现代制造工程Issue(7)：35-41,7.DOI:10.16731/j.cnki.1671-3133.2024.07.005
基于GA-GRNN的AWJ强化3D打印AlSi10Mg表面性能实验研究
Experimental study on the surface properties of AWJ surface strengthening 3D printed AlSi10Mg based on GA-GRNN
摘要
Abstract
Order to improve the accuracy and efficiency of the prediction of the strengthening effect of Abrasive Water Jet(AWJ)strengthening process on the surface properties of 3D printed AlSi10Mg materials,firstly,the surface strengthening exper-iment of AlSi10Mg material strengthened by abrasive waterjet was carried out.Then,based on the GA-GRNN neural network,the experimental data samples were trained with the surface hardness and surface residual stress as the target respectively,and the surface performance prediction model of 3D printed AlSi10Mg was established.Finally,the main parameters of AWJ strengthening in the established neural network model were optimized by genetic algorithm.The results show that the surface hardness and sur-face residual stress of AlSi10Mg material are effectively improved after abrasive water jet strengthening.The error of the estab-lished GA-GRNN prediction model is within 2.3％,which has high accuracy.After optimization by genetic algorithm,the best pa-rameter combination of surface hardness is obtained jet pressure 33 MPa,abrasive particle size 0.15 mm,target distance 12.4 mm,and the surface hardness is 159.25HV.The optimal parameter combination of surface residual stress is jet pressure 40 MPa,abrasive particle size 0.13 mm,target distance 15 mm,and the surface residual stress is-137.4 MPa.It provides data sup-port for the parameter selection of the surface of the subsequent abrasive water jet strengthening parts.关键词
磨料水射流/3D打印的AlSi10Mg/表面强化/GA-GRNN神经网络/遗传算法Key words
Abrasive Water Jet(AWJ)/3D printed AlSi10Mg/surface strengthen/GA-GRNN neural network/genetic algorithm分类
矿业与冶金引用本文复制引用
张苗苗,侯荣国,吕哲,王龙庆,石广行,王中庆..基于GA-GRNN的AWJ强化3D打印AlSi10Mg表面性能实验研究[J].现代制造工程,2024,(7):35-41,7.基金项目
山东省自然科学基金项目(ZR2020ME154) （ZR2020ME154）
