Design by Analogy (DbA) is a powerful method for fostering innovation by transferring knowledge from a source domain to solve problems in a target domain. However, traditional DbA approaches face significant challenges, including resource-intensive database management, linguistic and representational differences across domains, and the complexity of access and mapping processes. These limitations hinder scalability and efficiency, particularly for cross-domain analogies. Recent advancements in Artificial Intelligence (AI), especially Large Language Models (LLMs), offer promising solutions by facilitating efficient knowledge retrieval, bridging linguistic gaps, and enhancing semantic reasoning. This paper explores the potential of AI technologies to address these challenges, proposing a framework for analogical reasoning.

This paper challenges a false dichotomy between subjectivity and objectivity in understanding the nature of human social relationships. I argue that social relationships are composed of both subjective and objective components, which are inherently interdependent. They are influenced by biological properties and subject to evolutionary processes, yet they cannot be reduced to them. I use emerging research on kinship as an example that showcases the appeal of this integrated approach. This paper takes a step in the direction of a unified account of sociality, contributing to a more comprehensive understanding of human social behavior.

Given the rise of Generative AI and Large Language Models (LLMs), there is a high interest in their use also in engineering design domain. Current research approaches lack to leverage LLM's new orchestration capabilities and use the LLMs in ways that expose their inherent weaknesses. We present a conceptual model to visualize the contribution of LLMs to design tasks and distribute ownership in the design activities: the triangle of design responsibility. A literature review on the design engineering field presents its current uses in this community. The understanding of the model is validated with industry via survey. We identify future research directions in the field of complex product design. We hope that this model helps design automation developers, researchers and industry practitioners to position and assign responsibility effectively in their design automation implementation.

Uncertainty in coping with sustainability demands poses a challenge to decision makers concerned with manufacturing companies’ product engineering. Therefore, our paper reports on a newly developed guide to address their uncertainty and support them in initiating targeted sustainability action. The guide, based on an interview study (n = 25; 4 company cases and 1 consultancy) and a systematic literature review, addresses decision makers in product engineering and beyond. It was initially applied and evaluated in company workshops. The guide provides success criteria and reflection questions for each step toward targeted sustainability action: understanding, operationalizing, and implementing. This paper outlines the main concepts behind the guide and contributes to the literature by suggesting a novel approach to sustainability action in product engineering by addressing uncertainty.

While performing design tasks, engineers rely heavily on their knowledge. However, the expanding knowledge space makes it impractical to perform the design tasks without external inputs. This study explores how AI can bridge the knowledge space expansion gap in design. The study introduces the AICED framework implemented as a web tool Pro-Explora, leveraging advanced multi-agent LLM technology to accelerate early-stage design tasks. Pro-Explora generates professional problem definitions, PDS documents, and unique solution concept images within five minutes, maintaining creative flow. Its effectiveness was validated in a real-life project, with outputs deemed highly relevant by experienced designers. The study highlights the AICED framework’s industry implications, addressing required knowledge. This pioneering study opens new avenues for specific LLM applications in engineering design.

The evolving needs of customers and stakeholders necessitate the collaboration of diverse system elements within a cyber-physical, socio-technical network. Such Sociotechnical systems are characterized by numerous complex interdependencies as well as by endogenous and exogenous influences. A key issue that developers must address is the mitigation of data and information uncertainties. This paper introduces an ontological approach to facilitate the identification and mitigation of uncertainties in data and information within a model-based methodology for satellite development projects. The work outlines the results of preliminary studies forming the foundation for this ontological concept. The proposed approach comprises an overarching General Ontology, complemented by a Uncertainty and Structure Ontology, creating a framework for uncertainty management in satellite development.

Increased interest in space exploration demands a shift in the design and manufacturing of space systems. Traditionally, space structures are limited by constraints associated with launch systems that affect cost, volume, and mass. The concept of Factory in Space (FIS) proposes the fabrication of systems in space to circumvent the launch constraints. FIS offers a transition to a circular economy in space by minimizing resource consumption and creating a self-sustaining factory ecosystem. This paper evaluates the role of circular design in FIS. Circular design in FIS leads to a reduction in design complexity and modular designs that could enhance space exploration. Material selection, modular design, design for robustness, and lifecycle thinking are highlighted as factors that influence design for circularity in FIS. Finally, the challenges associated with circularity in FIS are presented

The development of smart Product-Service Systems (smart PSS) introduces unique challenges for requirements management due to their dynamic, data-driven, and multidisciplinary nature. This paper investigates methods and principles for data-driven requirements management, emphasizing lifecycle alignment, data utilization, multidisciplinary collaboration, and customer-centricity. A systematic literature review forms the basis for assessing 16 existing frameworks, focusing on their suitability for the data-driven design of smart PSS. Key gaps are identified in areas such as data planning, lifecycle integration, and the handling of system-level requirements. To address these challenges, this study proposes principles for data-driven requirements management that leverage real-time data, ensure traceability, foster interdisciplinary alignment, and adapt to contextual variables.

