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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Malakooti, B. Zhou, Y. |
| Copyright Year | 1992 |
| Description | Author affiliation: Dept. of Syst. Eng., Case Western Reserve Univ., Cleveland, OH, USA (Malakooti, B.; Zhou, Y.) |
| Abstract | The authors report on a monitoring and supervising system for machining operations by using in-process regressions and adaptive feedforward artificial neural networks. The system uses different sensors. It is designed for tool life measurement and prediction, supervision of machining operations, and catastrophic tool failure monitoring. Adaptive feedforward artificial neural networks (AF-ANNs) are used for supervising, and in-process regressions for monitoring machining operations. The supervision of machining operations is studied within the framework of multiple criteria decision making. The decision maker (operator) considers several criteria, such as cutting quality, production rate, and tool life. To make the optimal decision with several criteria, the decision maker's preferences have to be elicited and assessed. The AF-ANN is used to determine the preferences. |
| Starting Page | 534 |
| Ending Page | 539 |
| File Size | 385291 |
| Page Count | 6 |
| File Format | |
| ISBN | 0780305590 |
| DOI | 10.1109/IJCNN.1992.226933 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 1992-06-07 |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Adaptive systems Neural networks Machining Condition monitoring Computerized monitoring Decision making Torque Artificial neural networks Production Sensor phenomena and characterization |
| Content Type | Text |
| Resource Type | Article |
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