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Grinding Wheel Condition Monitoring With Boosted Classifiers, Fengming Tang

LSU Master's Theses

In this thesis, two data sets collected in grinding process under different cutting and wheel conditions were studied. One is the cutting forces in three directions, i.e. X, Y and Z, collected under two different cutting conditions. The other one is the acoustic emission (AE) signals collected under different wheel conditions(sharp and dull). For the goal of grinding wheel condition monitoring, the regression model with autocorrelated errors was proved to be effective and was used to extract features from signals in this study. The coefficients of the models served as the features used in the classification step that employed boosting …