  In this paper, a white-Box support vector machine (SVM) framework and its
swarm-based optimization is presented for supervision of toothed milling cutter
through characterization of real-time spindle vibrations. The anomalous moments
of vibration evolved due to in-process tool failures (i.e., flank and nose
wear, crater and notch wear, edge fracture) have been investigated through
time-domain response of acceleration and statistical features. The Recursive
Feature Elimination with Cross-Validation (RFECV) with decision trees as the
estimator has been implemented for feature selection. Further, the competence
of standard SVM has been examined for tool health monitoring followed by its
optimization through application of swarm based algorithms. The comparative
analysis of performance of five meta-heuristic algorithms (Elephant Herding
Optimization, Monarch Butterfly Optimization, Harris Hawks Optimization, Slime
Mould Algorithm, and Moth Search Algorithm) has been carried out. The white-box
approach has been presented considering global and local representation that
provides insight into the performance of machine learning models in tool
condition monitoring.

Machine Learning;Neural and Evolutionary Computing