Artificial Intelligence Tools for Better Use of Axiomatic Design
Axiomatic Design (AD) provides a powerful thinking framework for solving complex engineering problems through the concept of design domains and diligent mapping and decomposition between functional and physical domains. Despite this utility, AD is yet to be implemented for widespread use by design p...
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| Format: | Article |
| Language: | English |
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IOP Publishing
2024
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| Online Access: | https://hdl.handle.net/1721.1/153634 |
| _version_ | 1826196006703726592 |
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| author | Akay, Haluk Kim, Sang-Gook |
| author2 | Massachusetts Institute of Technology. Department of Mechanical Engineering |
| author_facet | Massachusetts Institute of Technology. Department of Mechanical Engineering Akay, Haluk Kim, Sang-Gook |
| author_sort | Akay, Haluk |
| collection | MIT |
| description | Axiomatic Design (AD) provides a powerful thinking framework for solving complex engineering problems through the concept of design domains and diligent mapping and decomposition between functional and physical domains. Despite this utility, AD is yet to be implemented for widespread use by design practitioners solving real world problems in industry and exists primarily in the realm of academia. This is due, in part, to a high level of design expertise and familiarity with its methodology required to apply the AD approach effectively. It is difficult to correctly identify, extract, and abstract top-level functional requirements (FRs) based on early-stage design research. Furthermore, guiding early-stage design by striving to maintain functional independence, the first Axiom, is difficult at a systems level without explicit methods of quantifying the relationship between high-level FRs and design parameters (DPs). To address these challenges, Artificial Intelligence (AI) methods, specifically in deep learning (DL) assisted Natural Language Processing (NLP), have been applied to represent design knowledge for machines to understand, and, following AD principles, support the practice of human designers. NLP-based question-answering is demonstrated to automate early-stage identification of FRs and to assist design decomposition by recursively mapping and traversing down along the FR-DP hierarchical structure. Functional coupling analysis could then be conducted with vectorized FRs and DPs from NLP-based language embeddings. This paper presents a framework for how AI can be applied to design based on the principles of AD, which will enable a virtual design assistant system based on both human and machine intelligence. |
| first_indexed | 2024-09-23T10:19:21Z |
| format | Article |
| id | mit-1721.1/153634 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T10:19:21Z |
| publishDate | 2024 |
| publisher | IOP Publishing |
| record_format | dspace |
| spelling | mit-1721.1/1536342024-09-19T15:23:14Z Artificial Intelligence Tools for Better Use of Axiomatic Design Akay, Haluk Kim, Sang-Gook Massachusetts Institute of Technology. Department of Mechanical Engineering General Medicine Axiomatic Design (AD) provides a powerful thinking framework for solving complex engineering problems through the concept of design domains and diligent mapping and decomposition between functional and physical domains. Despite this utility, AD is yet to be implemented for widespread use by design practitioners solving real world problems in industry and exists primarily in the realm of academia. This is due, in part, to a high level of design expertise and familiarity with its methodology required to apply the AD approach effectively. It is difficult to correctly identify, extract, and abstract top-level functional requirements (FRs) based on early-stage design research. Furthermore, guiding early-stage design by striving to maintain functional independence, the first Axiom, is difficult at a systems level without explicit methods of quantifying the relationship between high-level FRs and design parameters (DPs). To address these challenges, Artificial Intelligence (AI) methods, specifically in deep learning (DL) assisted Natural Language Processing (NLP), have been applied to represent design knowledge for machines to understand, and, following AD principles, support the practice of human designers. NLP-based question-answering is demonstrated to automate early-stage identification of FRs and to assist design decomposition by recursively mapping and traversing down along the FR-DP hierarchical structure. Functional coupling analysis could then be conducted with vectorized FRs and DPs from NLP-based language embeddings. This paper presents a framework for how AI can be applied to design based on the principles of AD, which will enable a virtual design assistant system based on both human and machine intelligence. 2024-03-01T20:03:47Z 2024-03-01T20:03:47Z 2021-08-01 2024-03-01T19:57:28Z Article http://purl.org/eprint/type/ConferencePaper 1757-8981 1757-899X https://hdl.handle.net/1721.1/153634 Haluk Akay and Sang-Gook Kim 2021 IOP Conf. Ser.: Mater. Sci. Eng. 1174 012005. en 10.1088/1757-899x/1174/1/012005 IOP Conference Series: Materials Science and Engineering Creative Commons Attribution https://creativecommons.org/licenses/by/3.0/ application/pdf IOP Publishing IOP Science |
| spellingShingle | General Medicine Akay, Haluk Kim, Sang-Gook Artificial Intelligence Tools for Better Use of Axiomatic Design |
| title | Artificial Intelligence Tools for Better Use of Axiomatic Design |
| title_full | Artificial Intelligence Tools for Better Use of Axiomatic Design |
| title_fullStr | Artificial Intelligence Tools for Better Use of Axiomatic Design |
| title_full_unstemmed | Artificial Intelligence Tools for Better Use of Axiomatic Design |
| title_short | Artificial Intelligence Tools for Better Use of Axiomatic Design |
| title_sort | artificial intelligence tools for better use of axiomatic design |
| topic | General Medicine |
| url | https://hdl.handle.net/1721.1/153634 |
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