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.

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.

Analysing verbal data produced during the design activity is helpful to gain a better understanding of design creativity. To understand exchange of information in terms of creative outcomes, a semantic analysis approach was used to measure the semantic content of communications between students and teachers. The goal was to use this tool to analyse design conversations, and to investigate their relation to design creativity, assessed in terms of originality, usability, feasibility, aesthetics, elaboration, overall value and overall creativity. Abstraction, Polysemy, Information Content and Semantic Similarity were employed to explore 35 design conversations from the DTRS10 dataset. Main findings suggest that a significant relationship exists between Information Content and Originality, and between Information Content and Overall creativity of the produced design outcomes. Significant relations were also found between Abstraction, Polysemy, Information Content, and Feasibility, as well as between Semantic Similarity and Overall Value of the outcomes. Implications for the use of semantic measures for encouraging creativity in the design studio are discussed.

In the last few years there has been a noticeable change in the development of headlamp systems in the field of vehicle lighting technology. Starting with adaptive front-lighting systems via Matrix LED systems, high-resolution headlamps will provide more safety in road traffic in the near future.

For the implementation of high-resolution headlamps various spatial light modulators and light generating technologies can be applied. The emitted light of the light source is directed via an illumination optics onto the modulator and a projection optics is applied to image the spatial light modulator into the traffic area. The formerly mechatronic systems are thus increasingly become opto- mechatronic systems. Therefore, the optic design must be taken into account in the early development phase of these systems.

In this paper we present a methodical approach to describe the optic design for optomechatronic systems. This approach can be used to develop efficient and high-intensity optomechatronic systems using various spatial light modulators and light generating technologies. Conclusively we demonstrate an exemplary application of the methodology on a high-resolution projection module.

We study a DevOps software development process for a Product-Service System (PSS) using a design structure matrix (DSM) representation. We find unique features such as nested, planned iterations at differing rates which are not evident in conventional engineering development projects. We describe the impact of integrating ongoing operations into a development process and identify some of the enablers that lead to adoption of a DevOps process. We conclude by discussing the implications of our findings and raise questions for further research.

The observation of designers' behaviour in collaborative design activities and the analysis of protocols improved the understanding of how novel ideas emerge, what occurs among designers and, indirectly, what methods have a good impact on the outcomes. Yet, protocol analysis requires recording the design sessions, often in a simulated environment, thus introducing a bias in the observation. Moreover, the analysis takes up to 1000 times the duration of the observed design session. These limitations definitely hinder the scalability of this practice to large experiments in real operational environments.

This paper investigates the possibility to use the data collected in log files, automatically recorded during collaborative design sessions assisted by an ICT design support tool, as a means to extract relevant information about the design process and ultimately to infer insights about co-designers' cognition during the session. In this perspective, the paper proposes a set of metrics tailored to an Augmented Reality-based collaborative design tool. The study has been carried about by processing the data collected in 5 real case studies conducted in three different design companies.

For the development of intelligent technical systems, Systems Engineering and Solution Patterns are the guarantee for success. In order to avoid cost-intensive iterations, the documentation and reuse of solution knowledge is addressed during the systems design. Using an interdisciplinary specification technique, a uniform structuring of Solution Patterns as well as the composition in a multidimensional knowledge space takes place. This is the basis of an associated systematics for a solution pattern-based system design of mechatronic systems, which is validated by two cooperating DeltaRobots.

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.

Aluminum foam sandwich (AFS) is an innovative sandwich material for designing lighter products and has many advantages such as high stiffness and high mechanical-energy absorption capacity. Although AFS is ready for series production, the number of use cases is low. A survey was carried out in order to identify the obstacles in the application of aluminum foam sandwich. This paper presents the results of the survey, derives the demand for a support method for designing with aluminum foam sandwich and shows various support options to simplify the application of the material.

Companies struggle with identifying relevant sustainability aspects strategically, assessing alternative solutions quantitatively, and making trade-offs. This paper reports results from a prescriptive study with an aerospace company, and presents the Sustainability Criteria And product life-cycle Data Simulation (SCADS) approach. Based on strategic integration of sustainability indicators, this approach aims to enable visualisation and comparison of the sustainability implications of different concepts in early design phases of product development.

By means of additive manufacturing, especially laser powder bed fusion, particle dampers can be integrated locally into structural components and thus significantly reduce component vibrations. However, detailed design recommendations for additively manufactured particle dampers do not yet exist. The research question in this paper is: How can the effect of particle damping be described as a function of excitation force, cavity width and cavity length? For beams made of AlSi10Mg, it is shown that a powder-filled cavity of 2.5% to 5% is sufficient to increase the damping by more than x10.

