Current trends in product development are digital engineering, the increasing use of assistance tools based on artificial intelligence and in general shorter product lifecycles. These trends and new tools strongly rely on available data and will irreversibly change established product development processes. One example for such a new data driven tool is the plausibility check of linear finite element simulations with Convolutional Neural Networks (CNN). This tool is capable of determining whether new simulation results are plausible or non-plausible according to numeric input data. The digitalization and the increased use of data driven tools employing algorithms known from Artificial Intelligence also shifts the roles of many involved engineers. This paper describes and highlights this transition from current product development processes to a data driven / simulation driven product development process. Particularly, the shifts and changes of different roles and domains are illustrated and an example for changing roles in the design and simulation department is described. Furthermore, required adjustments in the design process are derived and compared to the current status.

This paper analyzes the potential of crossdisciplinary collaboration in the methodical development of Modular Design by harmonization asynchronous mechatronic system structures. Subsystem boundaries in multidisciplinary development processes are set disciplinespecific, resulting in inconsistencies in module fitting. Based on a case study, harmonization of disciplines is elaborated as a solution. This aligns discipline structures and reduces effects on the variety in system structures.This implementation shows support for modular design and enables an integrated view as a systems-in-system.

Artificial intelligence (AI) is seen as a great opportunity to secure future competitiveness in many corporate sectors. Potential for its use also exists in product development (PD) activities due to the amount of data generated and processed. Nevertheless, there are problems in applying the technology. This paper addresses current challenges based on a literature review, considering three disciplines that are necessary for the scope of this paper as a minimum: AI itself, information technology infrastructures (ITI) in context of digital transformation (DT), and PD as an application area. Building on the basic considerations of the state of the art, a link between the domains is established by outlining a possible reference framework towards the utilization of AI applications in PD. This enables an expanded interdisciplinary understanding. Key obstacles appear specifically to be difficult collaboration conditions between the disciplines of PD and AI applications development due to communication problems. Reasons for this include:

• – Meta models of PD do not provide a sufficient information base

• – Lack of standardized process models for the deployment of AI

Standards are an important source of knowledge in product development. Due to the increasing digitization of the product development process, standard development organizations aim to develop machine-actionable standards that automatically enforce operations in output devices. However, the current representation format in PDF or XML does not meet the requirements of machine-actionable standards. This paper examines existing approaches towards the representation of XML data in knowledge graphs and their transferability towards the domain of digital standards. Based on these approaches, the paper aims to develop and validate a concept for transferring standard content from XML format to a graph-based representation, using the example of formulas. For this purpose, a concept for the automatic identification, extraction and modeling of formulas will be presented. Afterwards, the concept is validated using the example of DIN ISO 281 whereas a chatbot application serves as conversational user interface. It is proven, that knowledge graphs are suitable for the representation of machine-actionable standard content. Future work will investigate the abstraction towards a general approach as well as further information objects of standards.

Design for AM (DfAM) requires the definition of Design Actions (DAs) to optimize AM manufacturing processes. However, AM understanding is still very blurred. Often designers are challenged by selecting the right design parameters. A method to list and collect DfAM DAs is currently missing. The paper aims at providing a framework to collect DfAM DAs according to a developed ontology to create databases (DBs). DBs were tested with two real case studies and geometric features to improve identified. Future developments aim at widening the database to provide all-around support for AM processes.

Process engineering industries are now facing growing economic pressure and societies' demands to improve their production technologies and equipment, making them more efficient and environmentally friendly. However unexpected additional technical and ecological drawbacks may appear as negative side effects of the new environmentally-friendly technologies. Thus, in their efforts to intensify upstream and downstream processes, industrial companies require a systematic aid to avoid compromising of ecological impact. The paper conceptualises a comprehensive approach for eco-innovation and eco- design in process engineering. The approach combines the advantages of Process Intensification as Knowledge-Based Engineering (KBE), inventive tools of Knowledge-Based Innovation (KBI), and main principles and best-practices of Eco-Design and Sustainable Manufacturing. It includes a correlation matrix for identification of eco-engineering contradictions and a process mapping technique for problem definition, database of Process Intensification methods and equipment, as well as a set of strongest inventive operators for eco-ideation.

Automotive systems are changing rapidly from purely mechanical to smart, programmable assistants. These systems react and respond to the driving environment and communicate with other subsystems for better driver support and safety. However, instead of supporting, the complexity of such systems can result in a stressful experience for the driver, adding to the workload. Hence, a poorly designed system, from a usability and user experience perspective, can lead to reduced usage or even ignorance of the provided functionalities, especially concerning Adaptive Driver Assistance Systems.

