Artificial Intelligence Tools for Better Use of Axiomatic Design
Name
Akay_2021_IOP_Conf._Ser.__Mater._Sci._Eng._1174_012005.pdf
Size
501.76 KB
Format
Adobe PDF
Checksum (MD5)
a35037606403ab3a8389d7cbc85004db
Author(s)
Akay, Haluk
Kim, Sang-Gook
Date Issued
August 1, 2021
Journal
IOP Conference Series: Materials Science and Engineering
Publisher
IOP Publishing
Citation
Haluk Akay and Sang-Gook Kim 2021 IOP Conf. Ser.: Mater. Sci. Eng. 1174 012005.
Version
Final published version
Abstract
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.
Subjects
General Medicine
MIT Department
Massachusetts Institute of Technology. Department of Mechanical Engineering
Terms of Use
Creative Commons Attribution
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1088/1757-899x/1174/1/012005
