programme, Camber

Previous research and experience points to many advantages if sheet metal forming is simulated with elastic dies. Some areas that are enabled by simulations with elastic dies are virtual spotting, improved digital twins, and improved production support. A promising method was selected from the literature, and after important modifications it is deemed to be fast and robust for simulating industrial sized dies. The method consists of meshing die solids with a coarse mesh to represent the structural behaviour of the die. The forming surfaces are then represented by a fine shell mesh connected to the solid mesh by tied contacts with an offset. With additional modifications to reduce solver time this yields a robust and flexible way of modelling sheet metal forming with elastic dies. There is an increase in preprocessing and simulation time compared to using rigid tools, but industrial dies can now be modeled within an hour and solved within a working day. It is also easy to update the model by replacing separate parts such as die solids or forming surfaces. One of the main criteria in favor of the selected approach is the realistic modeling of blankholder and cushion systems. In this paper simulations of three industrial cases are demonstrated: one case of virtual die spotting and two cases of production support. The three cases demonstrate the importance and potential of using elastic dies during virtual die tryout, production support, and for cases like digital twins and production control., Eureka Smart Camber

To improve the accuracy of forming simulations, advanced friction models are increasingly used in the industry. These models account for the physical properties of the sheet, tool and lubrication and describe the tribological conditions during the forming operation. One of the main influencing factors on the tribology system, and therefore the friction coefficient, is the surface roughness of the tools. Until now, it is often assumed all tools have the same uniform surface roughness. In reality however, the tool might have different surface conditions dependent on the type and location of the tool. That is, the blank holder might be differently polished than the punch, and sharp radii might have a different tool roughness compared to flat areas. This paper investigates a significant number of tool measurements from different tool sets from Volvo Cars, and quantifies the effect of local surface conditions on product quality.

Self-piercing riveting (SPR) has been widely used in automobile body jointing. However, the riveting process is prone to various forming quality failures, such as empty riveting, repeated riveting, substrate cracking, and other riveting defects. This paper combines deep learning algorithms to achieve non-contact monitoring of SPR forming quality. And a lightweight convolutional neural network with higher accuracy and less computational effort is designed. The ablation and comparative experiments results show that the lightweight convolutional neural network proposed in this paper achieves improved accuracy and reduced computational complexity. Compared with the original algorithm, the algorithm's accuracy in this paper is increased by 4.5[Formula: see text], and the recall is increased by 1.4[Formula: see text]. In addition, the amount of redundant parameters is reduced by 86.5[Formula: see text], and the amount of computation is reduced by 47.33[Formula: see text]. This method can effectively overcome the limitations of low efficiency, high work intensity, and easy leakage of manual visual inspection methods and provide a more efficient solution for monitoring the quality of SPR forming quality. © 2023. The Author(s).

The current efforts to reduce the carbon footprint throughout the chain in the automotive industry by increased use of recycled materials poses new challenges for materials production and their use. The increase of steel scrap fraction in the current primary steel making processes, used for producing steel sheet metal for automotive components, possibly affects the material properties variability beyond the limits observed in the materials produced today despite mitigating actions in steel production. In this paper material variability increase was modelled by selecting deterministic values outside the range of the material grade used to design and manufacture an automotive part. The values were selected from an experimental data set representing the cold rolled mild steels material class range. The effects were studied numerically on a reverse engineered model of an existing automotive part production process. It was found that the manufacturing feasibility in this particular case is mainly affected by the weighted average plastic strain ratio and less by the degree of planar anisotropy.

The paper presents an approach where the iterative replication of Discrete Event Simulations on future operational scenarios is used to derive data-driven design merit functions. The presented contribution proposes an extension of the EVOKE (Early Value Oriented Design Exploration with Knowledge Maturity) approach determining when and how the experience-based judgment about maximization, minimization, optimization, and avoidance functions, correlating value drivers and quantified objectives, can be substituted by data-driven mathematical functions obtained by scenarios simulations. The approach is described through a simplified case concerning the development of autonomous electric vehicles to complement the public transport system in the city of Karlskrona in Sweden. The consideration of value drivers and quantified objectives presented is meant to support a preliminary screening of potential design configurations to support the definition of high-level product and system-related functional requirements, to be run before a more detailed conceptual design analysis. © The Author(s), 2023. Published by Cambridge University Press., TRUST-SOS

The design of Product-Service Systems (PSS) is challenging due to the inherent complexities and the associated uncertainties. This challenge aggravates when the PSS being considered has a longer lifespan, is expected to encounter a dynamic context, and integrates many novel technologies. From systems engineering literature, one of the measures for mitigating the risks associated with the uncertainties is incorporating means in the system to change internally as a response to change externally. Such systems are referred to as value-robust systems, and their development largely relies on Tradespace exploration and synthesis. Tradespace exploration and synthesis can be challenging and a time-consuming task due to dimensionality. In this light, this paper aims to present an approach that enables the population of the Tradespace and then, supports the synthesis of such a Tradespace using a clustering algorithm for support changeability quantification in PSS. The proposed method is also implemented on a demonstrative case from the construction machinery industry., TRUST-SOS, eTwin