In this paper, the authors propose a combined design approach for user behavior evaluation of such systems. At the core of the design is a mixed methods approach, where objective data, which is automatically collected in vehicles, is augmented with subjective data, which is gathered through in- depth interviews with end-users. The aim of the proposed methodology design is to improve current practices on user behavior evaluation, achieve a deeper understanding of driver's behavior, and improve the validity and rigor of the named results.

Complex technological artefacts are often decomposed into smaller components to keep their design manageable. The resulting challenge is to coordinate decisions that involve multiple components and to design components such that high-level targets are met. Analytical Target Cascading (ATC) is an analytical coordination method for the optimization of decomposed systems, which we aim to incorporate in systems engineering design process. To this extent, we relate the domain of engineering optimization to the domain of requirements engineering, and propose a method that constructs an ATC problem from functional specifications and requirements written in the newly developed Elephant Specification Language. The proposed method is demonstrated in the two-level design of an automotive powertrain. This contribution is a step towards design automation and is expected to increase the usability of decomposed optimization techniques.

Innovation portfolio management (IPM) aims at selecting ideas with regard to their potential for innovation and measuring them considering customer and business value. The evaluation of benefits and risk is especially challenging for disruptive innovation (DI) due to their characteristics such as low comparability to existing technologies and uncertain customer reactions. This paper highlights the lack of approaches to managing DI in IPM and addresses it through a framework that expands the understanding of value-orientation in IPM, allowing for the inclusion of DI.

The modeling of battery electric vehicles (BEVs) still represents a challenge for vehicle manufacturers. The installation of the new types of components needed for BEVs gives rise to uncertainties in the quantification of parameters like the vehicle's weight. Indeed, vehicle weight plays a key role, since it has a drastic effect on the vehicle's range, which is an important selling point for BEVs. Uncertainties in weight estimation create weight fluctuations during the early development phase and the need to resize components like the electric machine or battery. This in turn affects the components' volume and weight. However, such resizing can also lead to component collision and unfeasibility of the vehicle architecture. To solve this problem and to support concept engineers during the early development phase, an iterative approach is required that is capable of estimating weight and volume fluctuations in the relevant components. The approach should also consider the geometrical interdependencies of the components, to ensure that no collisions occur between them. Taking the gearbox as an example application, this paper presents a novel approach that satisfies these requirements.

Generative design (GD) algorithms is a fast growing field. From the point of view of Design Science, this fast growth leads to wonder what exactly is 'generated' by GD algorithms and how? In the last decades, advances in design theory enabled to establish conditions and operators that characterize design generativity. Thus, it is now possible to study GD algorithms with the lenses of Design Science in order to reach a deeper and unified understanding of their generative techniques, their differences and, if possible, find new paths for improving their generativity.

In this paper, first, we rely on C-K ttheory to build a canonical model of GD, based independent of the field of application of the algorithm. This model shows that GD is generative if and only if it builds, not one single artefact, but a “topology of artefacts” that allows for design constructability, covering strategies, and functional comparability of designs. Second, we use the canonical model to compare four well documented and most advanced types of GD algorithms. From these cases, it appears that generating a topology enables the analyses of interdependences and the design of resilience.

Electric Vehicles (EVs) are very quite at low speed, which can be hazardous for pedestrians. It is necessary to add warning sounds but this can represent an annoyance if they are poorly designed. On the other hand, they can be not enough detectable because of the masking effect due to the background noise. In this paper, we propose a method for the design of EV sounds that takes into account in the same time detectability and unpleasantness. It is based on user tests and implements Interactive Genetic Algorithms (IGA) for the optimization of the sounds. Synthesized EV sounds, based on additive synthesis and filtering, are proposed to a set of participants during a hearing test. An experimental protocol is proposed for the assessment of the detectability and the unpleasantness of the EV sounds. After the convergence of the method, sounds obtained with the IGA are compared to different sound design proposals. Results show that the quality of the sounds designed by the IGA method is significantly higher than the design proposals, validating the relevance of the approach.

Engineering changes are necessary to stay competitive, unavoidable and occur more frequently with increased product complexity. Currently, scheduling of engineering changes into production and supply chain is a manual process. With new possibilities in the field of artificial intelligence, this publication presents the vision of a flexible multi-agent system with four agents and a single shared database. By autonomously scheduling changes and predicting KPI impacts of implementation dates, the agent-system provides additional capacity and decision-making support to the organisation.

