A design catalog is a repository of design problems and their solutions, enabling designers to explore and discover applicable solutions for their specific design challenges. Creating such catalogs has depended on human knowledge and implicit judgment, with no systematic approach established. This study aims to develop a systematic method to create a design catalog from patent documents. We utilize a large language model (LLM) to extract problem-solution pairs described in the documents, presenting them as general purpose-means pairs. Subsequently, we create a design catalog by classifying the problems using similarity-based clustering, enhanced by the LLM’s semantic text similarity capabilities. We demonstrate a case study of creating a design catalog for martial arts devices and generating new design concepts based on the catalog to verify the effectiveness of the proposed method.

Extending the lifetime of products is one objective of a Circular Economy. The lifetime of a vehicle is limited not only by wear, but also by declining customer satisfaction. Customer satisfaction is related to the different types of quality. Components aim for different types of quality. That is why modularization is seen as a possible enabler to facilitate both durability and adaptability in the vehicle structure. Additionally, extending their lifetime integrates passenger vehicles into a Circular Economy. This paper aims to define classes of components to support the development of a modular structure for passenger vehicles that is suitable for a Circular Economy. It provides four classes based on the relevance of components to customer satisfaction and their expected lifetime. This enables the targeted development of R-strategies for components.

The EU AI Act (Regulation (EU) 2024/1689) represents a significant departure from the EU’s traditionally restrained regulatory approach to commercial arbitration. The Act classifies certain use cases of AI in arbitration as potentially “high-risk” and introduces stringent compliance obligations for legal tech providers, arbitral institutions and arbitrators. This article argues that the Act’s application to arbitration disrupts the long-standing balance between party autonomy, procedural flexibility and regulatory oversight that has characterised the EU’s treatment of the field. It also highlights the challenges of reconciling its rigid framework with key aspects of arbitration – namely, party autonomy, confidentiality and procedural flexibility. The article concludes by proposing a full or partial carve-out of commercial arbitration from the scope of the AI Act’s high-risk provisions.

Organizational capability is key to achieving strategic goals and adaptability. This study applies the TASKS framework to evaluate taskload, affect, skills, knowledge, and stress using a questionnaire developed through the Environment-Based Design (EBD) methodology. A structured perception-centered evaluation was conducted to assess employees’ perceptions of organizational alignment, with middle managers’ responses serving as a reference. Findings emphasize the need for better communication, leadership engagement, and goal clarity to enhance transformation readiness. The TASKS framework’s perception-centered evaluation assesses organizational capability and identifies role-based misalignments. Future research will expand the framework’s application to validate its effectiveness and refine strategies for enhancing organizational capability.

In the context of volatile markets, characterised by a need for continuous product development involving module-wise product modifications, the importance of flexibility as an attribute of products and their production system has been increasing. This paper presents a methodological approach focusing on the flexibility evaluation of modules regarding their interfaces. The subject encourages engineers and researchers to analyse and rethink the interface design and the location of module boundaries regarding change propagation. The method was validated using the Design Method Validation System (DMVS) to determine its usefulness, applicability and acceptability. The design workshop for validation was applied to a product family of trunk lids by employees of a German car manufacturer.

Computer-aided design (CAD) has become essential for hardware product development in our industrial age. However, increasing complexity, shorter lead times, and cost pressures present new challenges. While generative AI has gained significant attention and transformed various business functions, its application in engineering design with CAD remains underdeveloped. Our research aims to explore why generative AI has not yet reached its potential in CAD, despite its prominence in other fields, by identifying key challenges through case studies and a literature review. These challenges include small datasets, difficulty representing mixed data types, proprietary file formats, and lack of advanced CAD modeling commands. We propose future developments such as high-quality datasets, a vendor-neutral format, novel neural network architectures, and expanded generative methods.

This study examines the integration of values into design methodologies, essential for guiding value-driven design processes. Values, spanning ethical, economic, and functional dimensions, influence decision-making and project outcomes. Through Principal Component Analysis (PCA), five clusters of design methodologies were identified, each addressing distinct aspects of value integration. Interviews with designers highlighted challenges in defining, formalizing, and adapting values due to their inherent subjectivity and volatility. This study, by adopting a values-centered perspective, enriches our understanding of design methodologies and paves the way for more informed methodological choices across various contexts.

