The design of gear boxes is a complex challenge characterized by conflicting requirements and seemingly circular dependencies. Existing tools support engineers but focus on a single predefined design, often leading to costly iterative processes and non-optimal solutions. Solution Space Engineering (SSE) alleviates this by generating multiple designs represented by solution spaces. For this, a particular model structure is needed, and thus restructuring existing models, e.g., from industry standards. The application of solution spaces to a two-stage gear box is presented.

Despite the rapid advancement of generative Large Language Models (LLMs), there is still limited understanding of their potential impacts on engineering design (ED). This study fills this gap by collecting the tasks LLMs can perform within ED, using a Natural Language Processing analysis of 15,355 ED research papers. The results lead to a framework of LLM tasks in design, classifying them for different functions of LLMs and ED phases. Our findings illuminate the opportunities and risks of using LLMs for design, offering a foundation for future research and application in this domain.

Using foresight methods such as scenarios, possible futures can be described and anticipated. Needs and requirements as well as product properties can be derived from that, which is necessary to plan successful products for future customers. Changes that occur in the future development can be identified with monitoring and the expected future can be adjusted. A consistent understanding of the use of monitoring for the product engineering process does not exist and is developed in this paper. Thereby, monitoring is considered in the context of validation and located in iPeM.

The understanding of prototyping has changed in recent years to an approach that accompanies the product development process. This paper examines whether classic approaches from product development are also suitable for planning prototyping sequences. The stepwise process-oriented and the problem-oriented approach are discussed. A criticality assessment is proposed as a metric for the prioritization of the functional areas and a procedure is derived from this. The procedure is illustrated using an example. The result is discussed and future steps are suggested.

Interdisciplinary transition innovation, management, and engineering (InTIME) Design has been developed to overcome sustainability transition challenges in complex systems. The intersections of InTIME Design with a range of reported design for sustainability (DfS) approaches were analysed. Results demonstrate similar core principles across DfS approaches. InTIME Design accomplishes convergence of the studied approaches, and organises the DfS approaches into workflow phases, adds a complimentary wicked problem definition, and deploys systems engineering problem solving.

Generative design is now a core capability of today's CAD systems enabling broader exploration of the design space. This paper presents a desktop review of 15 tools with the aim of characterising where the tools are used in the design process and to what extent they can be considered creative.The results showed that, despite their increasing capabilities, GD tools are unable to exhibit all phases of creativity and cannot be applied in all stages of the design process. This highlights the criticality of the human in design providing opportunities for GD to augment designer creativity.

The emotional experiences significantly impact students' design processes. This research presents the EmotionProbe, a design probe tool for capturing design students' emotions while designing. The tool allows simple data collection, offering important insights into the students' design experiences, introducing the spectrum of emotions that design students go through, and showcasing various analysis approaches, allowing for a richer understanding of the design's emotional dimension. The research highlights the need for an emotional tool supporting students' emotional well-being while designing.

Over years of practice, industrial designers have developed three steps that always lead to a design solution: inspiration, ideation, and implementation. Usually, the result is a product, but sometimes it is a demonstrator, a versatile concept, combining design, engineering, and art. However, designers usually are not aware of this notion. But if the result is different, how does it affect the design process? The analysis of the contextual inquiry study of two demonstrators shows that the transformation should happen during the ideation phase, where abstract concept becomes a story.

The role of Artificial Intelligence (AI) in design is clearly growing. One of the tenets of the paper is that stimulation could be among the design processes mostly benefitting from the introduction of AI. Available contributions have been reviewed to understand the current support AI can give in design inspiration and ideation. We also reflected on what AI should and ahould not do in the future: a framework is proposed. Based on the reviewed contributions, in no case, AI is seen as a substitute of designers. Most contributions originate from the IT domain and have a demonstrative purpose.

Design problems are wicked in nature. Wicked problems are difficult to understand, formulate and solve. The literature focuses mainly on the characteristics of wicked problems, very little is available to how wicked problems (synonymous to ill-structured) should be formulated to make them well structured. Assessment of wickedness can help designers formulate problems into well-structured. This work proposes a metric for (lack of) structuredness as a measure for the degree to which a design problem is ill-structured. A Delphi-based method as benchmark for validating the metric is also proposed.

Data-driven design is believed to be empowered by machine learning (ML) with advanced pattern classification and prediction. However, research on how ML can be used to support automotive human-machine interface (HMI) design is lacking. We presented a case study of truck HMI design to understand the current data use and expectations of ML in the design process. Findings show decentralized data practices, the role of expertise in decision-making, and the envisioned reactive use of ML, where we underscore the implications for advancing human-ML collaboration in designing future truck HMI systems.

