The present paper proposes a framework for translating lost haptic cues of textiles into digital environments with a view to reducing perceptual uncertainty in the context of online shopping. The model’s integration of touch-related attributes and multimodal representations facilitates reliable customer perception, enhances material communication, and guides designers towards informed decisions. The paper outlines the challenges and opportunities inherent in the domain of multisensory digital textile experiences, while concurrently establishing a foundation for future research in this field.

Mixed Reality (MR) prototyping offers significant design opportunities but introduces complexity in prototype specification. This paper presents a card-based design tool to support designers in this specification process. The tool is based on a comprehensive taxonomy of MR prototype fidelity and foundational research into the interplay between, and value of, different physical and virtual characteristics. A validation study demonstrates that the developed tool supports and guides designer reasoning, resulting in higher quality MR prototypes with stronger rationale for their implementation.

The transition to a circular economy requires products that encourage circular consumer behaviour. Despite the central role of designers in this transition, the design for circular behaviour (DfCB) approach remains under-explored. This paper presents a literature-based conceptual model explaining which factors need to be in place, and how they interrelate, in order for designers to facilitate circular behaviours through product design. By pointing out gaps in the current state, future research directions are suggested to foster the establishment of DfCB.

This study proposes a framework to map biomimetic innovation progress along TRLs and identify recurring development patterns. Pilot results reveal stagnation between TRL 2–4, linked to generic upscaling struggles and biomimetic-specific barriers. Emerging hypotheses suggest early onset of upscaling challenges post-POC and influence of biological model knowledge on progress. Study insights open paths for methodological work to bridge POC obtention and validation difficulties, and for further use of the framework on bigger datasets, to build a baseline for biomimetic innovation development.

This contribution addresses the lack of a structured framework for idea workshops in the Integrated Design Engineering (IDE) and resource-constrained settings. The current workshops in IDE are based on general creativity literature rather than on the processes and experiences inherent in IDE. This contribution derives a sequenced mode and integrates proposals to overcome helps to overcome common pitfalls. The sequencing shows best-practice in IDE and enables untrained users to enhance idea quality and process efficiency. The contribution offers a foundation for creativity technique assignment.

This paper explores the application of Web3.0 technologies to provide de-centralised secure, private, and provenance preserving trust networks for society’s increasingly digital design and manufacture workflows. It provides an overview of the key technologies involved and an example of a minimal trust framework required for issuing jobs between actors and machines in a makerspace. A comparison with centralised AM farm platforms is made and demonstrates how Web3.0 can support emergent trust structures compared to fixed centrally managed structures that actors need to agree to.

Industry is experiencing rising thermal loads, so geometries that improve energy transfer are needed. However, defects arising from overhang in additive manufacturing affect the functionality of triply periodic minimal surface (TPMS) based heat exchangers. This study addresses how TPMS superposition affects heat transferring and overhang critical surfaces. The objective is to quantify the functional and manufacturing trade-offs, and to identify the optimal hybrid cells formed from gyroid, Schwarz and diamond units.

Recent advances in machine learning (ML) offer substantial potential for product development (PD), yet adoption remains limited. A crucial step is identifying suitable ML algorithms for a given PD problem, which requires translating domain-specific formulations into appropriate ML tasks. Prior work indicates that LLMs struggle with this step due to insufficient domain knowledge. Therefore, this study investigates whether a domain-specific GraphRAG approach improves model performance by enriching prompts with structured context from a PD knowledge graph.

High-quality requirements are essential for successful product development. This work proposes a model-based requirements engineering framework and AI-supported tool. The framework links design characteristics and measured properties via an OPM-based system model. This enables the implementation of a tool for systematic verification and validation of requirements in early product development stages, supporting the transition from experience- to data-/evidence-driven decision making and industry 4.0 paradigms. A hydraulic-press case-study demonstrates feasibility of the end-to-end workflow.

