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| Content Provider | The American Society of Mechanical Engineers (ASME) Digital Collection |
|---|---|
| Author | Matt, R. Bohm Robert, B. Stone |
| Copyright Year | 2009 |
| Abstract | Over the last few decades design researchers have put forward theories and proposed methodologies that increase the chance that a design team will reliably arrive at the optimal solution to a given design problem. Studies, however, bear out that theories and methodologies alone will not guarantee an optimal or even good design solution. Instead, a breadth of knowledge across multiple engineering domains and the time and tools to thoroughly evaluate the design space are as important as any prescriptive design method. This work presents one of the underlying engineering technologies needed to leverage artificial intelligence approaches to thoroughly search the design space and synthesize concept solutions. Artificial intelligence methods are employed to generate a natural language to formal component terms thesaurus as part of a novel form-initiated concept generation approach. With this fundamental natural language interpretation algorithm, designers may now suggest an initial solution to a problem, expressed in everyday terms, and then rely on a machine to abstract the underlying functionality and conduct a thorough search of the solution space. |
| Sponsorship | Design Engineering Division and Computers in Engineering Division |
| Starting Page | 1341 |
| Ending Page | 1350 |
| Page Count | 10 |
| File Format | |
| ISBN | 9780791848999 |
| DOI | 10.1115/DETC2009-86581 |
| e-ISBN | 9780791838563 |
| Volume Number | Volume 2: 29th Computers and Information in Engineering Conference, Parts A and B |
| Conference Proceedings | ASME 2009 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference |
| Language | English |
| Publisher Date | 2009-08-30 |
| Publisher Place | San Diego, California, USA |
| Access Restriction | Subscribed |
| Subject Keyword | Design Teams Algorithms Design methodology Artificial intelligence Machinery |
| Content Type | Text |
| Resource Type | Article |
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