Sensor-integrating, gentelligent components “inherit” data on operational loads from one generation to the next for design optimisations and require an optimal sensor placement (OSP) to make accurate decisions based on this data. The OSP can be very time-consuming, and most studies focus only on one load case. To address this issue, a methodology for OSP for several load cases, based on the region-growing algorithm for FEM simulation data (RGA4FEM) for solution space reduction, is presented. For validation of the methodology’s applicability, a case study is carried out for a boom of a satellite antenna. The results show that region-based approaches are slower to converge but need smaller populations to find global optima with a genetic algorithm. Furthermore, high robustness is achieved for the most demanding parameters on all load cases in a single optimisation.

Radiotherapy involves applying radiation doses to tumor cells and healthy tissue. To protect healthy tissue, an accessory called a bolus is used. Traditional boluses face issues such as limited adaptability and inconsistencies in radiodensity. This study proposes a low-cost process that uses 3D scans and additive manufacturing (AM) to design and produce custom boluses. The method uses a 3D scanner as an alternative to standard medical image acquisition, processes the images with CAD and mesh optimization, and then manufactures the pieces through additive manufacturing using polylactic acid (PLA) as the printing material. By optimizing the fill percentage, radiodensity was controlled, resulting in boluses that achieved a 65% cost reduction in material and an 81% savings in imaging compared to the traditional method.

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.

The rise of mindfulness apps has integrated these tools into daily life, but concerns arise about preserving traditional practices and the ethical use of manipulative dark patterns that undermine user autonomy. This study examines the impact of dark patterns on user perceptions, engagement, and trust in mindfulness apps using expert reviews, surveys, journaling, and interviews. Three apps—Calm, Headspace, and Insight Timer—were analyzed for dark patterns, with participants documenting their experiences and perceptions. The findings underscore the need for ethical design practices to enhance trust and informed decision-making while highlighting the influence of dark patterns on user behavior and experience.

Interdisciplinary work environments, such as in the engineering of Cyber-Physical Systems (CPS), face significant communication challenges due to the need for collaboration among different engineering domains. This study examines communication comprehensibility within a CPS research project involving 30 researchers from multiple universities. We conducted two surveys to assess the status quo of communication comprehensibility. While most research descriptions are generally understandable, significant barriers exist due to technical terminology and differing epistemic foundations. The study presents a systematic approach to assess communication comprehensibility in interdisciplinary projects and highlights the need for support in enhancing communication. Further data from multiple projects is needed to develop effective communication models for interdisciplinary teams.

The marine industry is increasingly adopting platform and modular design strategies while facing growing sustainability regulations and emission constraints. This paper proposes an approach that integrates scope 3 upstream CO2 emissions (i.e., procurement) into a Decision Support Environment (DSE) for design space exploration of alternative modular ship design concepts. The DSE, deployed in the conceptual design stage, enables simultaneous testing of various cruise ship configurations regarding CO2 emissions using a bottom-up approach with parametric CO2 models. It leverages data-driven models from existing databases or AI-generated data exemplified in a case study on the hotel system of a cruise ship illustrates how parametric design variables influence CO2 emissions, demonstrating a preliminary result of a prescriptive study in collaboration with a major international ship manufacturer

This paper demonstrates how the Portfolio of Capability Constraint Network (PCCN) facilitates modeling and analyzing complex manufacturing networks by framing them as constraint satisfaction problems (CSPs). These models face high complexity due to numerous n-ary constraints and large solution spaces, posing challenges for standard solution algorithms. Existing CSP remodeling approaches were reviewed but found unsuitable for the specific needs of PCCNs. As a result, tailored design guidelines and heuristics were developed to reduce problem complexity effectively. The applicability of these guidelines was validated using a use case involving the production of a multi-material shaft with tailored forming technology. Results showed significant efficiency gains in solution searches, emphasizing the practical value of the proposed methods in simplifying and optimizing PCCN-based models.

Advances in information and communication technology (ICT) foster smart systems. Seamless data flows between stakeholders are crucial for their functioning. Designing communication systems to manage data exchange in distributed multi-stakeholder networks is challenged by the complexity of diverse stakeholders with varying interests and data needs. This requires a comprehensive understanding of data flows and communication dynamics. This paper investigates methods for modeling and analyzing data-related links between stakeholders in complex systems. After defining requirements and reviewing available methods, an approach combining dependency and structure modeling (DSM) and systems modeling language (SysML) is identified as most suitable. This is applied to a case study of autonomous buses in public transport, demonstrating its applicability and providing a foundation for further work.

Many optimization models have been proposed to assist original equipment manufacturers (OEMs) in pricing and production planning for remanufactured product; however, most assume deterministic yield rates for takeback products during disassembly. In practice, yield rates are influenced by interdependencies among subparts and disassembly operations. To address this limitation, this study enhances a previously established optimization approach by incorporating yield uncertainty into the disassembly process using a diffusion process framework. The effectiveness of this approach is validated through case studies on two distinct products: a smartphone and a recreational boat engine. The results demonstrate that integrating stochastic yield modeling allows stakeholders to make more informed decisions, ultimately improving both economic performance and environmental sustainability.

Product Line Engineering (PLE) and Systems Engineering (SE) are critical for developing complex systems, yet current methodologies inadequately address the integration of variability across multiple layers of system design. This study introduces an integrated variability modeling framework based on FODA and aligned with ARCADIA’s MBSE method, addressing operational, functional, and constructional viewpoints. Validation using Renault’s Advanced Driver-Assistance Systems (ADAS) showcased reduced design time and enhanced configuration adaptability. Key challenges, such as aligning feature models and managing dependencies, were addressed through a modular, layered strategy. The proposed approach ensures flexibility, scalability, and system integrity, offering a robust framework for diverse domains.