Can observing opposing partisans engage in dialogue depolarize Americans at scale? Partisan animosity poses a challenge to democracy in the United States. Direct intergroup contact interventions have shown promise in reducing partisan polarization, but are costly, time-consuming, and sensitive to subtle changes in implementation. Vicarious intergroup contact—observing co-partisans engage with outparty members—offers a possible solution to the drawbacks of direct contact, and could potentially depolarize Americans quickly and at scale. We test this proposition using a pre-registered, placebo-controlled trial with a nationally representative sample of Americans. Using both attitudinal and behavioral measures, we find that a 50-minute documentary showing an intergroup contact workshop reduces polarization and increases interest but not investment in depolarization activities. While we find no evidence that the film mitigates anti-democratic attitudes, it does increase optimism about the survival of democratic institutions. Our findings suggest that vicarious intergroup contact delivered via mass media can be an effective, inexpensive, and scalable way to promote depolarization among Americans.

Various methods, such as LCA, LCC, or circularity indicators, are used to integrate sustainability into product development. However, these approaches often require extensive expertise in both processes and sustainability, which is not always available in combination. This paper introduces a framework based on the double diamond model, structured into (1) a preliminary assessment, (2) a collaborative workshop, and (3) a prioritization process. It aims to help engineers identify sustainability improvements without requiring prior expertise. A case study on an industrial digital printing system identified five opportunities for enhancing sustainability. Three measures were validated using LCA and the RPR metric. The study resulted in seven principles for sustainable printer design, with a lightweight door design, reduced number of rivets, and logistical improvements as key outcomes.

The ability to modify designs, personalize nutrition, and improve food sustainability makes 3D food printing (3DFP) an exciting emerging technology. Food materials’ complex chemistry and mechanics make it difficult to consistently print designs of different shapes. This research uses two methods to assess printed food fidelity: Manual and automated image analysis with custom-developed algorithm. Fidelity based on printed area was measured for three overhang designs (0°, 30°, and 60°) and three food ink mixtures. The manual method provided a baseline for analysis by comparing printed images with CAD images. Both methods showed consistent results with only ±3% differences in analyzing printed design areas. While the computational method offers advantages for efficiency and bias reduction, making it well-suited for fidelity assessment to assess designs.

Natural Language Processing (NLP) has been widely applied in design, particularly for analyzing technical documents like patents and scientific papers to extract engineering design knowledge. This work aims to enhance this process by integrating the Axiomatic Design methodology with NLP techniques applied to patent texts. The objectives are to (1) extract Functional requirements (FRs) and Design parameters (DPs), and (2) identify how FRs and DPs are related in text (Axiomatic relations). The second objective is particularly challenging due to limited focus on understanding semantic relations in literature, and previous studies often extract Axiomatic relations in an unstructured way. The approach achieves 60% precision for the first objective and 30-50% for the second. Moreover, a case study shows the practical application of this methodology to assist the work of designers.

Assumption-making is a critical cognitive process in design, where incomplete information is ever-present. Understanding how assumptions are formed, maintained, and adapted can offer key insights into decision-making. While theoretical explorations of assumptions exist, empirical research remains limited. This pilot study investigates how varying temporal constraints influence assumption-making while solving ill-structured problems. The challenge lies in isolating the temporal and cognitive factors at play. The early insights reveal that task ambiguity, contextual framing, and time constraints play significant roles in shaping responses, highlighting the dual nature of assumption-making as both adaptable and resistant to change. The insights highlight the importance of strategic task design that balances ambiguity and structure to deepen our understanding of assumption-making.

Product development is critical for sustainable development, yet sustainable design practices remain under-implemented in the industry. This paper explores the aerospace sector, addressing its specific barriers and enablers to sustainable design. Through a comprehensive literature review, group discussions, and expert group interviews, this study introduces an impact model with essential elements for enabling sustainable product development in aerospace and explains their causal relations. Five key elements were identified: business drive, sustainability implementation, knowledge, ownership, and collaboration. In addition to the impact model, the paper discusses aerospace-specific challenges and opportunities for sustainable product development. Findings from this study offer a practical framework for practitioners and researchers to plan and implement interventions in organizations.

