Combinatorial optimization (CO) is essential for improving efficiency and performance in engineering applications. Traditional algorithms based on pure mathematical reasoning are limited and incapable to capture the contextual nuances for optimization. This study explores the potential of Large Language Models (LLMs) in solving engineering CO problems by leveraging their reasoning power and contextual knowledge. We propose a novel LLM-based framework that integrates network topology and contextual domain knowledge to optimize the sequencing of Design Structure Matrix (DSM) —a common CO problem. Our experiments on various DSM cases demonstrate that the proposed method achieves faster convergence and higher solution quality than benchmark methods. Moreover, results show that incorporating contextual domain knowledge significantly improves performance despite the choice of LLMs.

The transition to Industry 5.0 (I5.0) marks a shift toward human-centric, sustainable, and resilient manufacturing, leveraging technologies like collaborative robots (cobots) and AR/VR to enhance inclusivity, empowerment, and safety. This study investigates how Learning Factories (LFs) can effectively convey I5.0 principles to students and professionals. A simulated production line using AR/VR allowed participants to interact with virtual cobots, assessing key pillars of safety, inclusivity, and empowerment. A survey was used to assess the impact of this immersive environment on participants' perceptions and unconscious reactions. The findings demonstrate LFs' potential to prepare a workforce that integrates human creativity with technological innovation.

Facing increasingly dynamic market environments and global challenges such as climate change and resource scarcity, companies are under constant pressure to innovate and remain competitive. As technology is a key enabler, companies need to understand the drivers of technological change. Technology Foresight systematically identifies and analyzes emerging technologies to support engineering design decisions. However, the growing volume of data is outpacing manual processing capabilities. This research explores the integration of Generative AI to enhance Technology Foresight by automating technology analysis and information synthesis. This paper presents a comprehensive problem analysis, reviews existing solutions, and proposes a framework that demonstrates the potential of Large Language Models combined with a Retrieval Augmented Generation architecture to transform Technology Foresight.

Publicly available generative AI tools, such as ChatGPT, Midjourney, and DALL-E 3, have the potential to transform product development by accelerating tasks and improving design ideation. Through case studies of scenario management and persona storyboarding, this research explores the strengths and limitations of generative AI (GenAI) tools. The results highlight GenAI's ability to accelerate routine tasks, improve ideation, and support iterative design, but also reveal limitations in contextual understanding and output quality. Key findings show that effective GenAI integration depends on precise prompt design, iterative interaction and critical validation. Despite their potential, GenAI tools cannot replace human expertise for nuanced design tasks. The study provides actionable insights and best practices for leveraging GenAI tools, paving the way for enhanced human-AI collaboration.

This paper introduces a method for developing a knowledge graph aimed to enhance collaboration and information management in virtual product development. The proposed methodology integrates data from diverse sources, including CAD models and geometry, requirements and user-related data to construct a knowledge graph that enhances the retrieval and organization of information. Use-cases, such as information retrieval, tracking changes, and user-issue management, are explored to illustrate the potential of the knowledge graph. The paper details the steps of building the knowledge graph, from defining the ontology to implementing the graph using a structured data format. The structured method presented demonstrates significant potential to enhance collaboration and support the virtual development process, potentially leading to reduced development times and costs.

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.

Light weight design Plans am cranial role in enhancing efficiency and sustainability. The strategic use of advanced materials, such as fiber-reinforced plastics, can help achieving lightweight designs. However, the anisotropic material properties of composite materials also lead to new challenges in the design and manufacturing process. Additionally, due to the layered structure of composite parts, the number of design points is increased drastically. Moreover, the complex manufacturing process, including curing, makes composite parts prone to variations. Therefore, this research paper presents an innovative lightweight design approach that aims to overcome the described difficulties by linking the individual simulation steps, providing a continuous simulation strategy and taking variations into account. Finally, the presented simulation strategy is applied to an electrified cross skate.

Despite the lightweighting benefts that hollow structures afford, current Generative Design (GD) tools are not capable of synthesising them by default. This paper proposes an approach to generate hollow structures using an off-the-shelf GD tool and an innovative shelling method. The approach is used to create solid and hollow variants of a load bearing component. These are modelled using Finite Element Analysis (FEA) then Additively Manufactured (AM) and characterised via destructive load testing. FEA results show that the shelled structures are up to 2.5 x stiffer than their solid counterparts however destructive testing revealed small stiffness losses attributable to the AM process. Despite the physical testing results the method offers the potential to apply GD tools to industries where hollow tubes are accepted practice, enabling part consolidation capabilities to be leveraged.

This study examines the effects of linguistic elements in Vocaloid BGM on creativity, fluency and originality, aiming to design sound environments that enhance creative performance. Experiments were conducted under three BGM conditions: voiced-meaningful (VF), voiced-meaningless (VL), and non-voiced (NV). VF utilized the original Vocaloid song with lyrics, NV excluded lyrics entirely, while VL replaced lyrics with the syllable “la.” Results revealed that VF BGM could disrupt concentration and reduce creativity, while VL and NV conditions enhanced relaxation and improved originality. These effects became more pronounced as tasks progressed. A positive correlation was identified between mind-wandering tendencies and creativity, particularly with VF BGM. The findings highlight the importance of tailoring sound environments to cognitive modes and personal characteristics.

Engineering design tasks are cognitively complex and there is a growing interest in understanding the neurocognitive processes involved in design. Consequently, researchers are increasingly using bio-physical markers such as eye tracking to study design neurocognition. However, these studies are largely correlational, and little is understood about the construct validity of eye-tracking metrics such as fixation durations and saccade frequency. Moreover, these studies rarely account for non-design factors such as neurodivergence (e.g., ADHD) on eye-tracking metrics during design. We aim to examine this research gap through a causal-comparative study with designers with and without ADHD, performing divergent and convergent design tasks. Our findings call for a deeper investigation into the construct validity of eye-tracking metrics while considering a broad range of external factors.