The process of gathering needs and generating requirements for design for individuals with special needs can be particularly challenging, and the intended solutions are increasingly evolving into Cyber-Physical-Social Systems (CPSS) further complicating the task. Co-design is the preferred approach but when the primary users are children, the challenges are compounded since they are unable to partner in the design process making the task of eliciting needs further difficult. This paper presents an empirical attempt to collate a master list of requirements for design for children with special needs to aid the design process. The study revealed several lacunae in comprehensibility of Requirements and Criteria, and mapping of the two, prompting further investigation into the hindrances to developing a robust and comprehensive resource for designers by designers.

This study applies Artificial Intelligence-Generated Content (AIGC) to design cultural products inspired by Sanxingdui, an ancient Chinese civilization famed for mystical bronze artifacts. Addressing the challenge of merging tradition with modernity, an AIGC framework automates cultural element extraction, generates design concepts, and optimizes aesthetics using generative models. Comparative analysis via Quality Function Deployment (QFD) shows AIGC products achieve higher user satisfaction in aesthetics, symbolism, and engagement. The research highlights the significance of AI in enhancing creativity, efficiency, and cultural preservation, despite algorithmic limitations. It provides actionable strategies for integrating AI into cultural industries, bridging heritage and technology to drive sustainable innovation.

The article presents the techno-typological analysis of a large bone arrow point assemblage recovered at different sites from the Late period of Sierras de Córdoba, Argentina (around 1220–330 cal BP). These bone arrow points exhibit a wide range of morphology and sizes. We classified them into typological groups or subgroups according to their morphology. Basic attributes (weight, length, neck width, blade width, thickness, angle of barbs, etc.) were measured to roughly assess the mass, velocity, and capability for tissue damage of bone-tipped projectiles. Bone arrow points were part of a specialized mechanism system designed to severely wound enemies or occasionally finish off prey from a short distance, creating more serious bleeding wounds than the smaller, easy-to-make chipped-stone arrow points that dominated late-period assemblages. Our analysis shows that the adoption of a broad-spectrum foraging and cultivation base around 1220 cal BP was accompanied by the development of new types of weapons for hunting and warfare. The design of the bone projectile points is consistent with a period during which social tensions increased across the Sierras de Córdoba, with clear evidence of physical violence.

The amount of electronic waste worldwide is increasing every year and the often incorrect handling of it has a major impact on the ecosystem. As electronics are also gaining more share in the automotive sector, the industry has to find a suitable way of dealing with them at the vehicle’s end-of-life stage. For this reason, this work introduces an approach consisting of the Physical Component Mapping (PCM) for the interface representation of automotive electronics, alongside the Eco-Sensitivity Framework (ESF) as guidance for circular automotive electronics design. A case study shows how the approach accompanies the product development process and supports identifying suitable strategies that are potentially possible or can be made available through design changes. This helps car makers and suppliers of vehicle electronics to accelerate their transition to a circular economy.

Research is presented on the development of student confidence in design through the use of design exercises in a non-design (materials engineering) course. This work revisits a prior study incorporating over three times the number of subjects, substantially expanding the statistical robustness of the analysis. Four distinct design exercises, covering topics like tensile failure, creep, impact, and fatigue, are integrated into the course, each employing structured pre- and post-assessment surveys to gauge confidence levels. Results consistently show significant improvements in student confidence, with post-exercise scores rising by 2 points on a 9 point Likert scale. This work underscores the efficacy of design exercises in bridging engineering science with practical design application of the topical knowledge, with implications for optimizing engineering education strategies.

For a multi- product manufacturing organization, product innovation is a constant process. A question which every such organization must answer for every innovative idea is whether that idea is to be incorporated in the existing product as a continuous process or it should be implemented as a new product? This paper studies the impact of architectural and design factors on this decision and formulates a decision parameter to facilitate this decision. This has been done by studying various innovation ideas implemented at two motorcycle manufacturers, collected by studying their spare parts catalogues across models and the implementation decision in case of each idea. The study reveals a clear relationship between the factors and the decisions, and the formulated parameter can clearly demarcate the ideas between the two implementation choices.

The increasing complexity and connectivity of the mobility system and modern automotive systems, particularly connected autonomous vehicles, demand a paradigm shift toward resilience-by-design to address disruptions in dynamic environments. Unlike established safety and cybersecurity engineering in automotive, resilience engineering has yet to be systematically integrated into development processes. This paper defines resilience using a standard-based definition method, emphasizing disruption tolerance, adaptability, and recoverability. We identify action fields to advance the topic and propose a resilience-by-design framework extending safety and cybersecurity perspectives. Resilience-by-design offers strategies and methods to design robust, adaptive systems, ensuring reliability and availability of automotive systems, functions, and components in operation.