Remanufacturing can be facilitated by design activities considering value creation, preservation, and recovery. Design-related decisions for remanufacturing can affect the performance of business models, but there is a lack of literature to identify these barriers or enablers. Through an analysis of selected remanufacturing cases, an initial step to bridge this gap is provided. Findings highlight the potential of design for remanufacturing for enhanced value creation processes and new service offerings, and present recurrent barriers and enablers to remanufacturing in the cases.

This study employs a hybrid bibliometric analysis and the TCM (Theory, Context, Method) framework to examine the integration of emerging technologies like AR, VR, and AI in design education. Utilizing VOSviewer and CiteSpace on Web of Science data, it identifies pivotal research clusters and trending topics. The analysis reveals a shift toward immersive representational ecosystems and highlights critical research gaps. Consequently, the paper proposes a preliminary conceptual framework for collaborative design, offering a roadmap for pedagogical and curriculum transformation.

Colour, Material and Finish (CMF) designers face rising circularity demands but lack tools that combine reliable data, traceable reasoning and creative control. This paper reports a case study with automotive CMF designers, identifying pain points in data access, evaluation of circular options, authorship and trust in AI. We propose design requirements and a conceptual model for agentic AI systems that support circular CMF work while preserving designer agency, accountability, and confidence in material decisions.

The integration of Generative AI in engineering education requires a deeper understanding of diverse student adoption patterns. This study applies cluster analysis grounded in the Technology Acceptance Model and extended constructs on survey data to create different user profiles. Four distinct user profiles emerged: Empowered Optimizers, Mainstream Pragmatists, Skeptical Minimalists, and Ethical Achievers. The findings challenge one-size-fits-all approaches, providing a student-centred framework for designing tailored instructional strategies, GenAI training, and ethical guidelines.

This study evaluates the efficacy of various freely available Large Language Models (LLMs) in conducting semi-automated purpose-oriented technology searches to support design activities as well as Technology Intelligence for innovation management, using a systematic manual search as a baseline for comparison. The case to run the comparison focuses on identifying water purification technologies suitable for mobile systems. The results show that LLMs can target more technologies than human-based searches, reducing time demands and providing wider entry points for additional technology analysis.

Barriers such as limited repair literacy and design-for-disposability continue to reinforce replacement cultures. This paper introduces AIFixer, an AI-powered interactive tool that guides consumers through electronic repair, promoting sustainable product lifecycles. Using a mixed-methods, user-centred approach, the study evaluates AIFixer’s usability and behavioural impact across real-world repair tasks. Findings show that conversational AI lowers barriers, builds confidence, and generates data for circular design, highlighting opportunities for multimodal and community-integrated development.

In this work, we propose an extension of classical form-finding that incorporates non-design space requirements directly into the process. This enables numerical weight optimization of thin-walled structural components. We present a concrete implementation which relies exclusively on standard structural finite element analysis, promoting integration into existing workflows. The method is validated on benchmark problems with known optimal solutions. Finally, its practical benefits are demonstrated through a more realistic engineering case study.

Dominant designs establish de facto standards for all products within an industry, shaping both competition and innovation dynamics. Studying dominant designs enables firms to make informed decisions for new product development and to anticipate technological shifts. This paper presents a computer-based method that automatically extracts the spatial configuration of components from patent drawings to support the analysis of dominant designs and anomaly detection. A case study on eyeglasses validates the approach, demonstrating its potential for data-driven design innovation.

This study examines designers’ cognitive and emotional experiences during the design thinking process and the effect of time constraints. Using the MetaCogno tool, 83 participants reported moment-to-moment experiences across Problem Analysis, Ideation, Evaluation, and Sketching. Positive experiences dominated, with time-limited designers showing higher enjoyment, focus, and engagement. Findings highlight the dynamic interplay of cognition and emotion, and suggest that time pressure can enhance focus and motivation during design.

This paper compares nine Model-Based Systems Engineering methodologies with ISO 15288:2023 on the basis of their technical process coverage, to assess the extent to which the methodologies and standard are aligned. The technical processes covered include the definition of stakeholders, stakeholder needs, system requirements, architecture, design, verification, and validation. Two comparison criteria are also linked to the standard, namely, traceability and customisation. The results indicate that the definition of system requirements and system architecture are common core components of SE.

Barriers limit laboratory participation for students with study-impairing conditions. A mixed-methods study via survey (n=43) and interviews (n=9) identified two student clusters with distinct accessibility needs: Cluster 1 requiring flexible scheduling and modular structure for chronic illness and caregiving; Cluster 2 requiring explicit guidance and multi-modal content for visual impairment and neurodivergence. Both required institutional support infrastructure. Results establish evidence-based requirements for inclusive remote laboratory development applicable beyond target populations.

We present a thermal process-monitoring system for MEX tracking layer temperature as a proxy for interlayer adhesion. Python-based hottest-point tracking by infrared thermography is implemented on a chamber-heated desktop printer to track nozzle movements and measure the temperature field millimeters ahead of deposition logging the results on a CSV file. We quantify accuracy versus camera distance (Δd=73mm) and probe radius (R2-R5). Where R3-R4 provided just a ΔRMSE of 1.52°C suggesting R3 as the optimal distance. The results can inform mechanical properties in load-bearing AM applications.

To design for sustainability requires systemic change which cannot be carried by design teams alone. Using a case study approach in aerospace, this study investigates stakeholder influence in sustainable product development. It identifies main internal and external stakeholders and discusses how design teams should engage with them. Findings support practitioners to navigate structural barriers and plan design interventions, and highlight the research need to consider organizational structures, decision-making processes, and cross-functional collaboration for sustainable product development.

This paper reconsiders the concept of sense of place through the perspective of worlding. While often defined as the emotional and cognitive bond between people and environments, this study expands it toward a design-oriented view that reinterprets and regenerates local meanings. Based on a design inquiry into regional contexts, it examines how making and storytelling can translate local materials into prototypes that evoke new relations to place. The study suggests worlding as a generative process through which place is continuously reimagined and experienced within dynamic local contexts.

The applicability and scalability of design adaptations utilizing reinforcement learning can be broadened by using graph-based approaches instead of rigid vector- or grid-based ones. However, graph-based approaches often require a high number of simulations to converge. To reduce the simulation effort in the mechanical optimisation, the reinforcement learning setup is enriched with task-specific causal and physically based information. This work systematically investigates the influence of this additional information on the efficiency of design adaptations using a factorial test design.

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.