The main objective of this paper is the investigation of possibilities to enhance the resilience and sustainability of technical systems by means of function-oriented system design. Design for Resilience aims at creating technical systems capable of withstanding and adapting to internal and external changes. Design for Sustainability has the objective to create solutions that meet present needs without compromising future generations, for instance by means of avoiding environmental destruction, improving resource efficiency, and achieving a long-term ecological balance. Function-oriented design is the most abstract form of solution generation. This paper presents arguments to verify the hypothesis that function-oriented system design is a prerequisite for both Design for Resilience and Design for Sustainability, discusses connections between both aspects, and proposes a common process.

In response to the environmental challenges posed by climate change and shortened product lifecycles, businesses must prioritize the design of sustainable and adaptable products. Upgradeable products present a viable solution to incorporate environmental impacts by maintaining technological relevance and addressing evolving user and customer needs, thus minimizing resource waste. To develop an effective design support for this, it is essential to create a specified method-testbed. This work employed a guideline-based expert study, applying qualitative content analysis to eight interviews. The analysis identified 38 factors crucial for supporting the development of sustainable, upgradeable mechatronic systems. These factors were consolidated into distinct objectives, resulting in 13 requirements that represent the method-testbed for a design support aimed at strategic upgrade planning.

The complexity of modern products poses significant challenges for the industry. Existing model-based systems engineering (MBSE) methodologies often lack the scalability and mechanisms for assessing maturity required to meet diverse organizational needs. Implementing MBSE all at once is impractical due to the complexity of changes required and resistance to change among employees. The MBSE Cube was developed as a scalable, demand-oriented framework to support organizations transitioning from no systems engineering processes or document-based approaches to model-based practices. This artifact-based approach guides the systematic creation of development artifacts, forming the foundation for MBSE implementation. By integrating abstraction levels, system views, and maturity levels, the Cube helps organizations assess their state and develop tailored MBSE adoption strategies.

Decision-making in product development is a complex process that benefits from leveraging past experiences. This paper presents an ontology-based framework to facilitate decision-reuse in product development by classifying current decisions within a structured scheme. The proposed decision-reuse ontology provides similar past decisions, linking them to their classifications and offering SPARQL queries, conditions, and decision outcomes. By integrating the ontology with other domain-specific ontologies, it supports product developers in making informed decisions based on historical knowledge. The resulting decision outcomes, classifications, and metadata are fed back into the decision-reuse ontology, ensuring a continuous cycle of knowledge enrichment. This approach not only enhances decision-making but also fosters knowledge transfer throughout the development process.

Functionalism has been increasingly challenged by legal comparatists questioning its nature and suitability. These epistemologically-focused critiques have effectively dichotomised modern comparative law methods, leaving two undertheorised possibilities, namely, the functionalist model—understood in conventional positivist (and substance-ontic) lexes—and emergent postmodern approaches as typified by Pierre Legrand’s system of ‘negative comparative law’ protocols. This article explores an often-neglected alternative grounded in process-relational philosophy. As shown by re-examining Ernst Rabel’s original model, its central claim is that a synthesis of early functionalist theory and process-relational principles exposes postmodern critiques as imprecise and overstated—arguably misconceiving key notions of ‘difference’ and ‘similarity’, and consequently failing to appreciate how greater awareness of the correct order and relationships between levels of abstraction can enhance the nature and possibilities of comparative legal knowledge.

This study proposes an ML-based interactive framework for early-stage design, addressing the challenge where physical prototypes are accurate but costly, and virtual prototypes are affordable but less reliable. The NN-based human-in-the-loop framework integrates pre-training and fine-tuning techniques to reduce reliance on extensive physical prototyping while maintaining model accuracy. Using projectile motion as an example, the framework demonstrates its ability to guide design by iteratively updating models based on limited experimental data and human expertise. The results highlight the framework’s effectiveness in achieving performance comparable to models trained on larger datasets, offering a cost-effective solution for creating accurate design models.

