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首页> 外文期刊>International Journal of Computer Integrated Manufacturing >Modelling and prediction of tool wear using LS-SVM in milling operation
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Modelling and prediction of tool wear using LS-SVM in milling operation

机译：在铣削操作中使用LS-SVM进行刀具磨损的建模和预测

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摘要

This article focuses on the least squares support vector machine (LS-SVM), which can solve highly nonlinear and noisy black-box modelling problems, and tool wear model based on LS-SVM for ball-end milling cutter is established by considering the joint effect of machining conditions. In the established model, machining parameters and position parameter of ball-end cutter are considered as input and the output of the proposed model is tool wear of cutting edge position. The experimental measured tool wear is used to train the established model, and the interconnection relationship between input and output parameters is determined after training. The analysis and comparison of predicted performance are given by taking different tuning parameters and data regularisation. Some interesting analysis results are deduced from the established LS-SVM-based tool wear model. In order to further show the effectiveness of LS-SVM-based tool wear model, the verified comparison between LS-SVM-based and ANN-based model is given. Finally, the discussion of interactional effect of machining parameters on tool wear estimation is used to evaluate prediction performance of LS-SVM-based model. The verification shows that the LS-SVM-based tool wear model is suitable to predict tool wear at certain range of cutting conditions in milling operation.
机译：本文将重点放在最小二乘支持向量机（LS-SVM）上，它可以解决高度非线性和嘈杂的黑箱建模问题，并考虑了关节，建立了基于LS-SVM的球头铣刀刀具磨损模型。加工条件的影响。在建立的模型中，将球形端铣刀的加工参数和位置参数作为输入，所提出的模型的输出为切削刃位置的刀具磨损。实验测量的刀具磨损用于训练建立的模型，训练后确定输入和输出参数之间的相互关系。通过采用不同的调整参数和数据正则化，可以对预测性能进行分析和比较。从建立的基于LS-SVM的刀具磨损模型中得出一些有趣的分析结果。为了进一步展示基于LS-SVM的刀具磨损模型的有效性，给出了基于LS-SVM的模型与基于ANN的模型的经过验证的比较。最后，讨论了加工参数对刀具磨损估计的交互作用，以评估基于LS-SVM的模型的预测性能。验证表明，基于LS-SVM的刀具磨损模型适用于预测铣削操作中特定切削条件范围内的刀具磨损。

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