基于人工神经网络的多次搭接激光冲击作用下材料残余应力与显微硬度预测方法

Prediction about residual stress and microhardness of material subjected to multiple overlap laser shock processing using artificial neural network

本文通过人工神经网络方法实现多次搭接激光冲击作用下材料残余应力与显微硬度的预测。以镍基粉末冶金高温合金FGH95为实验材料,基于正交实验设计的思路,确定了多次搭接激光冲击强化的实验参数。利用X射线应力测量仪和显微硬度计分别测量了实验试件激光冲击强化处理前后的残余应力和显微硬度分布,并对残余应力和显微硬度变化规律进行了简要分析。构建了4层网络结构(4-N-(N-1)-2)的人工神经网络预测模型,其中输入为激光能量、光斑搭接率、冲击次数和深度,输出为残余应力和显微硬度。对不同网络结构的预测性能进行了比较和分析。在最优的模型下(网络结构为4-7-6-2),预测值与实验值十分吻合,预测效果极佳。此外,基于人工神经网络的方法,还研究了激光冲击强化工艺参数对材料响应的影响。研究表明,在实验数据较为缺乏的情况下,人工神经网络是预测多次搭接激光冲击作用下材料残余应力和显微硬度的有效方法。

In this work,the nickel-based powder metallurgy superalloy FGH95 was selected as experimental material,and the experimental parameters in multiple overlap laser shock processing(LSP)treatment were selected based on orthogonal experimental design.The experimental data of residual stress and microhardness were measured in the same depth.The residual stress and microhardness laws were investigated and analyzed.Artificial neural network(ANN)with four layers(4-N-(N-1)-2)was applied to predict the residual stress and microhardness of FGH95 subjected to multiple overlap LSP.The experimental data were divided as training-testing sets in pairs.Laser energy,overlap rate,shocked times and depth were set as inputs,while residual stress and microhardness were set as outputs.The prediction performances with different network configuration of developed ANN models were compared and analyzed.The developed ANN model with network configuration of 4-7-6-2 showed the best predict performance.The predicted values showed a good agreement with the experimental values.In addition,the correlation coefficients among all the parameters and the effect of LSP parameters on materials response were studied.It can be concluded that ANN is a useful method to predict residual stress and microhardness of material subjected to LSP when with limited experimental data.