Recent advances in AI offer promising opportunities for creative design, particularly through the generation of inspirational images. While prior research has explored the general benefits and limitations of text-to-image tools, there is significant potential in overcoming these constraints by investigating agile, multimodal prompting to facilitate more project-appropriate human-AI interaction. We present the development of a system designed to support both text-based and sketch-based image generation, serving as a research artefact for studying creativity support through multimodal Generative AI. The system enables dynamic dialogue interaction and visualization of the respective contributions. This paper focuses on the development of this AI system as a research artefact to enable future research through design, exploring how multimodal prompting can influence the design process.

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.

The intersection of design and narrative plays a crucial role in shaping meaningful experiences. While narrative experience has been explored in product design, its role in service design remains underdeveloped. This study introduces a narrative-driven service design approach, integrating narrative to enhance user experiences. Using a Research through Design methodology, ten digital service prototypes were developed, embedding “stories of moments of joy” as a design foundation. Findings suggest that starting with narratives fosters deeper emotional engagement and enhances service interactions. Participant feedback highlights how this approach provides an alternative to traditional problem-solving models, emphasizing narrative-driven innovation in service design. By positioning narrative as a central design element, this study contributes to advancing service design methodologies.

Model-based Systems Engineering (MBSE) supports managing complex engineering projects. A pivotal element of MBSE is the concept of views which provide tailored representations of a system model to address stakeholder concerns. Despite standards describing the use and generation of views, the adoption and practical implementation of MBSE views and viewpoints in industrial practice remain insufficiently explored. Interviews with German practitioners reveal a disconnect between theory and practice: views and viewpoints and the involvement in MBSE are often limited to technical experts, excluding non-technical stakeholders. High complexity, abstract representations, and tool-related barriers impede broader engagement. The findings suggest stakeholder-specific, accessible visualizations integrated into easy-to-use tools to improve understanding, collaboration, and decision-making.

Variation simulation approaches are frequently used to analyse the effects of geometrical variations on the final product quality. Various software tools are used during product development as they strongly differ in their specified goals, the context of use, and users. Although a few workarounds and information-sharing strategies exist, switching software usually results in the simulation model being built from scratch, leading to redundant manual effort and uncertainties. This paper examines the potential and limitations of the Quality Information Framework (QIF) information model in improving collaborative work within a heterogeneous simulation software landscape by exchanging variation simulation model-related information in a standardised Model-Based Definition sense. An application scenario shows how QIF can bridge the gap between tools used in early and late design phases.

Artificial Intelligence (AI) techniques are increasingly explored to support design activities within the manufacturing context mainly driven by the development of AI technologies. However, few studies were conducted in practice from industrial perspectives. This research aims to understand the opportunities and challenges of AI in design in the real world. A workshop involving twenty-five participants from more than ten manufacturing firms is organised to collect relevant information. The opportunities and challenges identified are categorised by adopting a readily available data-driven design framework. Seven research directions are proposed accordingly to address the industry challenges and opportunities. This research serves as a guide for ensuring future AI in design research and applications are grounded in practice to bridge the gap between academic research and industry practice.

The application of Generative Artificial Intelligence (AI) in early-stage design processes has emerged as a promising method for generating innovative solution concepts. However, AI-driven concepts may introduce secondary problems when implemented practically. This study proposes a systematic framework integrating Generative AI (GPT-4o), patent analysis using Retrieval-Augmented Generation (RAG), and Failure Mode and Effects Analysis (FMEA) to predict, evaluate, and mitigate potential risks. Applied to a case study on nickel recovery through froth flotation, the framework significantly enhanced the feasibility, usefulness, and sustainability of solution concepts. The research highlights the scientific contribution and practical benefits of combining Generative AI with structured risk-analysis methods for sustainable innovation.

Engineering design has recently undergone a paradigm shift led by generative artificial intelligence (AI). The Generative Design (GD) paradigm utilizes generative AI tools (e.g., large language models) to define the objective space and computationally exploit the design space. This is a drastic shift from the roles of human designers in the Traditional Design (TD) paradigm which consists of manual design-objective space co-evolution, and has created a research gap for Generative Design Thinking (GDT): how a designer thinks and cognitively approaches the design process during GD. To fill this gap, we propose the Paradigmatic Design Thinking Model which uniquely defines design thinking as situated within three factors (Design Cognition, Design Tools, and Design Methodology) and use it to explain design thinking in two paradigms: Traditional Design Thinking and Generative Design Thinking.

There is great potential for engineering design approaches in medicine to personalize treatments according to unique patient physiology and needs. However, it is challenging to optimize solutions such as medical implants given the complex biomechanical interactions between the body and implant. Here, we review personalization for clinical needs, biomechanical modelling, and computational design for interbody spinal cage implants. By reviewing relevant literature, research suggests specific clinical needs are addressable by redesigning cages with multi-objective optimization or artificial intelligence methods integrated with finite element modelling of the spine. Such an approach is generalizable to further biomechanical design cases, where personalized design provides promise to deliver higher quality solutions for the clinic.

Organizational design implementations frequently fail, with existing dominant frameworks and tools, such as the ever-present maturity assessments, falling short in addressing the complex, nonlinear nature of socio-technical systems (STS). This paper introduces PhylOrg, a methodology leveraging phylogenetic analysis to guide organizational design by mapping evolutionary pathways of socio-technical traits (STTs). By identifying coherent and efficient sequences of change, PhylOrg minimizes resistance and aligns initiatives with organizational contexts. Grounded in theories of complex adaptive systems (CAS) and evolutionary processes, PhylOrg proposes to offer prescriptive, context-sensitive guidance to Organizational design leaders. A pilot study demonstrates PhylOrg’s potential, highlighting foundational evolutionary traits as prerequisites for more advanced capabilities.