This paper presents a controlled laboratory study investigating how environmental factors influence team resilience and teamflow in product development contexts. It examines which factors shape teams’ adaptive responses and collective engagement. In the study, one factor per People–Organization–Technology dimension was systematically manipulated: member unavailability, time autonomy, and material quality. Results reveal distinct effects on performance and collaborative dynamics, providing empirical foundations for designing resilient team environments in engineering work.

Critical infrastructures are complex, interdependent systems on which our societies are reliant. A better understanding of these interdependencies is vital to improving their functioning and resilience. While various studies and surveys have been conducted, we aim to cast a new perspective by focusing on what Rinaldi et al. introduced in 2001, as “logical interdependencies” and their modeling and simulation considering the human factor, and by adopting a cross-area approach to guide future works through the identification of research directions and common design challenges, good practices.

This paper posits that a Digital Twin can be viewed as a collection of nodes and edges where nodes represent actions on/with data and edges represent the flow of data between nodes. The paper provides a schema whereby the nodes and edges can be defined and the costs and benefits can be attributed, as well as analysis techniques enabled by the schema. The potential of the schema in supporting the design of Digital Twins is then demonstrated through a worked example, in which it is shown that traditional bottom-up cost estimates significantly overestimate costs when compared with this approach.

This work presents an ML-based inverse design framework for multi-material lattices with curved struts, targeting mechanical and thermal performance. Using cubic-spline parameterization and discrete material assignment, the design space expands beyond conventional lattices. A workflow combining a material classifier, property predictor, and inverse generators addresses one-to-many mapping, enabling probabilistic sampling and diverse designs. The approach supports multi-objective trade-offs and lays the foundation for multi-scale optimization of functionally graded metamaterials.

Leveraging the vast interconnection of language and ideas through Large Language Models, a designer’s understanding of the needs, wants and desires of intended stakeholders defines the value proposition and product design requirements of a product or service through implementation of the Value Opportunity Analysis (VOA). The resulting VOA LLM Bot explores emotion, aesthetic and other human-valued attributes, and significantly increases perception of the VOA as a useful method for identifying product requirements, and analyzing opportunity solutions.

This study aims to examine the influence of semantic feedback on the functional connectivity of students’ brains in design education. We evaluated functional connectivity using EEG. After the instructor provided feedback, we observed a significant reduction in students’ alpha-band activity across 16 channel pairs. It suggests that, after receiving feedback, participants relied more on localized neural circuits rather than on broad, diffuse connections. Semantic feedback potentially facilitates participation in more efficient cognitive processes, thereby assisting design ideation.

The paper presents a simulation framework for evaluating fast charging and battery swapping strategies in battery-electric construction machinery. Developed using discrete-event and agent-based modeling, the framework supports scenario analysis in mining and road construction contexts. Case studies demonstrate how charging strategies impact productivity, energy costs, and battery degradation. Results highlight trade-offs between operational efficiency and long-term sustainability, offering a decision-support tool for electromobility transition in construction machinery.

In this work, we propose a multimodal, language-model–based design assistance framework for the design ideation stage. The framework leverages large language models (LLMs) to interpret user intentions with mood boards, enrich initial ideas with essential contextual details, and produce structured instructions for visual language models (VLMs) to enhance the accuracy and consistency of visual feedback.

This paper explores the adaptation needs of employees in the context of implementing virtual reality (VR) in product development. Rather than analysing the overall process, the study focuses specifically on the employee aspects, including their roles, tasks, and challenges within the workflow. Existing work-related activities were analysed and visualized to identify inefficiencies. A set of tailored assessment criteria was created to systematically evaluate various sources of waste and process-related challenges.

This paper presents a characterization approach for analysing geometric variability in industrial 3D model datasets to support the preparation of synthetic datasets for machine-learning applications. By implementing pairwise Hausdorff distances and manifold-based embedding techniques, the study identifies variability ranges required for generating representative synthetic data and demonstrates how targeted augmentation can effectively reproduce real data’s variability, ultimately leading to more reliable and robust NN model performance.

The paper assesses the social impacts of composting and anaerobic digestion facilities for household biowaste in France. Using the Social Life Cycle Assessment (SLCA), it identifies 16 indicators that compare workers’ conditions, community impacts, and societal benefits. This work proposes a framework for incorporating social dimensions into a multi-criteria assessment of anaerobic digestion and composting facilities in Europe, with a particular focus on France.

This paper presents a concept for an AI-supported DfAM framework aimed at supporting knowledge extraction, focusing on early design phases. The concept is derived from a set of objectives and integrates, in addition to the user, an agile DfAM process model, an AI copilot based on a large language model, and a structured knowledge base. A configured GPT is used as a prototype to demonstrate the feasibility of selected required functions. With regard to a full-scale framework, findings from this prototyping process and remaining open questions are discussed.

Speech-capable AI systems introduce new possibilities for communication and collaboration in design, yet methods for analysing human-AI interactions through speech remain limited. This paper proposes and applies a method for analysing conversational interactions in speech-based human-AI design activity. Grounded in conversation analysis, this method reveals how conversational structure and designer roles emerge through spoken interaction, offering an analytical framework for examining communication, cognition, and collaboration in design.

Nadheim offers healthcare to persons selling sexual services. Using relational, feminist, and system-oriented design, a rich design methodology combined cultural probes, vignette studies, and giga mapping unc toovered issues of service fragmentation, stigma, and digital exclusion. A co-created digital tool offering anonymous, centralized access to health, legal, and support services. An added speculative concept imagines a sex worker union to allow for radical change. Findings highlight trust, inclusion, and co-agency, positioning design as a catalyst for social justice.

Product variety increases complexity across product, process, and organizational domains, yet existing complexity measures offer limited guidance for design decisions. This study implements established metrics within a framework to enable consistent assessment across domains. The results reveal substantial redundancies among these metrics - with the notable exception of modularity, emphasizing its central role in complexity management. The consolidated measure set provides practitioners with a systematic basis for evaluating design strategies and managing complexity in product families.

This study investigates the potential of multi-material additive manufacturing (MMAM) designs for improving microchannel reactors for ammonia decomposition. Using CFD simulations, designs made from stainless steel 316L and CuCr1Zr to enhance specific surface area and temperature distribution were analyzed. Results show that MMAM designs can reduce temperature gradients by up to 26.81 K and boost fuel processor efficiency by up to 3.2 percentage points compared to mono-material designs. These findings underscore the potential of MMAM in optimizing the reactor efficiency.

This study presents a method for early identification of AM-suitable components and circular design routes. A structured, data-lean questionnaire with AHP-based weighting, combined with rule-based R-strategy identification and an AM-specific feasibility gate, enables transparent screening in early development. Demonstrated on a structural bus component for both diesel and electric operation, the approach proposes a dominant R-strategy and integrates a prospective LCA-in-the-loop workflow, showing how early circularity signals and life-cycle feedback inform robust redesign decisions.

This paper explores the role of artificial intelligence to reduce resource burden and support service delivery processes in generalist secondary-care mental health services in the Netherlands. Through semi-structured interviews with domain experts and using service blueprinting as a stimulus, we identified challenges and bottlenecks in mental health care pathways and intervention opportunities. We propose four intervention directions for design researchers and developers to prototype and assess how AI technologies may alleviate capacity issues in mental healthcare.