SAR provides an unobtrusive implementation of AR and enables multiple stakeholders to observe and interact with an augmented physical model. This is synonymous with co-design activities and hence, there is a potential for SAR to have a significant impact in the way design teams may set-up and run their co-design activities in the future. Whilst there are a growing number of studies which apply SAR to design activities, few studies exist that examine a particular element of a design activity in a controlled manner. This paper will begin to fill this gap through the controlled study of SAR and its effects on the communication between participants of a co-design activity. To do so the paper compares a controlled design session, using more traditional methods of design representations (3D models on a screen), to sessions run using SAR. The sessions are then analysed to gather information on the gestures used by the participants as well as the overall efficiency of the participants at completing the set design task. The paper concludes that the data gathered tentatively supports a link between the use of SAR and improved communication between design session participants.

As Industry 4.0 will greatly challenge employee mental workload (MWL), research on objective wearable MWL-monitoring is in high demand. However, numerous research lines validating such technology might become redundant when employees eventually object to its implementation. In a pilot study, we manipulated two ways in which employees might perceive MWL-monitoring initiatives. We found that framing the technology in terms of serving intrinsic goals (e.g., improving health) together with an autonomy-supportive context (e.g., allowing discussion) yields higher user acceptability when compared to framing in terms of extrinsic goals (e.g., increasing productivity) together with a controlling context (e.g., mandating use). User acceptability still panned out neutral in case of the former, however - feeding into our own and suggested future work.

Due to increasing cost pressure, companies are expanding their product range and therefore increasingly offering so-called product-service systems (PSS) to increase sales. At the same time, PSS can also contribute to an increase in variety-induced complexity in the company, which ultimately further increases cost pressure. In this article, the causes and effects of variety-induced complexity through products, services and PSS are analysed and the need for a new PSS design method is then demonstrated. Finally, a new framework is presented that enables the design of variety-oriented PSS.

Bio-inspired design (BID) involves generating innovative ideas for engineering design by drawing inspiration from natural biological phenomena and systems, using a form of design-by-analogy. Despite its many successes, BID approaches encounter research challenges including unstructured data and existing models that hinder comprehension and processing, limited focus on finding biological knowledge compared to defined problems, and insufficient guidance of the ideation process with algorithms. This paper proposes a knowledge-based approach to address the challenges. The approach involves transforming unstructured data into structured knowledge, including information about natural sources, their benefits, and applications. The structured knowledge is then used to construct a semantic network, enabling designers to retrieve information for BID in two ways. Furthermore, a three-step ideation method is developed to encourage divergent thinking and explore additional potential solutions by drawing inspiration and utilizing knowledge. The knowledge-based BID approach is implemented as a tool and design cases are conducted to illustrate the process of applying this tool for BID.

As catalysts for product innovation and product development, different approaches for biologically inspired design (BID) are exciting options. However, while general BID theory require a focus on single functions, real world products are characterized by performing multiple functions. The development of an anterior eye-chamber model is used to showcase the issue.

In a systematic literature review (SLR), state-of-the-art methodologies, methods and tools BID practice are discovered and the current state of multi-functionality in BID are assessed.

The SLR revealed 18 contributions with 8 BID methodologies and 12 stage-specific BID tools (of which 50% addressed the solution search phase) in addition to 5 papers addressing multi-functionality in BID. At present multi-functionality in BID is only treated in a limited set of papers. While designers interested in BID are advised to discover multi-functional analogies, the present approach to handling multi-functional problems in BID suggest functional decomposition and multiple BID efforts. Therefore, the development of design support for handling multi-functional problems, including tools for problem analysis are needed.

Design is widely understood as a domain-independent notion, comprising any activity concerned with creating artefacts. This paper shows that models can be viewed as artefacts, and that the design of models resembles the design of artefacts in other domains. The function-behaviour-structure (FBS) ontology of design is applied to models, mapping generic characteristics of models derived from literature on modelling onto basic, design-ontological categories. An example of model design, namely the CRISP-DM model for designing data mining models, is analysed and compared with models of designing in other domains (systems engineering, mechanical engineering, software engineering, and service design). The results show that there are fundamental commonalities but also differences, revealing the need for further research in developing a theory of model design.

Serious games (SGs) as a new educational format have gained interest among many scholars from diverse fields. SGs seem to be useful tools for teaching innovation processes (IP) as they guarantee intrinsic motivation and provide situated learning. So far, there is no guideline on designing IP games and lowering their development time while ensuring their effectiveness. To fill this gap, we should first analyse the existing IP games with evaluation methods and synthesise their commonalities. Numerous methods have been put forward in the literature to assess digital SGs; however, most of the SGs for IP are board games. That is why we explore in this paper the use of Serious Game Design Assessment (SGDA) framework to analyse IP board games. According to the case study on an open innovation board game, we suggest applying this method to examine the a priori relevance of game elements (components that make up a game system). Moreover, we make recommendations to complement the SGDA framework with Game and Learning Mechanics, and real-world information. This contribution should help designers transform traditional educational supports into serious board games for teaching IP.