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

作　　者：张苗苗 侯荣国[1,2] 吕哲 王龙庆[1] 石广行 王中庆 ZHANG Miaomiao;HOU Rongguo;L Zhe;WANG Longqing;SHI Guangxing;WANG Zhongqing(School of Mechanical Engineering,Shandong University of Technology,Zibo 255000,China;Shandong Provincial Key Laboratory of Precision Manufacturing and No-traditional Machining,Zibo 255000,China)

机构地区：[1]山东理工大学机械工程学院,淄博255000 [2]山东省精密制造与特种加工重点实验室,淄博255000

出　　处：《现代制造工程》2024年第7期35-41,共7页Modern Manufacturing Engineering

基　　金：山东省自然科学基金项目(ZR2020ME154)。

摘　　要：为提高磨料水射流(Abrasive Water Jet,AWJ)强化工艺对3D打印AlSi10Mg表面性能的强化效果预测的准确性及高效性,首先开展磨料水射流强化AlSi10Mg表面强化实验;然后分别以表面硬度和表面残余应力作为目标,基于遗传算法-广义回归神经网络(Genetic Algorithm-Generalized Ragression Neural Network,GA-GRNN)对实验数据样本进行训练,建立3D打印AlSi10Mg表面性能预测模型;最后,利用遗传算法对建立的神经网络预测模型中的AWJ强化主要参数进行优化。研究结果表明,经过磨料水射流强化后的AlSi10Mg表面硬度与表面残余应力均得到有效提高;建立的GA-GRNN预测模型与校验值误差在2.3%以内,具有较高的准确性;经遗传算法优化后,得到表面硬度最佳参数组合:射流压力为33 MPa,磨料粒径为0.15 mm,靶距为12.4 mm,此时表面硬度为159.25HV;表面残余应力最佳参数组合:射流压力为40 MPa,磨料粒径为0.13 mm,靶距为15 mm,此时表面残余应力为-137.4 MPa。为后续磨料水射流强化零件表面的参数选择提供数据支撑。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.

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 

分 类 号：TG73[金属学及工艺—刀具与模具] TH142.2[一般工业技术—材料科学与工程]