Name
Title | ||
|---|---|---|
A knowledge-based system for numerical design of experiments processes in mechanical engineering. |
Abstract | ||
|---|---|---|
•An architecture for numerical design of experiment configuration is proposed.•A bayesian network with a “multi-net” strategy is proposed.•Models are trained from historical data and expert knowledge.•The performances of the proposed method are validated through a case study. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1016/j.eswa.2019.01.013 | Expert Systems with Applications |
Keywords | Field | DocType |
Knowledge based system,Numerical design of experiments,Bayesian network | Industrial engineering,Computer science,Knowledge-based systems,Bayesian network,Artificial intelligence,Inference engine,Product design,Machine learning,Design of experiments,Automotive industry | Journal |
Volume | ISSN | Citations |
122 | 0957-4174 | 0 |
PageRank | References | Authors |
0.34 | 15 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Gaëtan Blondet | 1 | 2 | 0.71 |
| Julien Le Duigou | 2 | 19 | 7.07 |
| Nassirn Boudaoud | 3 | 2 | 1.04 |