基于BP神经网络的抛浆冲击后金属基体表面粗糙度控制研究  

Study on surface roughness control of metal matrix after slurry blasting based on BP neural network

作　　者：齐浩 周存龙[1,2] 柴泽琳 郭瑞[1,2] 李春阳[1,2] QI Hao;ZHOU Cun-long;CHAI Ze-lin;GUO Rui;LI Chun-yang(School of Mechanical Engineering,Taiyuan University of Science and Technology,Taiyuan 030024,China;Shanxi Provincial Key Laboratory of Metallurgical Equipment Design and Technology,Taiyuan University of Science and Technology,Taiyuan 030024,China)

机构地区：[1]太原科技大学机械工程学院,山西太原030024 [2]太原科技大学山西省冶金设备设计理论与技术重点实验室,山西太原030024

出　　处：《塑性工程学报》2023年第9期188-194,共7页Journal of Plasticity Engineering

基　　金：中央引导地方科技发展资金资助项目(Z135050009017);山西省科技重大专项(20181102015);山西省基础研究计划(202203021222192);山西省高等学校科技创新项目(2022L308)。

摘　　要：针对抛浆冲击后基体表面粗糙度缺乏精确控制模型的问题,基于BP神经网络,构建了抛浆工艺参数对基体表面粗糙度的BP神经网络预测模型,并以304不锈钢为研究对象进行了抛浆冲击试验,通过对样本数据进行方差分析检验,分析了各工艺参数对表面粗糙度的影响程度,由此确定了网络模型的输入和输出参数,以进行网络模型的训练和验证。结果表明,网络模型抛射速度的预测值和总样本的最大相对误差为4.80%;通过网格模型结果给定抛射速度试验得到的粗糙度值与目标值最大相对误差为2.40%。说明该神经网络模型能够指导工艺参数设定,实现冲击后钢板表面粗糙度的精准控制。Aiming at the problem of the lacking of accurate control model for substrate surface roughness after slurry blasting,the BP neural network prediction model of the slurry blasting process parameters on substrate surface roughness was constructed based on BP neural networks.Taking 304 stainless steel as the research object for the slurry blasting impact tests,the influence degree of each process parameter on the surface roughness was analyzed by analysis of variance test on the sample data,from which the input and output parameters of the network model were determined for the training and validation of the network model.The results show that the maximum relative error between the predicted value of the network model blasting velocity and the total sample is 4.80%;the maximum relative error between the roughness value obtained experimentally for the given blasting velocity by the network model result and the target value is 2.40%.It demonstrates that the neural network model can guide the process parameter setting and realize the accurate control of surface roughness of steel plate after impacting.

关 键 词：抛浆冲击 BP神经网络 抛射速度 表面粗糙度 

分 类 号：TG731[金属学及工艺—刀具与模具]