Students, educators, and professionals find value in industrial design students participating in internships, however, there is currently no approach for evaluating the quality of internships students are participating in. This research addresses the need for a standardized metric to evaluate industrial design internships. During a two-year longitudinal study conducted at three comprehensive universities, data were collected on internship experiences. Using this data, the authors developed a weighted ranking approach, providing a valuable tool to evaluate internships’ quality and relevance. This ranking fills a critical gap, offering unique insights for students, academic programs, and internship providers to assess and enhance internship quality, currently unaddressed by existing tools.

Achieving Net Zero requires designers to have a better understanding of the product use with studies showing user behaviour, cultural norms, seasonality and product interactions concomitantly dictate energy consumption. Data on product use can support data-driven design processes that have been shown to improve the efficiency of existing products. The paper reports a method that generates data for data-driven design processes from non-intrusive load monitoring (NILM) of household energy consumption data. The method produced appliance classification accuracies of 0.9984 while reducing sample size, sampling frequency and machine learning model complexity showing potential for it to be deployed at scale across communities.

The emergence of new technologies, such as additive manufacturing, and places to promote access to these equipment’s, such as fablabs and makers space, has supported the development of new methodologies based on prototyping. From problem definition to customer validation, prototypes can support the different phases of the innovation process. The biggest challenge being to design the right prototype to address the objective of each phase. Here, we propose to transpose and develop a model from human-computer interaction (Houde & Hill, 1997) (Yang, 2005) to the field of design sciences. The model intends to separate design issues into the “role”, the “look and feel” and the “implementation” axes. Next, we illustrate its potential through the characterization of different prototypes fabricated within the product development process of a tool design to unbend electric pylons.

Product development is a dynamic, multidisciplinary field shaped by evolving customer demands and the need for individualized products, increasing product variety. Key factors include economic performance, customer satisfaction, and sustainability. Lightweight design drives innovation by enhancing weight-specific performance, optimizing resources, and reducing CO2 emissions, especially in transportation. However, conflicts arise as lightweight design focuses on individual variants, neglecting broader product family implications, while Design for Variety strategies often exclude lightweight design. This study examines the interplay between product variety and lightweight design, proposing a measurement framework to support the development of variant products and their components within product families in the context of lightweight design.

This study aims to detect the ability of professors to distinguish design assignments generated by students with and without using AI. Ten students were recruited to undertake a conceptual design task twice, one with and one without the help of AI. 105 higher-education associate, assistant and full professors from industrial and product design programmes were recruited to assess the generated designs using a 7-point Likert Scale with nine indexes. The results indicate that assessors have moderate ability to distinguish between design assignments of students using AI and those where students did not use AI. Three cues to suggest the risk of the design assignment is made with AI instead of students who did not use AI were identified. By considering the three cues, lecturers distinguish design assignments generated by students with or without AI.

AI is increasingly used for systems and companies are integrating Machine Learning methods as well as Generative AI into modern products. For Systems Engineering this leads to new challenges, for example due to the increasing importance of data quality, data privacy or new legislation. This article highlights key challenges arising from the integration of AI components into technical systems and discusses the impact on classical role models for Systems Engineering. The paper presents results from a literature review as well as a view on how the development of AI-based systems is transforming traditional Systems Engineering from perspective of design teams. New demands on data quality assurance and legal risk management as well as establishing new roles in Systems Engineering are discussed. In addition, theses for shaping the future of Systems Engineering are presented.

Using an original method, we find the algebra of generalised symmetries of a remarkable (1+2)-dimensional ultraparabolic Fokker–Planck equation, which is also called the Kolmogorov equation and is singled out within the entire class of ultraparabolic linear second-order partial differential equations with three independent variables by its wonderful symmetry properties. It turns out that the essential subalgebra of this algebra, which consists of linear generalised symmetries, is generated by the recursion operators associated with the nilradical of the essential Lie invariance algebra of the Kolmogorov equation, and the Casimir operator of the Levi factor of the latter algebra unexpectedly arises in the consideration. We also establish an isomorphism between this algebra and the Lie algebra associated with the second Weyl algebra, which provides a dual perspective for studying their properties. After developing the theoretical background of finding exact solutions of homogeneous linear systems of differential equations using their linear generalised symmetries, we efficiently apply it to the Kolmogorov equation.

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.