Big Data Analytics for Improving Fidelity of Engineering Design Decisions 2018-01-1200
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.