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.

This study investigates the mechanical performance of PA6-CF and PLA components fabricated with desktop material extrusion additive manufacturing. To define the geometry, low-cost 3D scanning was used in combination with Generative Design in Autodesk Fusion 360. PA6-CF outperformed PLA by 25% in pre-failure peak load (1.85 kN vs. 1.47 kN), despite the datasheet values suggesting a 450% advantage in interlayer strength. Poor interlayer bonding of PA6-CF is attributed to low layer temperatures (87–136 °C) during the printing process, indicating that a chamber temperature of 60 °C is inadequate.

As systems become increasingly data-centric, interdisciplinary engineering design faces growing complexity and interdependencies. This paper investigates how a combined Design Structure Matrix (DSM) and graph-based modeling approach supports interdisciplinary decision-making by revealing critical data dependencies, compared to standalone DSM or graph models. Based on a case study on autonomous public transportation and expert input, the results illustrate complementary insights enabled by the combined approach and discuss its implications for industrial system design.

Life cycle assessments identify environmental hotspots, yet translating these insights into design actions remains slow and expert-dependent. Existing AI tools lack a dynamic link to current research. Here, we present an LLM-driven pipeline that interprets LCA hotspots, mines recent literature, and extracts feasible, research-backed design alternatives. In a case study on a headlamp control unit, the method produced relevant and applicable improvements, indicating its value for accelerating sustainable product design.

The study documents the approaches, processes, methods, and tools used by 11 start-ups to develop their smart products, while focusing on the sustainable concepts and techniques (SCTs) adopted. Although the majority of start-ups demonstrate awareness of the environmental impacts of their products and tend to implement relevant practices, the results indicate a lack of formalised SCT usage. Several start-ups have therefore recognised the value of a bespoke “eco-design toolbox” and aim to work towards reducing the environmental impacts of their later product versions.

Cities play a major role in designing future mobility plans. Our question is how to contribute to sustainable mobility design while effectively accounting for social equity, health, and wellbeing considerations. After defining a list of mobility-related social issues, two stakeholder-based workshops with mobility users from two major cities, namely Paris and Cairo, were conducted. Participants explored mobility problems through eighteen purposive persona models in total. In Cairo, participants mainly reported safety and security issues while in Paris, mobility stress was dominant.

The increasing digitalization and connectivity of development processes are forcing companies to transform their engineering comprehensively. The presented engineering reference capability map provides a structured framework for this transformation. The capability map is a four-level hierarchical model, contains essential engineering capabilities and is inspired by the periodic table of elements. Standardized profiles describe the characteristics and dependencies of each capability. The map serves as a reference framework for identifying gaps, potential, and development needs in engineering.

This study examines how AI can support the development of Leading Sustainability Criteria in sustainable product development, comparing AI-generated outputs with human-facilitated workshop results from four Swedish companies. Results highlight AI’s ability to accelerate and broaden sustainability framing, but emphasize that contextual relevance and legitimacy depend on participatory inputs. The findings suggest that AI is most effective when integrated into hybrid workflows that preserve human insight and stakeholder engagement—offering practical guidance for future implementation.

This work investigates the development of sneakers designed under Design for Disassembly principles and supported by additive manufacturing to promote a more sustainable and circular product life cycle. By responding to the limitations of traditional footwear assembly, the study identifies and organises 35 technical requirements derived from consumer needs. The proposed model offers a clear and adaptable framework that enhances decision-making in early design stages, guiding the creation of innovative, recyclable and environmentally responsible footwear solutions.