This paper presents a systematic approach to multidisciplinary design automation in electric motor engineering, focusing on component design. Existing work in this field is often limited to a single level and lacks portability and reusability. The approach aims to enable simultaneous component and system design, with comprehensive models capturing specifications and architecture. Feasibility is demonstrated through the automated design of additive manufactured hairpin windings.

This paper presents a novel method for automatic contradiction detection in requirements engineering using a hybrid approach combining formal logic with Large Language Models (LLMs), specifically GPT-3. Our three-phase process detects contradictions by identifying conditionals and pseudo-grammatical elements, and employing LLMs for nuanced contradiction detection. Tested extensively, including on a real-world electric bus project, our method achieved 99% accuracy and 60% recall. This approach significantly reduces manual effort, enhances quality, and is scalable for future advancements.

This study envisions a unified paradigm for design for automated disassembly. The goal is to integrate disassembly insights related to precious material recovery with the design phase for sustainable lifecycle management.Targeting plastic products with embedded electronics, the collaboration between design and robotic engineers aims to program a robot for disassembly for the LEGO® motor (45603) as demonstration, emphasizing a disassembly map as a vital tool. By considering the limitations and strengths of robots, this research pioneers a design for disassembly framework.

Automation and artificial intelligence (AI) are increasingly seen as appealing tools to perform design tasks traditionally accomplished by human designers. In today's digital economy, industries aim to adopt these tools to improve the efficiency of their complex design processes. But how does one decide what parts of their existing design process should be automated and which automation/AI tool to implement? With these questions in mind, we present a case study highlighting a company's decision-making process in converting its existing designer-dependent design process to one supported by automation. In this case study, we observed the company's decisions in selecting and rejecting certain automation and AI methods before finalizing a heuristics-based automation method that proved highly efficient compared to the company's traditional human-driven design program. In addition, we present three key discussion points observed in this case study: (1) the importance of implementing the designer's heuristics in the automation framework, (2) the importance of a uniform and modular design automation framework, and (3) the challenges of implementing AI methods.

The use of local natural and recycled feedstock is promising for sustainable construction. However, unlike versatile engineered bricks, natural and recycled feedstock involves design challenges due to their stochastic, sequential, and heterogeneous nature. For example, the practical use of stone masonry is limited, as it still relies on human experts with holistic domain knowledge to determine the sequential organization of natural stones with different sizes/shapes. Reinforcement learning (RL) is expected to address such design challenges, as it allows artificial intelligence (AI) agents to autonomously learn design policy, that is, identifying the best design decision at each time step. As a proof-of-concept RL framework for design automation involving heterogeneous feedstock, a stone masonry design framework is presented. The proposed framework is founded upon a virtual design environment, MasonTris, inspired by the analogy between stone masonry and Tetris. MasonTris provides a Tetris-like virtual environment combined with a finite element analysis (FEA), where AI agents learn effective design policies without human intervention. Also, a new data collection policy, almost-greedy policy, is designed to address the sparsity of feasible designs for faster/stable learning. As computation bottleneck occurs when parallel agents evaluate designs with different complexities, a modification of the RL framework is proposed that FEA is held until training data are retrieved for training. The feasibility and adaptability of the proposed framework are demonstrated by continuously improving stone masonry design policy in simplified design problems. The framework can be generalizable to different natural and recycled feedstock by incorporating more realistic assumptions, opening opportunities in design automation for sustainability.

This paper proposes that safety footwear manufacture shifts towards a sustainable Product-Service System (PSS). The PSS consists of seven stages from identification of requirements through to disposal or replacement. Through this, designers and manufacturers can realise safety footwear that is customised to end user sizing, function, industry and aesthetic requirements. For end users, this PSS leads to the creation of more comfortable and practicable footwear, addressing the underlying level of acceptance of uncomfortable safety footwear, reducing waste and providing sex specific offerings.

