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.

Additive manufacturing (AM) enables the creation of complex internal geometries, including cooling channels. Yet, the impact of AM-induced surface roughness on their fluid dynamics remains underexplored. The goal of this study is to provide insight into the effects of surface roughness on the fluid dynamics of AM channels. A parametric surface roughness model and computational fluid dynamics (CFD) simulations were employed to examine three representative AM channel cross-sections: diamond, droplet, and circular. The findings indicate that diamond profiles result in higher pressure losses and turbulence intensity compared to the other cross-sections. In contrast, droplet profiles exhibit lower pressure losses and turbulence intensity compared to diamond profiles, while circular channels remain optimal in non-overhang areas.

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.

The rapid increase in Waste Electrical and Electronic Equipment (WEEE) presents significant environmental challenges, requiring the development of efficient and effective disassembly and recycling strategies. Although extensive theoretical research has explored product redesign to facilitate automated disassembly, practical validations of such approaches remain limited. This study examines the technical feasibility of applying design-for-disassembly principles to enable automated dismantling processes using general-purpose robotic systems. Using a consumer electronic remote control as a case study, this paper demonstrates how targeted product redesign can improve compatibility with robotic systems during disassembly. The findings provide valuable insights into sustainable product design and end-of-life management, with implications for supporting a circular economy.

Crafting the design brief is often the first task of the design process and an arduous one. Design brief serves as the guiding beacon for the designer or design team to understand needs and envision intent, position stakeholders, qualify requirements, identify key criteria, outline objectives, and clarify if the ‘task’ is in line with the ask. Literature reports on the process of ‘briefing’ and ‘reframing’, and further articulates the structural components of a brief. Vision, Need statement, Criteria; and Goals characterise the final state of a brief, yet designers struggle with the process. This paper investigates the quality and structuring of design briefs developed by novice designers, individually versus in multi-disciplinary design teams, to assess the implication of teaming up and finds a significant improvement. After all, design is a team sport!

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.

The industrial application of additive manufacturing (AM) necessitates close collaboration between design and manufacturing. However, significant challenges persist throughout the product development process. To explore these challenges, we conducted interviews with 11 engineers from different companies utilizing AM technologies. These interviews revealed recurring themes, such as a lack of AM-specific mindset and uninformed decision-making, which pose challenges across different phases of product development. The identified challenges, along with proposed solutions and practices, were mapped to specific product development phases, providing a scope for development frameworks for AM. Our study indicates that while some challenges are phase-specific, others impact the entire product development process. Operational solutions for different development phases are still missing.

Customer engagement is crucial for success and innovation in digital businesses, but its impact on digital startups, particularly on business performance, is underexplored. This study investigates the relationship between customer-related digitalization factors, engagement, and business performance. Using a cross-sectional survey and Partial Least Squares Structural Equation Modeling, data from 125 startups were analyzed. The findings reveal that digitalization factors, encompassing Data Security, Transparency, Consumer Reviews, and Effective Communication, significantly impact customer engagement and digital business performance. Additionally, customer engagement mediates the relationship between digitalization factors and digital business performance, highlighting its critical role in fostering customer loyalty, communication, and co-creation.

Challenges of increasing system complexity and the need for interdisciplinary collaboration are prompting companies to reorganize towards Systems Engineering (SE). As part of the implementation of large-scale transformation programs, transformation progress is of great interest to management and employees involved. Existing maturity models lack measurable variables and reliable forecast. For this reason, a maturity model for evaluating SE Transformation is developed, that builds on quantitative metrics and enables an overarching view on transformation considering cultural aspects. Literature-based criteria for evaluating SE Transformation lay the foundation for measures and referenced metrics and indicators. Due to its data-centricity, the model presented enables a more comprehensive, fact-based decision-making basis for the design and steering of SE Transformation programs.

This paper investigates the effectiveness of machine learning models in predicting customer-relevant functional attributes of vehicles based on selected design variables, using a limited automobile market dataset. By comparing machine learning algorithms such as Support Vector Regression, k-Nearest Neighbour Regression, and Lasso Regression, the study evaluates the models’ predictive accuracy and their potential application in automotive design. The findings highlight both the opportunities and limitations of these methods, emphasising their capacity to support data-driven decision-making despite constraints posed by dataset size, as encountered in real-world, early-stage automotive platform strategies.

The circular economy (CE) seeks to replace traditional linear models by focusing on resource reuse and circulation. However, developing effective CE business strategies is difficult due to complex user behaviors and product flows. Existing scenario analysis tools often rely on survey-based conjoint methods, raising concerns about discrepancies with real purchasing patterns. This study introduces a data-driven simulation approach that employs a consumer preference model and product circulation processes based on actual operational data. Applied to a second-hand PC rental business, our method more accurately reproduces market behavior and reveals that targeting certain customer segments can enhance profitability and resource utilization. These findings underscore the approach’s value as a practical tool for pre-evaluating strategies in CE businesses.