Artificial Intelligence offers a wide variety of capabilities that can potentially address people's needs and desires in their specific contexts. This pilot study presents a collaborative method using a deck of AI cards tested with 58 production, AI, and information science students, and experts from an accessible media agency. The results suggest that, with the support of the method and AI cards, participants can ideate and reach conceptual AI solutions. Such conceptualisations can contribute to a more inclusive integration of AI solutions in society.

This paper shows how designers of IoT-enabled products can assess the environmental impacts associated with the user behaviour and the service system around the product. High-quality secondary data and a user-behaviour survey were able to highlight critical aspects of a smart fridge's design. A streamlined LCA looked at just the in-use phase of the product within 4 PSS scenarios. The system included: the effects on the food waste; grocery shopping methods; fridge door openings; and how the users interact with the smart fridge features. The results show that a smart fridge as within a PSS can reduce the impact on the environment (GWP of 21,700 kg CO2-eq within the ‘average PSS scenario’ and GWP of 23,100 kg CO2-eq for the normal fridge with ‘typical scenario’). The product's increased emissions are counteracted by the reduction in GWP due to: reduction in food waste; and shifts from brick-and-mortar grocery shopping to e-commerce. Therefore some of the critical aspects of the product's design that are most influential on the environmental impact of an IoT fridge are: the design of the web-browsing capability; and the use-by date tracking system.

Deep learning methods have been applied to randomly generate images, such as in fashion, furniture design. To date, consideration of human aspects which play a vital role in a design process has not been given significant attention in deep learning approaches. In this paper, results are reported from a human- in-the-loop design method where brain EEG signals are used to capture preferable design features. In the framework developed, an encoder extracting EEG features from raw signals recorded from subjects when viewing images from ImageNet are learned. Secondly, a GAN model is trained conditioned on the encoded EEG features to generate design images. Thirdly, the trained model is used to generate design images from a person's EEG measured brain activity in the cognitive process of thinking about a design. To verify the proposed method, a case study is presented following the proposed approach. The results indicate that the method can generate preferred designs styles guided by the preference related brain signals. In addition, this method could also help improve communication between designers and clients where clients might not be able to express design requests clearly.

Product Service Systems (PSS) are increasingly complex and collaborative. For instance, manufacturing companies, service providers, and other companies collaborate and jointly develop and operate a PSS (ex: smart grid), where its constituent elements are managed and operated independently. Managerial independence and operational independence are commonly considered key characteristics of a System of Systems (SoS). Hence, a collaborative PSS exhibits System of Systems (SoSs) characteristics. These systems have previously been introduced as Product Service Systems of Systems (PSSoSs). In this paper, we propose to identify relevant uncertainties in the PSSoS design process. For this purpose, we go beyond the PSSoS concept definition and propose a comprehensive framework for PSS and PSSoS characterization. Moreover, based on both a literature review and an industrial diagnosis, we identify PSSoSs-specific design uncertainties.

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.

Design methods are seldom used in engineering design practice. The presented study aims at finding the alternative strategies for situations with a need for methodological support. Semi-structured interviews were conducted with ten experienced design engineers to identify causes of and strategies for those situations. Three strategy clusters could be identified: generating information, experience and method application. As the individual's and the team's experience compete with the application of methods, they are seen as reasons for a lack of method application.

In order to meet an increasing internal variety, a solution can be the modularization of products. To motivate modularization projects throughout all phases of life, different effects of modular product families were collected in previous works on a literature basis. In this paper, a validation concept is presented, which will investigate these effects by using statements directly from industry representatives. In particular, the industry background is included in the evaluation to generate a more differentiated overall picture of impacts of modular product families.

In modular products conflicting objectives may occur. This leads to characteristics as component-dependent oversizing and undersizing as well as increased complexity of the interfaces. These conflicts can be resolved using the potentials of AM processes. For the best use possible, the potentials are systematically considered in the early design phases as part of an extended procedure. The extended procedure improves the benefit-effort ratio of modular respectively individual products and a further optimization of the product architecture and consideration of synergy effects is achieved.

Policymakers have attempted to preemptively address the concern of ethical issues with the regulation of automated vehicles. Unfortunately, both policymakers and designers of these technologies struggle to articulate ethical issues and their resolution. We propose a framework that engineers and designers of automated technologies can apply that allows them to identify and resolve ethical tensions within the design task. We demonstrate the practicability of the framework to the engineering design process through a human-subject study where engineers applied the framework in a workshop.