45钢表面硬化层深度的高鲁棒性微磁定量预测方法

Robust and Quantitative Prediction of Surface Hardened Layer Depth of 45 Steel Using Micro-magnetic Testing Method

考虑多功能微磁检测系统对微磁参量的重复测试性能,研究利用系统对45钢表面硬化层深度进行高鲁棒性定量预测的方法。首先,利用测试数据的变异系数β统计方法,定量评价了系统对41项微磁参量的重复测试能力,结合指标β和微磁参量对硬化层深度的敏感性指标S,对微磁参量进行了筛选;其次,融合多项微磁参量建立了硬化层深度的前馈神经网络定量预测模型,提出了改善模型鲁棒性的建模策略及鲁棒性评价方法;最后,讨论了输入节点逐项剔除和有条件保留规则对模型鲁棒性的影响规律。与传统建模方法相比,利用规则剔除微磁参量项数为8时,模型的MAE均值和MAE值小于5%的模型数量P分别下降约68.8%和增加约150%,表明提出的建模策略可以有效改善仪器在45钢表面硬化层深度定量预测过程中的鲁棒性。

Robust and quantitative prediction of surface hardened layer depth of 45 steel using micro-magnetic testing method was studied considering the repeatability of multi-functional micro-magnetic instrument in measuring multiple magnetic features.First,the repeatability of instrument in measuring 41 magnetic features was evaluated based on the statistical method of coefficient of variation(β)of the test data.By combining the index of β and the sensitivity(S)of magnetic feature to the variation in hardened layer depth,the magnetic features were filtered.Second,models of feedforward neural network(FNN)were established fusing multiple micro-magnetic features.Modeling strategy for improving the robustness of the model and model robustness evaluation method were proposed.Finally,the effect of rules of input nodes elimination and reservation on the robustness of the model were discussed.Compared with the traditional modeling method,when eight magneric features were eliminated from the input nodes of FNN model obeying the proposed rule,the mean value of MAE and the number of models with MAE value less than 5%decreased by about 68.8%and increased by 150%,respectively.This indicates that the proposed modeling strategy can effectively improve the robustness of the instrument,which is used for quantitative prediction of the surface hardened layer depth of 45 steel.