This study examines daily emotion regulation strategies of young adults aged 20-30 and proposes design opportunities for enhancing emotional well-being. Research with 29 participants revealed a preference for behavioral strategies (73.4%) over cognitive strategies (26.6%), particularly “Seek pleasure or relaxation” strategies. Significant differences were observed between positive and negative emotional contexts. Four main categories of emotion regulation strategies were identified: Disengagement, Seek pleasure or relaxation, Reallocate resources, and Engagement. Design opportunities were proposed based on these findings. This study enhances understanding of young adults’ emotion regulation and offers design strategies for product development. Future research should validate these strategies and explore personalized approaches, considering long-term impacts and ethics.

Verification and Validation (V&V) are essential processes in engineering Cyber-Physical Systems. However, the role of V&V engineers is often not given sufficient attention. Based on a systematic literature analysis and practical observations, a four-step method for Test-oriented Resilient Requirements Engineering (ToRRE) is developed. The steps are planning V&V, executing V&V activities, documenting V&V activities and analyzing results of V&V activities. Applying ToRRE ensures continuous information flow and traceability. Engineers are enabled to analyze requirements using engineering artifacts connected through Model-Based Systems Engineering. Adopting methods for Model-Based Effect Chain analysis to evaluated test cases and test scenarios, conclusions on requirements engineering and change management are enabled. The method is evaluated in an EU research project.

Laser Powder Bed Fusion (LPBF) enables complex metal components for the space industry. However, as-built surface roughness affects material properties and is closely linked to design geometry. As computer-aided design tools struggle to model roughness accurately, this study explores Additive Manufacturing Design Artefacts (AMDAs) to investigate design-related roughness and its impact on fatigue performance. A space industry case study using AMDAs to replicate a 4 mm unsupported roof radius of a rocket engine component found fatigue performance reductions of 88% in horizontal builds and 65% in vertical builds compared to machined surfaces. Microstructural analysis confirmed the influence of roughness and grain structure on fatigue behaviour. Findings highlight how AMDAs provide design-specific insights and support engineers in investigating uncertainties.

The reprocessing of used products within a circular factory relies on instance-individual design decisions. This requires specific design knowledge (SDK) on relations between embodiment and functional behavior. However, existing approaches do not model SDK in a way that supports product reuse to fulfill the functional requirements of new product generations. This paper presents a hypothesis-based modeling approach on building and structuring qualitative SDK. Drawing on elements of existing product models, the approach yields three outcomes - a function-related structure, design hypotheses, and the assignment of testing strategies. A case study of an angle grinder demonstrates how the approach addresses the requirements of a circular factory by facilitating targeted SDK buildup, ensuring comprehensive documentation, and preparing the quantification of knowledge.

This study examines generative design (GD) within mass personalization (MP) workflows, using custom dental implant abutments as a case study. Selected for their complex functional requirements, a parametric model developed in Rhino3D and Grasshopper, augmented with Wallacei for optimization, was compared to conventional industrial CAD approaches. GD automates design iterations and handles multi-objective optimizations, with performance improvements achieved by segmenting the parametric model. However, GD requires precise parameterization, posing challenges for less experienced designers. While GD enhances iteration efficiency and explores complex design spaces, its computational demands and limited adaptability to extensive geometric variations reduce overall efficiency.

This study presents an AI-driven method for generating preinventive structures - initial precursors to creative design concepts - using the Geneplore model as a theoretical framework. Multimodal AI is leveraged to derive preinventive structures from combinations of components of an existing product. This method is evaluated by comparing AI-generated structures of a product to those reverse identified from real repurposing solutions for the same product (IKEA hacks). The appearance of AI-generated preinventive structures in the repurposed designs suggests that this method can inspire and lead to viable design concepts. Implications extend to sustainable design, creative ideation, and the theory-driven development of design methods that support design in constrained solution spaces. Future work can refine these approaches and investigate broader applications in diverse design contexts.