Hackathons have recently garnered significant research interest. Hackathon teams frequently include developer, business, and designer roles, yet the designer role and experience of design in hackathon teams are poorly understood. In this paper, we present findings from ten interviews with designer hackathon participants. A thematic analysis reveals that the responsibilities of designers at hackathons roughly align with more typical design contexts, although the format of hackathon events forces designers to adapt approaches to design. Hackathon participants value teams with diverse skills, including design skills, yet designers face resistance from peers in developer roles when seeking to use established design methods for validating needs and generating solutions. This tension can make designers feel unwelcome at hackathons, harming efforts to attract a more diverse participant pool.

Climate change and rapid urbanisation constitute wicked problems to which the design community must respond. This paper focuses on hybrid smart Nature Based Solutions (NBS) which combine digital, engineered and natural components. Based on case studies and interviews, this paper presents a model to enable manufacturing organisations to navigate the complexities of designing and commercialising such complex systems, focusing on the inter-organisational partnerships required and mitigation techniques to address complexities throughout the project lifecycle. This work challenges existing concepts of hybrid, complex systems to account for NBS and their unique complexities. We argue that smart Nature Based System is a more apt way to conceptualise these solutions which incorporate digital twin, A.I and weather data to deliver urban resilience and sustainability.

This paper explores how creative preservation, affected by a regulatory framework, unfolds in the design of complex systems. Based on a case study of the Boeing 737 aircraft, it focuses on the role of grandfather rights, as part of the regulatory framework of aircraft design, as a precursor for creative preservation. The paper analyzes three design decisions related tot the evolving Boeing 737 aircraft models over a period of six decades and highlight the changing logic of creative preservation in relation to technology maturity, increasing complexity of design decisions, and expanded stakeholder involvement. Overall, the paper demonstrates that the management of design heritage is a ‘living system’ and that foundational practices may slowly become ineffective.

Teams have been favored due to the diverse knowledge access. However, diversity can also have negative effects, and team outputs can be influenced by many factors, such as psychological safety. While the effects of psychological safety have been studied, its development has received less attention. Prior research in this area has focused either on specific populations or cross-sectional effects. To add to this area, this study examined the longitudinal development of psychological safety in engineering capstone students: how it evolves, and whether this can be influenced by team-related experiences. This study showed that although psychological safety did change meaningfully with time, neither time nor experience alone could capture the change. The results could shed light on the evolution of psychological safety, as well as what factors could potentially influence its development.

The study investigates the integration of artificial intelligence (AI) into the product development process (PDP). It addresses two key research questions: which AI technologies exists to support designers across different phases of the PDP and which specific design activities these technologies enhance. Employing a systematic literature review, the research identifies AI technologies utilised in the design process and categorises them across the various phases of PDP. The findings emphasise a predominant focus on early-stage phases and the support of single activities within the PDP. Notable challenges include the lack of comprehensive end-to-end integration and limited compatibility in later phases. The study underscores the potential of AI while drawing attention to existing gaps in its adoption and the necessity for further research into cross-phase integration.

This paper explores the role of concept maps in investigating and highlighting project alignment through shared understanding within a multidisciplinary, long-term, creative, practice-based project called “Fish Project.” By combining surveys and concept maps, the case study investigates team dynamics, skill diversity, and evolving project comprehension. The project aims to integrate augmented reality and geo-locative technologies to cultivate care, community, and collaborative design through a digital fish ecosystem. Analysing concept map: metadata, topology, and vocabulary, the research highlights gaps in team alignment and provides areas for cohesive project visioning and execution. The findings underscore the importance of iterative communication tools to bridge interdisciplinary boundaries and strengthen team coherence.

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.

To specify the solution principles of a design, a material selection should be performed already in the concept phase. Based on the design constraints, inappropriate materials are removed using an attribute filter. Brittle materials are often removed using fracture toughness attribute limits, but this does not take into account the strength specific stress level and incorrectly excludes entire classes of materials. We propose a novel filtering method to account for brittle failure in material selection. Based on linear elastic fracture mechanics, we establish a relationship that correctly describes the transition between brittle and ductile materials. Representing the proposed filter on an Ashby plot, we evaluate its effect on the further material selection process. Additionally, we show how different defect sizes in the materials can be incorporated into the filtering process.

Bioinspiration offers an innovative approach to product design. A key challenge is selecting suitable biological features for complex engineering problems. The phenomenon of convergent evolution, where distantly related organisms independently develop similar functions, adds to this complexity. This study introduces novel meta-level design parameters to systematically select biological features with differing geometries yet similar functions. These parameters were derived through physical testing and numerical analysis of woodpecker-beak-inspired and Balanus-inspired structures, focusing on their impact resistance capabilities. These structures demonstrate potential for practical applications, such as in bicycle helmet liners.

Current legislative frameworks reflect a societal consensus to prioritize sustainability, incentivizing industries to integrate environmental goals into strategic objectives. Embedding sustainability into product development requires appropriate methods and tools. Technological advancements enable the utilization and analysis of operational machine data to support the development of new generations of sustainable systems and the conduction of Life-Cycle-Assessments. This research presents a method to support data-driven product development to reduce the environmental impact of new product generations of complex mechatronic systems during operation, addressing key factors such as the technical system, organizational infrastructure, and regulations. The application of the method resulted in multiple proposed design changes able to enhance machine sustainability and operational efficiency.