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| Content Provider | Springer Nature Link |
|---|---|
| Author | Redondo, Raquel Seda, Javier Vera, Vicente Hernando, Beatriz Corchado, Emilio |
| Copyright Year | 2013 |
| Abstract | This multidisciplinary research presents a novel hybrid intelligent system to perform a multi-objective industrial parameter optimization process. The intelligent system is based on the application of evolutionary and neural computation in conjunction with identification systems, which makes it possible to optimize the implementation conditions in the manufacturing process of high precision parts, including finishing precision, while saving time, financial costs and/or energy. Empirical verification of the proposed hybrid intelligent system is performed in a real industrial domain, where a case study is defined and analyzed. The experiments are carried out based on real dental milling processes using a high precision machining centre with five axes, requiring high finishing precision of measures in micrometers with a large number of process factors to analyze. The results of the experiments which validate the performance of the proposed approach are presented in this study. |
| Starting Page | 31 |
| Ending Page | 44 |
| Page Count | 14 |
| File Format | |
| ISSN | 14337541 |
| Journal | Pattern Analysis & Applications |
| Volume Number | 18 |
| Issue Number | 1 |
| e-ISSN | 1433755X |
| Language | English |
| Publisher | Springer London |
| Publisher Date | 2013-07-06 |
| Publisher Place | London |
| Access Restriction | One Nation One Subscription (ONOS) |
| Subject Keyword | Hybrid intelligent system Dental milling process Optimization Unsupervised learning Identification systems Multi-objective optimization Pattern Recognition |
| Content Type | Text |
| Resource Type | Article |
| Subject | Artificial Intelligence Computer Vision and Pattern Recognition |
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