Experimental study on the surface properties of AWJ surface strengthening 3D printed AlSi10Mg based on GA-GRNN

doi:10.16731/j.cnki.1671-3133.2024.07.005

Abstract

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 experiment of AlSi10Mg material strengthened by abrasive water jet 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 surface residual stress of AlSi10Mg material are effectively improved after abrasive water jet strengthening.The error of the established GA-GRNN prediction model is within 2.3 %,which has high accuracy.After optimization by genetic algorithm,the best parameter 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 support for the parameter selection of the surface of the subsequent abrasive water jet strengthening parts.

Key words: Abrasive Water Jet (AWJ), 3D printed AlSi10Mg, surface strengthen, GA-GRNN neural network, genetic algorithm

CLC Number:

TG73
TH142.2

ZHANG Miaomiao, HOU Rongguo, LÜ Zhe, WANG Longqing, SHI Guangxing, WANG Zhongqing. Experimental study on the surface properties of AWJ surface strengthening 3D printed AlSi10Mg based on GA-GRNN[J]. Modern Manufacturing Engineering, 2024, 526(7): 35-41.

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