Food production systems are shaped by external factors, such as social events and economic shifts, which influence and are influenced by labour dynamics—e.g., workforce availability—and human factors—e.g., worker skills. Using a systems approach, this paper explores how labour shortages impacting worker teams—such as in terms of mixture of availability, skills, and human behaviours—affect production and quality. UK apple harvesting is chosen as a case study due to its reliance on skilled seasonal migrant workers. Findings highlight the need for strategies such as upskilling local workers, enhancing training programmes, and adopting new technologies to mitigate labour shortages and enable high-performance collaborative worker groups.

While online learning allows learners to access materials flexibly and at their own pace, many struggle to self-regulate without supervision. Real-time interventions like pop-out quizzes, screen flashes, and text warnings aim to improve attention focus but risk distracting learners and segmenting the learning process. Despite eye-tracking technology being widely used for real-time intervention design, its potential for delayed and personalized interventions remains underexplored. To address this gap, we proposed and tested an eye-tracking-based video reconstruction and replay (EVRR) method, offering targeted review at the end of online classes without disrupting the learning process. EVRR shows significant positive effects on improving learning outcomes compared to self-paced reviews, especially for learners who are unfamiliar with the concepts.

Bioinspiration offers an innovative approach to product design. A key challenge is selecting suitable biological features for complex engineering problems. The phenomenon of convergent evolution, where distantly related organisms independently develop similar functions, adds to this complexity. This study introduces novel meta-level design parameters to systematically select biological features with differing geometries yet similar functions. These parameters were derived through physical testing and numerical analysis of woodpecker-beak-inspired and Balanus-inspired structures, focusing on their impact resistance capabilities. These structures demonstrate potential for practical applications, such as in bicycle helmet liners.

The automotive industry faces many simultaneous challenges like transitioning from combustion engines to electric vehicles. Suppliers must adapt to changing markets and develop new solutions. Existing transformation approaches focus on strategic goals and comprehensive implementation. However, there is no focus on the transition of the product portfolio. This paper presents a design-thinking-based approach to rapidly generate innovative product ideas. First, company assets, product portfolios, and market environments are analysed to define the ideation focus. Next, these are recombined by interdisciplinary teams to generate ideas, which are then evaluated. In a workshop with 15 experts from an exhaust pipe manufacturer, over 400 ideas were generated and refined into 15 actionable concepts in five hours. This approach supports rapid, cost-effective innovation and strategic transformation.

Design method validation is fundamental to ensure that design methods achieve their objectives in the intended situations and are accepted in practice. Although various method validation approaches have been developed, there is still a lack of practical guidance for planning validation studies based on project characteristics. To address this, an intensity map of the validation effort is presented as the core of a scenario-based planning approach. It categorizes projects according to the novelty of the method and the state of research on the problem or the research area, enabling the required validation studies, their sequence and validation criteria to be identified. Thereby, researchers can plan validation studies and estimate the required effort situation-based, allowing for a better alignment with their individual project characteristics before starting studies.

People rely on daily interactions with artifacts, greatly influencing their physical, mental, and social well-being. Despite this, current design practices often overlook well-being as a core consideration. Affordance theory, which explains how an artifact’s features enable specific user actions and experiences, offers a promising lens for addressing this gap. This study focuses on assessing affordance mechanisms as a potential tool to support design practices to design for positive well-being outcomes. Using transportation modes as a case study, we interviewed college students to explore how specific mechanisms can contribute to positive or negative well-being outcomes. Findings resulted in 233 examples, which showed trends in mechanisms, modes, and well-being outcomes. Ultimately, this work presents an initial framework for embedding well-being considerations into design.

To specify the solution principles of a design, a material selection should be performed already in the concept phase. Based on the design constraints, inappropriate materials are removed using an attribute filter. Brittle materials are often removed using fracture toughness attribute limits, but this does not take into account the strength specific stress level and incorrectly excludes entire classes of materials. We propose a novel filtering method to account for brittle failure in material selection. Based on linear elastic fracture mechanics, we establish a relationship that correctly describes the transition between brittle and ductile materials. Representing the proposed filter on an Ashby plot, we evaluate its effect on the further material selection process. Additionally, we show how different defect sizes in the materials can be incorporated into the filtering process.