Virtual Reality (VR) has garnered significant attention as a potential ‘empathy machine’ for its ability to simulate firsthand experiences of others’ perspectives. However, recent research reveals conflicting evidence regarding VR’s effectiveness in fostering empathy, with outcomes ranging from strong positive effects to complete ineffectiveness. By analyzing both subjective experiences and objective measures, this study aims to elucidate the relationship between VR design and human empathy, addressing three prevalent perspectives on the field’s inconsistencies: flawed mechanisms, ineffective design, and mismatched methodology. The findings contribute to the theoretical understanding of empathic VR and provide practical implications for designing effective VR-based empathy interventions in engineering contexts.

sEMG biofeedback therapy can be used to treat arm paresis after a stroke by using surface mounted EMG electrodes to measure muscle activity in the forearm and provide visual feedback to the patient. Since current sEMG biofeedback systems rely on manual placement of a few large electrodes, they cannot be used to discriminate between individual extrinsic finger muscle activities, which is necessary for training everyday hand movements. In this paper, we present our concept for the development of a device that enables the resolution of individual finger activities. We have developed a method that uses and reduces information from large-scale sEMG scans of a person's forearm to identify suitable locations for the strategic placement of a minimal number of electrodes in a personalised forearm sleeve, which is the key component of an effective biofeedback device for everyday hand movements.

This article explores the use of large language models (LLMs), specifically GPT, for enhancing information extraction from unstructured text in political science research. By automating the retrieval of explicit details from sources including historical documents, meeting minutes, news articles, and unstructured search results, GPT significantly reduces the time and resources required for data collection. The study highlights how GPT complements human research assistants, combining automated efficiency with human oversight to improve the reliability and depth of research. This integration not only makes comprehensive data collection more accessible; it also increases the overall research efficiency and scope of research. The article highlights GPT’s unique capabilities in information extraction and its potential to advance empirical research in the field. Additionally, we discuss ethical concerns related to student employment, privacy, bias, and environmental impact associated with the use of LLMs.

Text-to-Image Generative AI (GenAI) platforms offer designers new opportunities for inspiration-seeking and concept generation, marking a significant shift from traditional visualisation approaches like sketching. This study investigates how designers work with text-to-image GenAI during inspiration-seeking and ideation, aiming to characterise designers’ behaviours through designer-GenAI interaction data. Analysis of 503 prompts by four designers engaging in a GenAI supported design task identifies two distinct behaviours: exploratory, characterised by short, diverse prompts with low similarity; and narrowing, characterised by longer, high-similarity prompts used with detail focused variation functions. The findings highlight the value of GenAI interaction data to reveal patterns in designers’ behaviours, offering insights into how these tools support designers and inform best practices.

Requirements engineering is in the design process, translating stakeholder needs into actionable and well-defined specifications. While existing design enablers and tools provide partial solutions, they often fall short in addressing essential aspects such as real-time feedback, lifecycle management, and the use of controlled vocabularies. To bridge these gaps, the Requirements Authoring Design Enabler (RADE), a macro-enabled Excel tool, is presented to support requirement authoring, tracking, and management. RADE integrates features like automated feedback, a dual-mode interface, robust change tracking, and controlled vocabularies. The tool was tested with pre-service engineers with user feedback informing iterative refinements. RADE addresses key challenges in requirements engineering, demonstrating its potential to enhance design outcomes across various domains.

Companies in the development of cyber-physical systems are responding to the ever faster changing requirements of their own products by implementing agile methods. Until now, however, there has been a lack of ways to determine the true effects of agile transformation on their own processes to operate them in a targeted manner. This paper presents an impact model that defines factors that can be used to describe process changes and outlines the interdependencies between the individual factors and describes the influence of known agile methods. This allows the benefits of agile methods to be presented transparently and objectively.