Machine Learning - Special issue on learning with probabilistic representations
Evolving a Bayesian classifier for ECG-based age classification in medical applications
Applied Soft Computing
Improved use of continuous attributes in C4.5
Journal of Artificial Intelligence Research
Effect of SVM kernel functions on classification of vibration signals of a single point cutting tool
Expert Systems with Applications: An International Journal
Artificial Intelligence Based Green Technology Retrofit for Misfire Detection in Old Engines
International Journal of Green Computing
Hi-index | 12.07 |
Various methods of tool condition monitoring techniques are used to control the tool wear during machining in CNC machine tools. 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 used to probe into the structural information hidden in the signals acquired. This paper discusses machine tool condition monitoring of carbide tipped tool by using Naive Bayes and Bayes Net classifiers and compares the results of histogram features with the statistical features to establish better classification among the two. The vibration signals are acquired for various tool conditions like tool-good condition, tip-breakage, etc. The effort is to bring out the better feature-classifier combine. The results are discussed.