Publication Type:

Journal Article

Authors:

Dr. Radhika N

Source:

Journal of Engineering Science and Technology, Volume 10, Number 10, p.1310–1325 (2015)

URL:

http://jestec.taylors.edu.my/Vol%2010%20Issue%2010%20October%202015/Volume%20(10)%20Issue%20(10)%201310-1325.pdf

Abstract:

Aluminium/alumina/graphite hybrid metal matrix composites manufactured using stir casting technique was subjected to machining studies to predict tool condition during machining. Fresh tool as well as tools with specific amount of wear deliberately created prior to machining experiments was used. Vibration signals were acquired using an accelerometer for each tool condition. These signals were then processed to extract statistical and histogram features to predict the tool condition during machining. Two classifiers namely, Random Forest and Classification and Regression Tree (CART) were used to classify the tool condition. Results showed that histogram features with Random Forest classifier yielded maximum efficiency in predicting the tool condition. This machine learning approach enables the prediction of tool failure in advance, thereby minimizing the unexpected breakdown of tool and machine.

Cite this Research Publication

Dr. Radhika N, “Prediction of Tool Condition During Turning of aluminium/alumina/graphite Hybrid Metal Matrix Composites Using Machine Learning Approach”, Journal of Engineering Science and Technology, vol. 10, pp. 1310–1325, 2015.

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