基于t分布邻域嵌入与XGBoost的刀具多工况磨损评估

Multi-condition Wear Evaluation of Tool Based on T-SNE and XGBoost

刀具磨损的在线检测是未来自动化生产的必备功能,良好的评估模型可以有效地提高加工质量,降低经济损失。在现有研究基础上,提出了一种优化的刀具磨损评估方法,该方法综合使用了刀具切削过程中主轴的电流信号与振动信号,改善了单信号分析的不足。对采集到的信号提取时域、频域以及小波包特征,尽可能全面地提取了信号的有效信息。采用t分布邻域嵌入算法对特征进行降维,并使用K-means算法将多种不同的工况根据特征进行聚类,进一步提高了模型预测的准确率和泛化能力。最后使用XGBoost集成学习算法作为评估器,采用回归和分类两种方式对该模型进行评价。结果表明,样本不平衡问题对XGBoost算法的影响较小,和随机森林等传统集成学习算法相比,XGBoost在预测精度上有明显提升,在预测时间上减少一个数量级,是一种更为准确高效的刀具磨损检测算法,可以广泛地在工业上进行应用。

On-line inspection of tool wear is a necessary function for future automated production.A good evaluation model can effectively improve machining quality and reduce economic loss.Based on the existing research,an optimized tool wear evaluation method is presented.Current signal and vibration signal of spindle in cutting process are used synthetically,which can effectively improve the deficiency of single signal analysis.Time domain,frequency domain and wavelet packet features are extracted from the collected signals,and the signal feature information is extracted as comprehensively as possible.T-SNE is used to reduce the dimension of features,and K-means is used to cluster various working conditions according to the similarity of features,which further improves the accuracy and generalization ability of model prediction.Finally,the integrated learning algorithm XGBoost is used as the estimator,and the model is evaluated by regression and classification.The results show that the problem of sample imbalance has less impact on the XGBoost algorithm.Compared with the traditional integrated learning algorithms such as random forest,XGBoost improves the prediction accuracy and reduces the prediction time by an order of magnitude.It is a more accurate and efficient tool detection algorithm and can be widely used in industry.