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!

This paper focuses on the development of a viable business model for the PISCES Living Lab, which seeks to address plastic pollution in Indonesia. The overarching aim is to transition it from a project-based initiative to a self-sustaining service enterprise. The paper introduces a new modified engineering design process as a workshop template to guide an interdisciplinary team in creating a business model for a service-oriented living lab. A four-day workshop was conducted in Banyuwangi, Indonesia, involving a diverse group of stakeholders from the project, and the final outcome was the creation of a Business Model Canvas outlining the core components of the PISCES Living Lab’s business model. The findings demonstrate the effectiveness of integrating the engineering design process with business model innovation, offering a structured yet flexible approach to developing self-sustaining Living Labs.

AI is becoming an important part of complex products and systems (CoPS), transforming them into complex intelligent systems (CoIS), on which our society depends. Traditionally, system development relied on model-based approaches, and the emerging data-driven approaches offer new possibilities. This paper explores the intertwining of model-based and data-driven approaches in emerging CoIS through a comparative case study of their role in cloud-based automotive systems, which are part of the transportation system. The findings show that data-driven approaches not only complement model-based approaches but also play a pivotal role in the evolution of CoIS.

Biodesign has grown significantly in the last decade as an approach focused on designing with biological materials, processes and systems. The inherent transdisciplinarity of biodesign enables it to cut across multiple fields. In this work, we look at how biodesign has recently been applied within Human-Computer Interaction (HCI), a disciplinary field that focuses on the design, development and study of interactive technologies. Subsequently, Biological-HCI (Bio-HCI) has emerged as a rapidly growing and evolving area of research at the intersection of biodesign and HCI. To highlight the nascence of Bio-HCI, we examine three of our own Bio-HCI projects – SCOBY Breastplate, B10-PR1NT and $\mu $Me – as case studies that exemplify how biodesign is being explored through specific, situated practices with a variety of interactive technologies. Through these cases, we identify potential themes and opportunities for Bio-HCI as it continues to push current understandings of computational interaction and promote more sustainable technological futures.

This paper presents the Chinese Cizhou Kiln culture via a User Experience (UX) based mobile APP. By applying Garrett’s UX methodology, this research proposes a ‘Culture-UX Integration Framework’. Section 1 introduces the digital background for heritage designs. Section 2 describes the Cizhou Kiln development challenges. Section 3 provides the examples of the existed crafts APP designs. Section 4 illustrates the Hi-Fi prototype. Section 6 contains the evaluation and validation parts of this work, and this paper ends by Section 7, the conclusion. This paper contributes a novel of the knowledge that design paradigm balancing heritage preservation and functionality, validated via testing. The authors’ framework offers replicable methods for digital heritage design, By merging aesthetics, function, and culture, it advances preservation.

Demographic change is one of Germany’s most pressing social and economic challenges. Using data from a representative telephone survey, we analyze how well informed respondents are about the magnitude of demographic change and what factors influence the accuracy of their beliefs. We find that respondents tend to overestimate the old-age dependency ratio when considering the current and long-term demographic situation separately. However, their beliefs regarding the change of the old-age dependency ratios over the considered period are not far from the projected change. A better understanding of the German statutory pension insurance plays an important role for more accurate beliefs.

Deriving parametric CAD geometries from topology optimization results is a time-consuming step in the development of lightweight components, as the topology developed for the given building space corresponds to a non-parametric integral model. A labor-intensive constructive geometry repatriation is necessary and the choice of usable manufacturing processes is limited due to the integral design. Depending on the quantity, the components are often cast or additively manufactured. These restrictions prevent the economic use of topology optimization. Against this background, a methodology was developed with which topology-optimized structures can be converted into a production- and lightweight-oriented differential design for any quantities. The applicability and added value of the methodology are validated by successfully applying it to a mechanical engineering component.