Various methods, such as LCA, LCC, or circularity indicators, are used to integrate sustainability into product development. However, these approaches often require extensive expertise in both processes and sustainability, which is not always available in combination. This paper introduces a framework based on the double diamond model, structured into (1) a preliminary assessment, (2) a collaborative workshop, and (3) a prioritization process. It aims to help engineers identify sustainability improvements without requiring prior expertise. A case study on an industrial digital printing system identified five opportunities for enhancing sustainability. Three measures were validated using LCA and the RPR metric. The study resulted in seven principles for sustainable printer design, with a lightweight door design, reduced number of rivets, and logistical improvements as key outcomes.

Virtual Reality (VR) has garnered significant attention as a potential ‘empathy machine’ for its ability to simulate firsthand experiences of others’ perspectives. However, recent research reveals conflicting evidence regarding VR’s effectiveness in fostering empathy, with outcomes ranging from strong positive effects to complete ineffectiveness. By analyzing both subjective experiences and objective measures, this study aims to elucidate the relationship between VR design and human empathy, addressing three prevalent perspectives on the field’s inconsistencies: flawed mechanisms, ineffective design, and mismatched methodology. The findings contribute to the theoretical understanding of empathic VR and provide practical implications for designing effective VR-based empathy interventions in engineering contexts.

As the global elderly population grows, emotional challenges unique to this demographic are often neglected in design under the assumption that older adults can regulate their emotions independently. This study highlights the importance of fostering positive emotions in the elderly through leisure activities. It examines (1) how design practitioners conceptualize emotion regulation in older adults, (2) the challenges they face in creating supportive designs, and (3) enablers identified by elderly individuals. Twelve design practitioners generated 64 interactive design concepts to enhance elderly leisure experiences, followed by interviews with five elderly participants to explore their emotional needs and preferences. The findings underscore designers’ challenges and highlight opportunities for user-centered approaches to promote emotional well-being in aging populations.

Robust Design (RD) is crucial in product development to ensure that products maintain reliable performance under varying conditions. Design knowledge is fundamental to RD. However, current methods lack a systematic approach to support design engineers in building design knowledge for RD. This paper addresses this gap by introducing a hypothesis-based method for systematically building design knowledge for RD. RD hypotheses are specifically developed for this purpose and are tested through a five-step method. The application of this method is demonstrated in a case study involving a hand-operated coining machine. The results show that the proposed method supports building specific design knowledge through two RD hypotheses. By employing this method, design engineers are systematically supported in making design decisions, leading to more robust product concepts.

Exploring patterns in large text corpus is essential for effective knowledge discovery in research domains. However, machine-driven methods often introduce noise and rely heavily on parameter thresholds. Human expertise is therefore essential for ensuring reliable outcomes. This study conducts a comparative analysis of a classification task performed by both human and computer algorithms. During the task, human experts are asked to categorize a list of abstracts based on their semantic contents, where computer algorithms perform computations, including network analysis and document embeddings, to group the abstracts. The findings show a significant level of disagreement between human and computer-generated clusters, indicating the need for further investigation into the factors influencing community categorization and incorporating more advanced techniques to improve the results.

Generative artificial intelligence (GenAI) has the potential to further revolutionize Computer-Aided Design (CAD) by recognizing patterns, making predictions, and generating automated design suggestions. This paper presents a systematic literature review that examines the current state of research on the use of GenAI in CAD-based product development. With a focus on 3D modelling, it provides an overview of current approaches, most used datasets and commonly used AI models. Four application areas where GenAI can enhance CAD were derived: Design generation, Design reconstruction, Design retrieval, and Design modification. In total, 47 papers were selected, analysed and categorised.

Generative Design (GD) tools can produce high-performing components with complex geometries that are challenging to conceive via traditional methods. While potentially disruptive, GD tools have yet to achieve widespread use in industry. One reason is that current GD tools are limited to manufacturing methods capable of producing intricate geometries that GD often creates such as 3D printing. To overcome this barrier, this paper quantifies the benefit of altering generatively designed parts to use standardized elements like wire stock and sheet metal via processes such as CNC bending and water jet cutting. Using a parametric cost model, we show that parts incorporating standard components can halve the unit price for production volumes of only 4 parts. Finite Element Analysis (FEA) reveals that replacing up to 60% of part volume has minimal impact on performance. Our findings highlight a gap and opportunity in existing GD research.