The overall aim is to assess the superior of human- or design-centric source. This research compares the categories covered in a checklist by pain-points and needs identified individually using human- and design-centric sources. Data from 6 projects of a design course is used. It is found that there is no significant difference in the number of categories covered by pain-points and needs but the categories are not the same. This calls for integrating both sources in comparison to using only one source for designing which can potentially help to identify diverse and relevant outcomes.

This paper aims to identify the reasons for neglecting and the motivations for using management tools among three groups of students participating in a project that was carried out and followed over a period of one semester. For this purpose, a survey in the form of questionnaires and interviews was created. Particular emphasis was placed on the use of project management methods, tools and techniques and respondents were asked to rate the effectiveness of their usage. Additionally, the participants were asked to report limitations or distractions they had encountered. The results revealed five aspects that mainly influenced the students’ motivation in using management tools: the team atmosphere, determining responsibilities, performance transparency, expectations by the supervisors, feedback and performance evaluation. On that basis conclusions were drawn on what actions can be taken to motivate future students and designers.

Failure is part of the design process, and yet there is limited knowledge around how product design students perceive failure in their work. This pilot study aims to understand how a small sample size of undergraduate product design students conceptualize success and failure during specific stages of their design projects. This study uses a two-step data collection and analysis process. First, we collected responses from students on topics related to success and failure in a survey. Second, interviews were conducted with a subset of the survey respondents where these emergent topics were discussed and refined. In analyzing the responses, the research team used the Double Diamond Design process framework to organize what factors students deemed a success or failure within each stage. In summary, our preliminary findings indicate that determining success or failure is driven by the connection to the problem statement regardless of the stage; that student designers refer to failure as a spectrum but then in their examples showcase a binary view on the topic; and that examples of failure are often the opposite of success, reinforcing the notion of binary success vs. failure during student design projects.

The remote management of equipment is part of the functionalities granted by the design principles of Industry 4.0. However, some critical operations are managed by operators, machine setup and initialization serve as a significant illustration. Since the initialization is a repetitive task, industrial robots with a smart vision system can undertake these duties, enhancing the autonomy and flexibility of the manufacturing process. The smart vision system is considered essential for the implementation of several characteristics of Industry 4.0. This paper introduces a novel solution for controlling manufacturing machines using an embedded camera on the robot. This implementation requires the development of an interactive interface, designed in accordance with the supervision system known as Manufacturing Execution System. The framework is implemented inside a manufacturing cell, demonstrating a quick response time and an improvement between the cameras.

Product architecture decisions are made early in the product development process and have far-reaching effects. Unless anticipated through experience or intuition, many of these effects may not be apparent until much later in the development process, making changes to the architecture costly in time, effort, and resources. Many researchers through the years have studied various elements of product architecture and their effects. By aggregating observations on the effects of architecture strategies from a selection of the literature on the topic and storing them in a systematic data set, this information can be recalled in a matrix structure which allows for the identification, comparison and evaluation, and then selection of the most desirable product architecture strategies before expending resources along any development path. This paper introduces this matrix, referred to as the Product Architecture Strategy and Effect (PASE) Matrix, how to construct one, and a demonstration of its use.

Generative Design (GD) is a design approach that uses algorithms to generate designs. This paper investigates the role of optimisation algorithms in GD process. We study how Pareto Fronts – a classical optimization algorithm output – help designers to browse the variety associated with a design problem. Thanks to the “splitting condition” from design theory, we show that valuable Pareto Fronts for designers are those that allow the exploration of a variety of design parameters without modifying substantially the performance of the designed solution. We call “Splitting Pareto Front” the Pareto Fronts that display this property and investigate how to generate them. We compare, on an electrical battery design problem, two optimization algorithms – NSGA-II and MAP-Elites – based on the design parameters variety they generate. Our results show that MAP-Elites generates Pareto Fronts that are more splitting than those generated by NSGA-II. We then discuss this result in term of the design process: which algorithm is best suited for which design task? We conclude with the importance for future research on Generative Design Algorithms (GDA) to study jointly the functioning of GDA and their expected contribution to the design process.

Data-pushed projects are common in companies and consist in the design of a model in order to deliver a desirable output. The design of data science models appears at the intersection of optimisation and creativity logic, with in both cases the presence of anomalies to a various extent but no clear design process.

This paper therefore proposes to study the possible design processes in data-pushed projects, highlighting distinct knowledge exploration logics and the role of anomalies in each. This research introduces a theoretical framework to study data-pushed projects and is based on design theory. Three case studies complete this theoretical work to examine each of the processes and test our hypothesis.

As a result, this paper derives three design processes adapted to data-pushed projects and put forward for each of them: 1) the various knowledge leveraged and generated and 2) the specific role of anomalies.