Machine Learning
The Application of Nonstandard Support Vector Machine in Tool Condition Monitoring System
DELTA '04 Proceedings of the Second IEEE International Workshop on Electronic Design, Test and Applications
Neural Computation
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Improved use of continuous attributes in C4.5
Journal of Artificial Intelligence Research
Expert Systems with Applications: An International Journal
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IEEE Transactions on Neural Networks
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Expert Systems with Applications: An International Journal
Hi-index | 12.05 |
The studies on tool condition monitoring along with digital signal processing can be used to prevent damages on cutting tools and workpieces when the tool conditions become faulty. These studies have become more relevant in today's context where the order realization dates are crunched and deadlines are to be met in order to catch up with the competition. Based on a continuous acquisition of signals with sensor systems it is possible to classify certain wear parameters by the extraction of features. Data mining approach is extensively used to probe into structural health of the tool and the process. This paper discusses condition monitoring of carbide tipped tool using Support Vector Machine and compares the classification efficiency between C-SVC and @n-SVC. It further analyses the results with other classifiers like Decision Tree and Naive Bayes and Bayes Net. The vibration signals are acquired for various tool conditions like tool-good condition, tip-breakage, etc. The effort is to bring out the better features-classifier combination.