@Article{sdhm.2020.07595,
AUTHOR = {Suhas S. Aralikatti, K. N. Ravikumar, Hemantha Kumar, H. Shivananda Nayaka, V. Sugumaran},
TITLE = {Comparative Study on Tool Fault Diagnosis Methods Using Vibration Signals and Cutting Force Signals by Machine Learning Technique},
JOURNAL = {Structural Durability \& Health Monitoring},
VOLUME = {14},
YEAR = {2020},
NUMBER = {2},
PAGES = {127--145},
URL = {http://www.techscience.com/sdhm/v14n2/39423},
ISSN = {1930-2991},
ABSTRACT = {The state of cutting tool determines the quality of surface produced on
the machined parts. A faulty tool produces poor surface, inaccurate geometry and
non-economic production. Thus, it is necessary to monitor tool condition for a
machining process to have superior quality and economic production. In the present study, fault classification of single point cutting tool for hard turning has been
carried out by employing machine learning technique. Cutting force and vibration
signals were acquired to monitor tool condition during machining. A set of four
tooling conditions namely healthy, worn flank, broken insert and extended tool
overhang have been considered for the study. The machine learning technique
was applied to both vibration and cutting force signals. Discrete wavelet features
of the signals have been extracted using discrete wavelet transformation (DWT).
This transformation represents a large dataset into approximation coefficients
which contain the most useful information of the dataset. Significant features,
among features extracted, were selected using J48 decision tree technique. Classification of tool conditions was carried out using Naïve Bayes algorithm.
A 10 fold cross validation was incorporated to test the validity of classifier. A
comparison of performance of classifier was made between cutting force and
vibration signal to choose the best signal acquisition method in classifying tool
fault conditions using machine learning technique.},
DOI = {10.32604/sdhm.2020.07595}
}