Digital Engineering is an emerging trend and aims to support engineering design by integrating computational technologies like design automation, data science, digital twins, and product lifecycle management. To enable alignment of industrial practice with state of the art, an industrial survey is conducted to capture the status and identify obstacles that hinder implementation in the industry. The results show companies struggle with missing know-how and available experts. Future work should elaborate on methods that facilitate the integration of Digital Engineering in design practice.

The design rationale describes the justification of design decision or selection. To avoid unnecessary design iterations, a capturing and documentation of this rationale is highly desirable. In digital engineering processes it is of imminent importance not only to document the evaluation processes behind this rationale but to make them repeatable and digitally executable. This allows to design a variety of product variants within an engineering framework. This paper explains an approach based on graph-based design languages and presents it based on a section of an automotive assembly system.

This paper presents a framework to develop the automated design of fixtures using the combination of design automation (DA), multidisciplinary optimization and robotic simulation. MDO necessitates the use of concurrent and parametric designs which are created by DA and knowledge-based engineering tools. This approach is designed to decrease the time and cost of the fixture design process by increasing the degree of automation. AutoFix provides methods and tools for automatically optimizing resource-intensive fixture design utilizing digital tools from different disciplines.

This paper presents five industrial cases where design automation (DA) systems supported by design optimization has been developed, and aims to summarize the lesson learned and identify needs for future development of such projects. By mapping the challenges during development and deployment of the systems, common issues were found in technical areas such as model integration and organizational areas such as knowledge transfer. The latter can be seen as a two-layered design paradox; one for the product that the DA system is developed for, and one for the development of the DA system.

Data-enabled design (DED) is a promising new methodology for designing with users from within their own context in an iterative and hands-on fashion. However, the agile and flexible qualities of the methodology do not directly translate to every context. In this article, we reflect on the design process of an intelligent ecosystem, called ORBIT, and a proposed evaluative study planned with it. This was part of a DED project in collaboration with a medical hospital to study the post-operative behavior in the (remote) context of bariatric patients. The design and preparation of this project and the process towards an eventual study rejection from the medical ethical committee (METC) provide rich insights into (1) what it means to conduct DED research in a clinical context, and (2) where the boundaries of the method might lie in this specific application area. We highlight insights from carefully designing the substantial infrastructure for the study, and how different aspects of DED translated less easily to the clinical context. We analyze the proposed study setup through the lenses of several modifications we made to DED and further reflect on how to expand and scale up the methodology and adapt the process for the clinical context.

Mass Personalisation (MP) is becoming more significant to answer diversifying customer needs, as a result of the advancements in digital manufacturing. In contrary to the modular design in mass customisation, Design for MP (DfMP) proposes more profound changes in product and active user involvement in the design process, while maintaining mass efficiency. Traditional product development methodologies fall short in guiding MP, as it has the distinct differences with product variability and the customer involvement with specific needs. In this study, a dedicated design methodology for MP is presented, focussing on these key dimensions. The proposed methodology guides the designer through the development process of a user modifiable design and demonstrates how to facilitate the user involvement in reaching a personalised design. It proposes a flexible and adaptable seed design architecture, and an interactive customer co-creation process. The development of a seed design, construction of its design space, and management of the solution space with a design solution algorithm are elaborated. The application of the methodology was illustrated on the personalisation of knitted footwear, and 3D printed saxophone mouthpiece. The examples show the potential of the methodology to deal with coupled MP cases. A systematic approach to DfMP will allow expanding MP to more products, and acts as a foundation for the customer co-creation oriented design in the context of this emerging paradigm.

One challenge with design automation is system transparency with adjustable granularity because of the many different forms of representation from multiple disciplines. Previous research has focused on visualization through the generation of graphs, packaging into electronic books, and model highlighting. The research presented in this paper focuses instead on a visual programming approach, commonly applied in the building industry, where design assets and external references are wrapped into visual components and managed on a canvas with information input/output relations displayed. This entails additional documentation efforts, but the visualization is arguably more useful as groups and levels of granularity are adjusted by the engineers themselves as a part of the development work. To explore visual programming and its potential benefits as a way of enabling transparency with adjustable granularity of DA systems within mechanical manufacturing industry, an existing textual design automation system was transformed into a visual one using Grasshopper® (a visual programming environment) and discussed with respect to DA system transparency, feature-based CAD, and DA system development.