This work develops a method to integrate operational data into system models following MBSE principles. Empirical analysis reveals significant obstacles to data-driven development, including heterogeneous and non-transparent data structures, poor metadata documentation, insufficient data quality, lack of references, and limited data-driven mindset. A method based on the RFLP chain links operating data structures to logical-level elements. Data analyses are aligned with specific requirements or functional/physical elements, enabling systematic data-driven modeling. This method improves efficiency, fosters system knowledge development, and connects technical systems with operational data.

Large Language Models (LLMs) have advanced the extraction and generation of engineering design (ED) knowledge from textual data. However, assessing their accuracy in ED tasks remains challenging due to the lack of benchmark datasets specifically designed for ED applications. To address this, the study examines how theoretical concepts from Axiomatic Design Theory—such as Functional Requirements, Design Parameters, and their relationship—are expressed in natural language and develops a systematic approach for annotating ED concepts in text. It introduces a novel dataset of 6,000 patent sentences, annotated by domain experts. Annotation performance is assessed using inter-annotator agreement metrics, providing insights into the challenges of identifying ED concepts in text. The findings aim to support designers in better integrating design theories within LLMs for extracting ED knowledge.

Creativity is a requirement for excellence in product engineering. Despite its important role, the concept of creativity in engineering is challenging to articulate and define, both for practitioners and researchers. What makes technical creativity unique? Existing definitions often fall short of capturing the essence of creativity in technical contexts. The process of defining technical creativity is performed iteratively. In product engineering, creativity is not an abstract concept but a practical necessity, requiring motivation, imagination, expertise and experience. Therefore, two workshops with product engineers were held and the results were used to refine the definition. A shared understanding of technical creativity, that can be applied in daily engineering practice is created, enhancing both research and practical outcomes in product engineering.

There is a need for design of synthetic organs due to the high demand of organ replacements for patients and low availability of alternatives. Recent advancements in additive manufacturing are enabling the creation of biomimetic organs with biocompatible materials suitable for use in the body. Here, we consider a design, build, test approach for creating synthetic blood vessel tubules by comparing fused deposition modelling and stereolithography printing processes. Tubules were printed with vessel diameters from 10 mm to 20 mm and wall thicknesses of 1 mm to 2.5 mm. Mechanical testing results demonstrated high elongation of tubules prior to breaking. Results highlight the possibility for designers to create flexible biomimetic structures to aid biomedical applications, which opens the doors for new types of patient treatments in organ repair and transplantation.

Design research faces growing challenges from multifaceted developments, which traditional methods and lab settings often struggle to address. New approaches are needed to bridge the gap between controlled lab settings, field studies, and these complexities. Exhibition spaces offer opportunities for dynamic, real-world studies beyond lab-based research’s limitations. This study explores a hybrid ‘exhibition-experiment’ format by examining a design exhibition on biophilic workspace design. Participants visited different design exhibits (experimental conditions) within the experiment while a suite of passive measurement devices measured their emotional and physiological responses. The findings highlight the strengths and limitations of ‘exhibition-experiments’, provide insights into the usage of technology-driven tools, and discuss them as a hybrid approach between lab and field studies.

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

Packaging waste contributes significantly to resource depletion and pollution. Despite the crucial role of packaging in product preservation, its environmental impact has become a major issue. Addressing the circularity and sustainability (C&S) of packaging by design offers a route to mitigate these impacts and reduce waste. However, integrating C&S into the current packaging design process presents significant challenges, such as conflicts between C&S and functional requirements and inadequate tools to provide packaging-specific practical solutions. To address these challenges, this study proposes a novel packaging design framework developed through literature review, brainstorming sessions, and field visits. By incorporating iterative design strategies and leveraging past design knowledge, the framework empowers designers to create packaging solutions that meet C&S requirements.

Design teams commonly need to explain the rationale or logic behind how they frame design challenges and develop a particular design concept and not others. This paper explores the use of Design Logic Visualizations (DLV) as a boundary object to enhance understanding and communication in convergent interdisciplinary engineering design environments. We developed the DLV as a new design tool, building upon existing design process visualizations like design signatures, and provide a case study from our NASA team. We then use a reflection-based autoethnographic and collaborative inquiry approach to reflect on how the DLVs influenced our team, our process, and our decision-making. The findings suggest DLVs can serve as a succinct storytelling tool, support shared understanding across disciplines and levels of leadership, and, ultimately, influence design outcomes.

The integration of Model-Based Systems Engineering (MBSE) and data analytics (DA) has introduced a novel approach, Data-Driven Model-Based Systems Engineering (DDMBSE), which combines structured system modelling with data-driven insights. DDMBSE offers the potential for improvements in model optimisation, economic efficiency and the implementation of dynamic system updates based on real-time data. However, the diverse applications of DDMBSE lack a structured overview of its use cases. This paper addresses this gap by proposing a comprehensive framework for the categorisation and description of DDMBSE use cases. It provides users with a structure to navigate within DDMBSE landscape, consolidate knowledge, and identify underexplored areas for future research. This contribution establishes a foundation for advancing the implementation of DDMBSE across industries and fostering its adoption.