[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117872-en":3,"doc-seo-117872-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},117872,34359740700684,"Finn","https://ap-avatar.wpscdn.com/avatar/1f400023980c374ae676?_k=1777273430885731487",8,"Research & Report","Design Descriptions in the Development of Machine Learning Based Design Tools","Applications of machine learning technologies are increasingly used across sectors, with reported benefits and risks driving strong interest from engineering communities. This work explores how machine learning can be integrated into engineering design workflows to improve product development performance. Experiments apply machine learning to two shape-based challenges, clustering visually similar shapes and grouping shapes likely manufactured by the same primary process. Early results are presented, alongside key issues for design descriptions required to realize machine learning’s full potential in engineering design.","INTERNATIONAL CONFERENCE ON ENGINEERING DESIGN, ICED23  \n24-28 JULY 2023 , BORDEAUX, FRANCE  \nDESIGN DESCRIPTIONS IN THE DEVELOPMENT OF MACHINE LEARNING BASED DESIGN TOOLS  \nMcKay, Alison;  \nHazlehurst, Thomas A;  \nde Pennington, Alan;  \nHogg, David C  \nUniversity of Leeds  \nABSTRACT  \nApplications of machine learning technologies are becoming ubiquitous in many sectors and their impacts, both positive and negative, are widely reported. As a result, there is substantial interest from the engineering community to integrate machine learning technologies into design workflows with a view to improving the performance of the product development process. In essence, machine learning technologies are thought to have the potential to underpin future generations of data-enabled engineering design system that will deliver radical improvements to product development and so organisational performance. In this paper we report learning from experiments where we applied machine learning to two shape-based design challenges: in a given collection of designed shapes, clustering (i) visually similar shapes and (ii) shapes that are likely to be manufactured using the same primary process. Both challenges were identified with our industry partners and are embodied in a design case study. We report early results and conclude with issues for design descriptions that need to be addressed if the full potential of machine learning is to be realised in engineering design.  \nKeywords: Big data, Artificial intelligence, Design informatics  \nContact:  \nMcKay, Alison University of Leeds United Kingdom [a.mckay@leeds.ac.uk](a.mckay@leeds.ac.uk)  \nCite this article: McKay, A., Hazlehurst, T. A, de Pennington, A., Hogg, D. C. (2023)‘Design Descriptions in the Development of Machine Learning Based Design Tools’, in Proceedings of the International Conference on Engineering Design (ICED23), Bordeaux, France, 24-28 July 2023. DOI:10.1017/pds.2023.123  \nICED23 1227  \n[https://doi.org/10.1017/pds.2023.123](https://doi.org/10.1017/pds.2023.123) Published online by Cambridge University Press  \n1 INTRODUCTION  \nDespite major advances in computer science, computational support for engineering design practice remains limited. For example, applications such as that outlined by Cavalcantea et al. (2019), who report an application of machine learning for the selection of suppliers in manufacturing supply chains, have the potential to improve the effectiveness and efficiency of procurement processes, and so reduce time to market and costs. However, the impact of such improvements on design quality (e.g., creating a design that better meets its design requirements or is easier (and so quicker and cheaper) to manufacture or support through life) is limited. For design definition, e.g., in computer aided design, and design analysis and optimisation applications (collectively referred to as CAD here), design information captures the results of design processes. In mechanical design, this information includes geometric models of individual parts and geometric constraints between them, often attributed with further information such as material specifications. However, a given design can be described in many ways, typically driven by the preferences and capabilities of the CAD system used to create it and its users. As a result, the structure of a given shape model is user defined and there is no guarantee that, in a given collection of shape models, the logic behind the structures of the models will be meaningful or consistent.  \nThis paper reports experiments using a machine learning application to address two shape-based challenges in engineering design practice: clustering of (i) similar part shapes in a given design and (ii) shapes that are likely to be manufactured using a given process. The focus of this paper is on the application of machine learning in design rather than the development of the machine learning system itself although, given the research was driven through","cbCaib9UhCxEr18W","https://ap.wps.com/l/cbCaib9UhCxEr18W","pdf",633606,1,10,"English","en",105,"# Abstract\n# Introduction\n# Literature Review\n# Approach and Machine Learning System\n# Results\n# Conclusions and Issues","[{\"question\":\"What is the document’s main goal in using machine learning for engineering design?\",\"answer\":\"It aims to integrate machine learning into engineering design workflows to improve the performance of the product development process, especially through better data-enabled design systems.\"},{\"question\":\"Which two shape-based challenges are addressed in the experiments?\",\"answer\":\"The experiments tackle clustering (i) visually similar shapes and (ii) shapes likely manufactured using the same primary process.\"},{\"question\":\"How do the authors connect this work to engineering design practice?\",\"answer\":\"They emphasize application of machine learning in design rather than building the machine learning system itself, using prototypes and an industry-driven design case study.\"}]","Design Descriptions in the Development of Machine Learning Based Design Tools | 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