With increasing servitization, manufacturers are transitioning from solely selling products to integrated products and services. While the SAPPhIRE model of causality effectively represents technical systems and aids in product design activities like analysis, synthesis, and assessing design novelty, few studies have explored its extension to services. Previous research extended SAPPhIRE constructs to capture causality in Service Systems. This research compares SAPPhIRE models for services with Object Process Methodology (OPM), a benchmark for systems modeling. Results show that SAPPhIRE not only captures details represented by OPM but also provides additional useful information for service representation. The causal description of products and services using SAPPhIRE helps understand and improve existing service systems, trace root causes of issues, and foster creative ideation for new designs.

Researchers in history and the historical sciences explore the use of generative AI (GenAI) systems for reconstructing destroyed artifacts. This paper poses a novel question: Can such GenAI systems generate evidence that provides new knowledge about the world or can they only produce hypotheses that we might seek evidence for? Exploring responses to this question, the paper argues that (1) GenAI outputs can at least be understood as higher-order evidence (Parker 2022) and (2) may also constitute de novo synthetic evidence.

Current transitions, such as digital and ecological ones, bring new challenges for organizations, characterized as unknowns. Addressing them requires new management paradigms for which design-based methods show promise. Yet their organizational implementation remains limited, what this paper investigates. Based on a two-year collaboration with a French healthcare company, the study involved developing, delivering and evaluating a four-day training program. Based on interviews and evaluations from 65 participants, results indicate high satisfaction, significant habit disruption and intent to adopt design-based tools. Due to the development of a common language in the organization and the emphasis on learnings’ co-creation, this training had a transformative power. Thus, highlighting its practical value and opening pathways for exploring its long-term impact on organizational practices.

To investigate human behavior in spatial environments, researchers commonly implement video-based behavioral analysis, which is time-consuming and tedious. With improvements in algorithmic performance and expansions in behavior datasets, vision-based AI demonstrates great potential to support human behavior analysis and understanding in design research automatically. To bridge this gap, we proposed a framework for utilizing vision-based AI models for spatial behavior analysis tasks in design research and utilize it in applications. This work offers new insights for design researchers, pointing toward strategies for refning AI-enhanced human behavior analysis and integrating emerging AI technologies into the study of human behavior in design settings.

This paper discusses the development of WeBreath, a wearable system designed to monitor respiratory health for individuals with chronic respiratory diseases and sleep disorders. The project brought together experts in engineering, industrial design, and medicine, requiring an iterative process to address user needs, medical requirements, technical feasibility, and commercial constraints. Beyond describing the product, the paper examines how multidisciplinary collaboration shaped its development, highlighting challenges such as regulatory requirements, user adoption, and market readiness. It explores design challenges from a user experience perspective, balancing functionality with comfort, wearability, and usability. The paper provides insights into structuring teamwork in medtech projects, showing how user-centred design principles guided decision-making and shaped the outcome.

The growing demand for responsible resource use presents a significant challenge in today’s time-, cost-, and quality-driven product development. Therefore, this paper explores integrating creativity techniques into early development phases to achieve innovative, lightweight, and sustainable designs. Using a case study on extending the useful life of a bicycle trailer, a generic framework is introduced, aligning lightweighting and sustainability objectives in the idea generation process. Lessons learned highlight the critical role of the moderator, the importance of an iterative process, and the need for guidelines on method selection. The findings provide actionable recommendations for fostering sustainable innovation in lightweight design and form a basis for further research on adapting creativity techniques to sustainability goals.

Carbon fiber-reinforced plastics (CFRP) have a great lightweight potential due to their high strength-to-weight ratio. However, new challenges arise due to complex production processes and the large number of design parameters that are subject to variations. This study advances simulation methodologies to address these challenges by modeling the entire CFRP production process while accounting for fiber angle variations at each step. The approach enables prediction of assembly stresses and deformations by utilizing surrogate models, and supports further approaches, such as tolerance optimization and process refinement. Two case studies demonstrate the effectiveness of the method and illustrate its potential to support the optimization of the production process.