基于GA-GRNN的AWJ强化3D打印AlSi10Mg表面性能实验研究OA北大核心CSTPCD

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.