This content is not included in
your SAE MOBILUS subscription, or you are not logged in.
Big Data Analytics for Improving Fidelity of Engineering Design Decisions
Technical Paper
2018-01-1200
ISSN: 0148-7191, e-ISSN: 2688-3627
This content contains downloadable datasets
Annotation ability available
Sector:
Language:
English
Abstract
This paper presents a high-level framework (vision) for utilizing big data analytics to harvest repositories of known good designs for the purpose of aiding mechanical product designs. The paper outlines a novel approach for applying artificial intelligence (AI) to the training of a mechanical design system model, assimilates the definition of meta-data for design containers (binders) to that of labels for books in a library, and represents customers, requirements, components and assemblies in the form of database objects with hierarchical structure. Design information can be harvested, for the purpose of improving design decision fidelity for new designs, by providing such database representation of the design content. Further, a retrieval model, that operates on the archived design containers, and yields results that are likely to satisfy user queries, is presented. This model, which is based on latent semantic analysis (LSA), predicts the degree of relevance between accessible design information and a query, and presents the most relevant previous design information to the user. A simple example, one involving idea generation for conceptual design, is presented, in order to provide insight into the significant utility that may be derived from the proposed AI design framework.
Recommended Content
Authors
Citation
Steingrimsson, B., Yi, S., Jones, R., Kisialiou, M. et al., "Big Data Analytics for Improving Fidelity of Engineering Design Decisions," SAE Technical Paper 2018-01-1200, 2018, https://doi.org/10.4271/2018-01-1200.Data Sets - Support Documents
Title | Description | Download |
---|---|---|
Unnamed Dataset 1 | ||
Unnamed Dataset 2 | ||
Unnamed Dataset 3 | ||
Unnamed Dataset 4 |
Also In
References
- Brown , D.C. Artificial Intelligence for Design Process Improvement Clarkson J. , Eckert C. Design Process Improvement London Springer 2005 158 173
- Yi , K. A Study of Evaluating the Value of Social Tags as Indexing Terms Chu S. , Ritter W. , and Hawamdeh S. Managing Knowledge for Global and Collaborative Innovations Hackensack, NJ World Scientific Publishing 2009 221 232
- Steingrimsson , B. , Jones , R. , Etesami , F. , and Yi , S. Ecosystem for Engineering Design - A Comparative Analysis International Journal on Engineering Education 33 5 1499 1512 2017
- B. Steingrimsson and S. Yi 2017
- Duda , R.O. , Hart , P.E. , and Stork , D.G. Pattern Classification Second Wiley 2001
- Salton , G. A Vector Space Model for Automatic Indexing Communications of the ACM 18 11 613 620 1975
- Deerwester , S. , Dumais , S.T. , Furnas , G.W. , Landauer , T.K. , and Harshman , R. Indexing by latent semantic analysis Journal of the American Society for Information Science 41 6 391 1990
- Dumais , S.T. Latent Semantic Analysis Annual Review of Information Science and Technology 38 1 188 230 2004
- T. Landauer , D. McNamara , S. Dennis and W. Kintsch Handbook of Latent Semantic Analysis 2007
- Widrow , B. and Hoff , M.E. Adaptive Switching Circuits IRE WESCON Convention Record 4 New York 1960 96 104
- Spark API 2017
- Big Data Zone 2017
- Hadoop 2017 https://hadoop.apache.org/docs/r1.0.4/webhdfs.html
- Wikipedia 2017 https://en.wikipedia.org/wiki/Kerberos_(protocol)