Thermal history is critical to part performance and reliability in material-extrusion additive manufacturing. Using encoders and an infrared camera, we developed a method to generate thermal clouds, where each node has its distinct spatio-thermal data. Filters removed up to 20.68% of the data while preserving relevant thermal features. This study enables in-situ process monitoring that establishes the basis for part certification, particularly for high-performance polymers, and for predicting material strength from thermal clouds.

Providing effective assistive technologies is challenging due to misalignment with users’ needs, often leading to product abandonment. Occupational therapists play a key role in prescribing, adapting, and creating personalized ATs, yet technical and marketplace barriers complicate this work. Computational design tools can support OTs, yet no clear design guidelines currently exist. From four case studies, we identify six considerations: workflow integration, intuitive interfaces, real-time visualization, collaboration, customization, and safety, to guide OT-focused design tool development.

Advances in additive manufacturing (AM) enable the use of AM components in demanding complex applications with high functional requirements. As a result, integrating standardized machine elements such as conventional rolling bearings is gaining growing relevance. However, limitations regarding achievable tolerances or surface qualities in the MEX process stand in contrast to strict specifications for bearing integration. This study introduces a novel interface element and a corresponding integration process that considers both bearing requirements and the layered structure of MEX components.

This contribution analyses key factors for establishing innovation communities within the circular economy. A mixed-method approach combines systematic literature review and a stakeholder survey to identify success conditions, governance requirements, and implementation challenges. The results underline the importance of open, interdisciplinary networks and adaptive, participative governance. Recommendations focus on iterative evaluation, stakeholder inclusion, and scalable models for long-term impact.

This study investigates how large language models (LLMs) support extracting technical requirements from early product pitches. Mechanical engineering students worked under three conditions: manual, LLM-assisted, and LLM combined with a QFD interface. Both AI-assisted conditions improved requirement quality and lowered perceived difficulty. Thematic analysis showed cognitive effort shifted from generating requirements to evaluating and verifying AI outputs, while the LLM-only group reported the most positive attitudes.

This work presents the design and simulation-based validation of a next-generation cell-to-pack battery system for hybrid-electric refrigerated transport. The configuration integrates a welded stainless-steel frame, liquid-cooling system, and power-electronics module within a compact modular structure that improves assembly efficiency and adaptability. Finite-element and thermal analyses confirmed compliance with vibration and thermal requirements, achieving improved volumetric efficiency and scalability. The results provide a validated foundation for prototyping and future optimization.

Turbine blades are high-value components whose replacement is costly and slow, increasing the demand for effective repair strategies. Although PBF-LB/M supports precise additive repair, its application is limited by manual and time-intensive part preparation. This work introduces an automated digital workflow for cutting plane definition, repair geometry reconstruction and part alignment, improving reproducibility and reducing preparation time in PBF-LB/M-based turbine blade repair.

The paper presents an automated decision support application for sustainability metrics in product design by leveraging semantic technologies. The proposed framework utilizes an ontology for structured representation of design characteristics and correlating lifecycle data. A rule-based approach is investigated for enabling automated reasoning. The implementation showcases real-time impact assessments based on CAD models, supporting sustainable product development. This approach demonstrates significant potential for advancing lifecycle design practices in industrial contexts.

Conventional service design methods are valuable for improving healthcare experience, but are limited in scale and information capture. Based on a constructed database of 2,320 stories from patients and carers with multiple long-term conditions (MLTC), this paper shows how real-life experiences can be used to inform healthcare service redesign. By combining the richness of qualitative insight with the breadth and representativeness of large-scale data, it identifies “Continuity of care”, “Care coordination”, and “Temporal – Access to services” as the priority redesign opportunities for MLTC.

Trade-off studies often use the design of experiments approach, while simulation models enable data-based product optimization by AI. This paper presents a comparison of evolutionary algorithms, reinforcement learning as well as active learning for design space exploration. Based on a real-world case study and hypervolume analysis, the performance of selected algorithms is assessed. The results highlight their ability to identify pareto fronts and provide insights to deepen the understanding of AI-driven design space exploration.

As concerns about climate change and biodiversity loss intensify, circular economy strategies are crucial for decoupling economic growth from resource depletion. Yet, the consumer behavioural dimension including returning, repairing, and accepting refurbished products remains underexplored, in particular in the bicycle industry. By conducting a survey of bicycle users, this study finds a strong willingness to engage in these slow-the-loop practices, driven by cost savings, convenience, and trust, but hindered by knowledge gaps and quality concerns, implying recommendations for manufacturers.

This paper evaluates the criticality assessment within the Criticality-Based Planning of Prototype Sequences method. Two independent application studies investigate whether the existing scale definitions for novelty, technical difficulty and importance are sufficient for systematic and context-independent use. The results show that operationalised criteria and clearer evaluator guidance significantly improve consistency, reproducibility and applicability across different development projects.

Digital news interfaces often prioritise speed and visual intensity, overlooking the material conditions through which information is experienced. This paper investigates how a text-only news device influences perceptual clarity, attention, and emotional tone in everyday use. Using a research-through-design approach with a deployed design probe, findings reveal material and formal qualities shape news engagement, introducing the concept of “minimal media” and informing the design of calmer information devices.

This study presents the “P-Heroes”, a superhero family designed through a Research through Design process to support children with urinary incontinence. Inspired by children’s own superhero drawings, expert input, and validation sessions with children, the design evolved through iterative prototyping. Each hero embodies traits such as persistence, comfort, emotional regulation, resilience and structure. Together, the “P-Heroes” reflect the diversity of children’s journeys toward continence and serve as a playful conversation starter to help them express their needs and experiences more openly.

This study systematically characterizes product development processes (PDPs) in hardware-oriented SMEs. A PRISMA-based literature review identifies twelve empirical case studies and eight development frameworks. The analysis derives key PDP influence factors, synthesizes stage-wise SME PDP activities, and consolidates characteristic challenges. A gap analysis reveals the absence of SME-specific PDP development frameworks and missing integration with technology management. The results establish a structured empirical basis for future research.

Individualisation in military equipment aims to improve performance by aligning design with soldier-specific needs. Existing DfAM methodologies lack structured integration of user variability and iterative evaluation in defence contexts. This study develops an iterative DfAM process linking anthropometric input, additive strategy, constraints, and performance assessment. Demonstrated through a helmet liner case, three iterations addressed geometry, manufacturability, and impact-response behaviour within regulatory limits.

First year students have heterogeneous prerequisites for acquiring competences in engineering design education caused by varying school education or vocational training. Individual support is challenging in large groups and frontal lectures. An approach for individualized support is adaptive e-learning. This paper analyses the impact of offering an adaptive e-learning environment for engineering design education (AdE-Le). Analysis results for data of the same course from three different years give insights into acceptance and impact of using AdE-Le on acquiring professional competencies.

The nutrition transition model examines the impact of industrialization and economic development on standards of living through dietary changes. The changes in dietary structure over the last two centuries have been determined. These changes consist of an overall increase in calorie and protein intake and a shift from a diet of predominantly cereals to one with animal-based products. The literature considers that the growth in income is the principal factor explaining dietary change. Although it has also highlighted the importance of other environmental, social, institutional, and cultural factors. Therefore, a quantitative exercise has been made to verify the role of economic factors such as income and prices in the nutrition transition process.