Solar photovoltaic systems are a key renewable energy solution, but behavioural rebound effects offset their environmental potential. As Solar Home System adoption expands in low- to middle-income countries, understanding how contextual factors (e.g., social norms) shape these effects is crucial, yet research on this topic is scarce. Through a systematic literature review, this study identifies 15 contextual factors influencing behavioural rebound mechanisms (BRM). Findings are integrated into a design tool, helping developers analyse contexts, anticipate BRM, and apply prevention strategies.

Low-tech approaches and practices have developed in recent years, both as a model of strong sustainability in a context of polycrisis, and as an alternative technological discourse opposed to the prevailing techno-solutionism. Various organisations have drawn inspiration from them for the transition or redirection of their system. This article refers to this as “integration of low-tech approaches”. It provides an overview of such integrations in industries through the study of seven cases. Then, it discusses the challenges of their definition, which underpin their sustainability potential.

This paper presents the IMPRINT framework, a structured method to enhance the interpretation of EF 3.1 midpoint indicators in energy-intensive industries. By clustering the 25 indicators into five domains—climate and energy, human health, aquatic quality, air emissions, and resource depletion—the framework improves the readability of LCA results, highlights environmental hotspots and trade-offs, and supports more informed decision-making in industrial technology evaluation and sustainability planning.

Building and maintaining a digital twin requires considerable technical and financial effort. Thus, its economic viability depends on creating value across the full product lifecycle to balance the initial costs. Therefore, this study examines what potentials and challenges arise from the use of the Digital Twin throughout the product lifecycle with regard to its benefits for product development, using a systematic literature review. The identified factors were clustered and mapped to key components, highlighting the strengths and weaknesses of the individual components of the digital twin.

Industrial adoption of additive manufacturing (AM) remains limited, partly due to challenges in determining when AM is more suitable than conventional processes. Since this decision must be made early to enable effective design for AM, understanding the factors that shape such assessments is essential. This study used iterative need analysis and prototype development loops to investigate these factors. The findings identify key needs and barriers influencing early decisions on when to design for AM and show that effective support requires a deep understanding of the underlying problem.

We present FORGE (Framework for Optimization and Reinforcement-driven Generative Engineering), a probabilistic programming framework for generative design that unifies declarative, symbolic modeling and reinforcement learning (RL). FORGE can learn and refine a design generator through RL based on simulator-derived rewards. We demonstrate FORGE across several vehicle domains. FORGE creates an extensible, interpretable foundation for generative engineering. It can act as both a data generator for machine learning and a design optimizer, offering a practical alternative to purely neural methods.

The use of artificial intelligence, especially large language models (LLMs), is increasingly explored to support early system development. This paper evaluates low-threshold LLM-based tools for supporting conceptual design. Through an experiment, two LLM-based tools were compared generating alternative solutions using an existing function model of an electro-mechanical system as input. Functions were provided in natural language and using the Functional Basis. Results show limitations and significant potentials for effective and efficient conceptual design support.

This study explores how AI workflows and prompt engineering reshape storytelling in design education. Students utilized tools such as ChatGPT, Midjourney, Runway, and Meta Glasses to reframe existing projects through iterative scripting, image generation, and reflection. Analysis of 88 visual projects and over 80 videos showed a shift from static documentation to multimodal narratives. Findings suggest AI enhances communication fluency, engagement, and reflective practice through adaptive, platform-native storytelling.

Generative Design (GD) offers lightweight, manufacturable solutions, but manufacturing constraints often require indirect handling. Using a deflection lever for Additive Casting, we compare PTC Creo GTO and Altair Inspire using identical load cases and mass-minimisation. Both tools achieved −26% mass and −46% inertia while meeting displacement limits. Wall-thickness control and channel integration show clear trade-offs, yet both tools provide viable designs for early development.

Design thinking fosters creativity but it’s susGceptible to cognitive biases. We propose a rule-based framework supported by large language models that uses a Prompt-Reflection-Reframe loop to identify bias mechanisms in designers’ verbal reasoning and generate theory-grounded reflective prompts. Through scenario-based evaluations, we validate the framework’s theoretical foundations and establish a methodological basis for supporting bias-aware design practice.