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.

翻译：本文提出了一种白盒支持向量机（SVM）框架及其群体优化方法，用于通过实时主轴振动特性监测齿形铣刀。通过加速度的时域响应和统计特征，研究了由加工过程中刀具失效（即后刀面与刀尖磨损、月牙洼与缺口磨损、刃口崩裂）引起的振动异常时刻。采用以决策树为估计器的递归特征消除交叉验证（RFECV）方法进行特征选择。进一步，评估了标准SVM在刀具健康监测中的能力，并通过应用群体智能算法对其进行了优化。对五种元启发式算法（象群优化、帝王蝶优化、哈里斯鹰优化、黏菌算法和飞蛾搜索算法）的性能进行了比较分析。所提出的白盒方法考虑了全局与局部表示，为机器学习模型在刀具状态监测中的性能提供了深入见解。