The digitalization era has brought about unprecedented challenges for the manufacturing industries, pushing them to deliver solutions that encompass both product and service-related dimensions, known as Product-service Systems. This paper presents a number of lessons learned in the process of integrating the analysis of operational data as decision support in engineering design based on the empirical studies from two Swedish manufacturing companies operating in the construction machinery sector. The paper highlights the need to consider a five-dimensional perspective when collecting and analyzing data, encompassing data from the product, the service, the environment, the infrastructure, and the humans involved. Finally, a conceptual framework for data-driven design automation of Product-service Systems is proposed by leveraging the use of these data, introducing the concept of a Product-Service System Configurator as an enabler of design automation. The implementation of the proposed framework on multiple case studies in different industrial contexts shall be considered as the next step of the research.

The development of autonomous greenhouses has caught the interest of many researchers and industrial considering their potential of offering an optimal environment for the growth of high-quality crops with minimum resources. Since an autonomous greenhouse is a mechatronic system, the consideration of its subsystem (e.g. heating systems) and component (e.g. actuators and sensors) interactions early in the design phase can ease the product development process. Indeed, this consideration could shorten the design process, reduce the number of redesign loops, and improve the performance of the overall mechatronic system. In the case of a greenhouse, it would lead to a higher quality of the crops and a better management of resources. In this work, the layout design of a general autonomous greenhouse is translated into an optimization problem statement while considering product-related dependencies. Then, a genetic algorithm is used to carry out the optimization of the layout design. The methodology is applied to the design of a fully autonomous greenhouse (45 cm × 30 cm × 30 cm) for the growth of plants in space. Although some objectives are conflictual, the developed algorithm proposes a compromise to obtain a near-optimal feasible layout design. The algorithm was also able to optimize the volume of components (occupied space) while considering the energy consumption and the overall mass. Their respective summed values are 2844.32 cm3, which represents 7% of the total volume, 5.86 W, and 655.8 g.

Human designers often work in a visual design space, projecting step-by-step design progression through evolving mental images. The strategic evolution of that design leverages heuristics based on experience and domain knowledge. The methodology presented in this paper brings together the visual nature of design problem solving and design heuristics in a deep learning computational agent framework that emulates and enables human-mirrored design. When applied to a truss design task, results demonstrate superior results to those of human designers who provided the initial data.

To enhance the success of innovations, various methods for foresight have been developed. Automatization yields the potential of shifting effort away from the process to the actual in-depth analysis of resulting scenarios in scenario-technique. Within this paper, an approach based on a user-specific classification of input factors (consistency values) is presented. Generic consistency patterns used for a semi-automatized consistency assessment based on artificial neural networks are identified using a case study approach. Hereby, the effort for scenario-technique can be reduced significantly.

The digitalisation of the industry offers new opportunities to discuss design activities and support tools. Advancement in AI allows thinking about new Designer-AI tools interaction in the design process. The paper aims to initiate a characterisation of tools issued from researches in the application of AI in Design to rethink the division of work between Designer-AI tools. The paper is based on the literature on the concept of Levels of Automation in cognitive engineering, manufacturing and robotics, and proposes a grid of characterisation of the Level of Automation for the design process.

With industries striving towards increased customisation of complex products through engineer-to-order, methods are continuously sought to rationalise the product development process. To this end, a framework is proposed using CAD configurators, utilising design automation and knowledge-based engineering to integrate sales and design processes in product development. The application of this framework to the design of spiral staircases is described and analysed, with results showing decreased lead-times and a decreased risk for design errors.

Knowledge-based engineering (KBE) systems allow an easy adaption of designed artefacts to new functional or design requirements and automating routine design tasks. In the following article the author wants to focus on the three main concepts of linking CAD and KBE and answer the research questions (1) in which way is integration, embedding and coupling of KBE to a standard CAD system like Autodesk Inventor available and (2) how can the single approaches be compared in terms of modelling effort, user competences and system performance.