Cost and time for stamping die tryouts are significant within the car industry. A major contributing factor is that elastic deflections of stamping dies and presses are usually not considered during the virtual die design and forming simulation phase. Active surfaces of stamping dies are only cambered based on previous experiences of tool types and stamping presses. However, almost all stamping dies and presses are unique, and available experiences are not valid for new sheet materials. This leads to component deviations and often several loops of tool adjustments are needed. Previously partners within the SMART Advanced Manufacturing research project CAMBER have developed advanced deflection measuring devices to quantify the elastic deformations of stamping presses. Using these measurements, cambering methodologies can be utilized in sheet metal forming simulations. In this paper numerical substitutive stamping press models are described which are capable of compensating for measured stamping press dynamics. The result show that a numerical compensated tool can improve the contact by over 80% compared to the corresponding contact without compensation., This work has been conducted under the CAMBER European research project; a project funded under the SMART EUREKA CLUSTER on Advanced Manufacturing programme, Camber

To improve the accuracy of forming simulations, advanced friction models are increasingly used in the industry. These models account for the physical properties of the sheet, tool and lubrication and describe the tribological conditions during the forming operation. One of the main influencing factors on the tribology system, and therefore the friction coefficient, is the surface roughness of the tools. Until now, it is often assumed all tools have the same uniform surface roughness. In reality however, the tool might have different surface conditions dependent on the type and location of the tool. That is, the blank holder might be differently polished than the punch, and sharp radii might have a different tool roughness compared to flat areas. This paper investigates a significant number of tool measurements from different tool sets from Volvo Cars, and quantifies the effect of local surface conditions on product quality.

Sheet metal forming (SMF) simulations are traditionally carried out with rigid active forming surfaces. This means that the elasticity and dynamics of presses and die structures are ignored. The only geometries of the tools included in the simulations are the active forming surfaces. One reason for this simplification is the large amount of computational power that is required to solve finite element (FE) models that incorporates elastic stamping dies. Another reason is the lack of die CAD models before the later stages of stamping projects. Research during the last couple of decades indicated potential large benefits when including elastic dies in SMF simulations. For example, for simulating die try-out or for Digital Twins of presses and dies. Even though the need and potential benefits of elastic dies in simulations are well known it is not yet implemented on a wide scale. The main obstacles have been lacking data on presses and dies, long simulation times, and no standardized implementation in SMF software. This paper presents an overview of existing methods for SMF simulations with elastic dies and discuss their respective benefits and drawbacks. The survey of methods shows that simulation models with elastic tools will be needed for detailed analyses of forming operations and also for purposes like digital twins. On the other hand, simplified and robust models can be developed for non-FEA users to carry out simple one-step compensation of tool surfaces for virtual spotting purposes. The most promising and versatile method from the literature is selected, modified, and demonstrated for industrial sized dies., Eureka Smart Camber

Previous research and experience points to many advantages if sheet metal forming is simulated with elastic dies. Some areas that are enabled by simulations with elastic dies are virtual spotting, improved digital twins, and improved production support. A promising method was selected from the literature, and after important modifications it is deemed to be fast and robust for simulating industrial sized dies. The method consists of meshing die solids with a coarse mesh to represent the structural behaviour of the die. The forming surfaces are then represented by a fine shell mesh connected to the solid mesh by tied contacts with an offset. With additional modifications to reduce solver time this yields a robust and flexible way of modelling sheet metal forming with elastic dies. There is an increase in preprocessing and simulation time compared to using rigid tools, but industrial dies can now be modeled within an hour and solved within a working day. It is also easy to update the model by replacing separate parts such as die solids or forming surfaces. One of the main criteria in favor of the selected approach is the realistic modeling of blankholder and cushion systems. In this paper simulations of three industrial cases are demonstrated: one case of virtual die spotting and two cases of production support. The three cases demonstrate the importance and potential of using elastic dies during virtual die tryout, production support, and for cases like digital twins and production control., Eureka Smart Camber

Academic institutions are increasingly required to prepare future practitioners to face complex sustainability challenges. The need to foster the development of different skills, attitudes, and multidisciplinary collaboration raised the interest in alternative learning approaches. Game-based learning can be a tool to achieve a variety of desirable learning outcomes, including sustainability and collaborative attitudes change. We present a pilot study investigating the potential of a board game on sustainability risks and opportunities in product development and life cycle, performing a test with different student audiences. The paper discusses our results of the experiment, including a survey following the game, qualitative analysis of students' feedback, and observations during the game sessions. Additionally, we relate insights from students' reflections to CDIO learning objectives. We then illustrate lessons learned and the potential advantages of using the game compared to other teaching approaches and as a complementary tool. Finally, we propose future directions and recommendations for the use of the board game and game-based learning in sustainability education with different student audiences. © The Author(s), 2023. Published by Cambridge University Press.

The current efforts to reduce the carbon footprint throughout the chain in the automotive industry by increased use of recycled materials poses new challenges for materials production and their use. The increase of steel scrap fraction in the current primary steel making processes, used for producing steel sheet metal for automotive components, possibly affects the material properties variability beyond the limits observed in the materials produced today despite mitigating actions in steel production. In this paper material variability increase was modelled by selecting deterministic values outside the range of the material grade used to design and manufacture an automotive part. The values were selected from an experimental data set representing the cold rolled mild steels material class range. The effects were studied numerically on a reverse engineered model of an existing automotive part production process. It was found that the manufacturing feasibility in this particular case is mainly affected by the weighted average plastic strain ratio and less by the degree of planar anisotropy.