Extracting functional requirements from design documentation using machine learning
Good design practice and digital tools have enabled industry to produce valuable products. Early-stage design research involves rigorous background study of large volumes of design documentation which designers must analyze manually, to extract functional requirements which are abstracted and priori...
| Main Authors: | , |
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| Other Authors: | |
| Format: | Article |
| Language: | English |
| Published: |
Elsevier BV
2022
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| Online Access: | https://hdl.handle.net/1721.1/138846.2 |
| _version_ | 1811082470183927808 |
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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 | Good design practice and digital tools have enabled industry to produce valuable products. Early-stage design research involves rigorous background study of large volumes of design documentation which designers must analyze manually, to extract functional requirements which are abstracted and prioritized to guide a design. Recent advances in Machine Learning, specifically Natural Language Processing (NLP), can be applied to enhance the time-consuming and difficult practice of the human designer by performing tasks such as extracting functional requirements from long-form written documentation. This work demonstrates how extractive question-answering by neural networks can be applied to design as a tool for automating this initial step in the design process. We applied the language model BERT, fine-tuned on question-answering, to identify functional requirements in written documentation. Limitations due to wording sensitivity are discussed and an outline for training a design-specific model is discussed with a MEMS product design case. This work presents how this application of AI to design could enhance the work of human designers using the power of computing, which will open the door for learning from big data of past product designs by allowing machines to “read” them. |
| first_indexed | 2024-09-23T12:03:55Z |
| format | Article |
| id | mit-1721.1/138846.2 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T12:03:55Z |
| publishDate | 2022 |
| publisher | Elsevier BV |
| record_format | dspace |
| spelling | mit-1721.1/138846.22024-03-22T20:41:00Z Extracting functional requirements from design documentation using machine learning Akay, Haluk Kim, Sang-Gook Massachusetts Institute of Technology. Department of Mechanical Engineering Good design practice and digital tools have enabled industry to produce valuable products. Early-stage design research involves rigorous background study of large volumes of design documentation which designers must analyze manually, to extract functional requirements which are abstracted and prioritized to guide a design. Recent advances in Machine Learning, specifically Natural Language Processing (NLP), can be applied to enhance the time-consuming and difficult practice of the human designer by performing tasks such as extracting functional requirements from long-form written documentation. This work demonstrates how extractive question-answering by neural networks can be applied to design as a tool for automating this initial step in the design process. We applied the language model BERT, fine-tuned on question-answering, to identify functional requirements in written documentation. Limitations due to wording sensitivity are discussed and an outline for training a design-specific model is discussed with a MEMS product design case. This work presents how this application of AI to design could enhance the work of human designers using the power of computing, which will open the door for learning from big data of past product designs by allowing machines to “read” them. 2022-02-01T20:06:46Z 2022-01-07T19:07:26Z 2022-02-01T20:06:46Z 2021-06 2022-01-07T19:01:08Z Article http://purl.org/eprint/type/ConferencePaper 2212-8271 https://hdl.handle.net/1721.1/138846.2 Akay, Haluk and Kim, Sang-Gook. 2021. "Extracting functional requirements from design documentation using machine learning." Procedia CIRP, 100. en http://dx.doi.org/10.1016/J.PROCIR.2021.05.005 Procedia CIRP Creative Commons Attribution-NonCommercial-NoDerivs License http://creativecommons.org/licenses/by-nc-nd/4.0/ application/octet-stream Elsevier BV Elsevier |
| spellingShingle | Akay, Haluk Kim, Sang-Gook Extracting functional requirements from design documentation using machine learning |
| title | Extracting functional requirements from design documentation using machine learning |
| title_full | Extracting functional requirements from design documentation using machine learning |
| title_fullStr | Extracting functional requirements from design documentation using machine learning |
| title_full_unstemmed | Extracting functional requirements from design documentation using machine learning |
| title_short | Extracting functional requirements from design documentation using machine learning |
| title_sort | extracting functional requirements from design documentation using machine learning |
| url | https://hdl.handle.net/1721.1/138846.2 |
| work_keys_str_mv | AT akayhaluk extractingfunctionalrequirementsfromdesigndocumentationusingmachinelearning AT kimsanggook extractingfunctionalrequirementsfromdesigndocumentationusingmachinelearning |