车削增材制造Inconel 718切削力及预测模型研究  

Study of Cutting Force and Its Prediction Model for Turning Additively Manufactured Inconel 718

作　　者：周浩淳 金成哲[1] 李治达 刘玮 Zhou Haochun;Jin Chengzhe;Li Zhida;Liu Wei(School of Mechanical Engineering,Shenyang Ligong University,Shenyang 110159,China;不详)

机构地区：[1]沈阳理工大学机械工程学院,沈阳市110159 [2]沈阳理工大学装备工程学院

出　　处：《工具技术》2025年第3期111-115,共5页Tool Engineering

基　　金：辽宁省属本科高校基本科研业务费专项资金资助(LJ232410144074)。

摘　　要：为研究切削参数对车削激光增材制造Inconel 718镍基高温合金切削力的影响规律,本文结合车削仿真和切削试验,对比分析不同切削参数下车削增材制造Inconel 718切削力,并建立切削力预测模型。研究结果表明:切削参数对切削力的影响程度为背吃刀量a_(p)>进给量f>切削速度v_(c),仿真与切削试验结果误差为3.66%,验证了仿真模型的合理性和准确性。基于RBF和BP神经网络分别建立切削力预测模型,两种神经网络预测模型平均预测精度为96.274%和95.484%,预测精度较高,且RBF神经网络模型的预测精度更高,本研究为车削激光熔融增材制造Inconel 718切削力预测提供了理论基础。To study the influence law of cutting parameters on the cutting force of turning laser additive manufacturing Inconel 718 nickel-based high temperature alloy,this paper adopts the method of combining turning simulation and cutting test,and carries out the comparative analysis of simulation and test on the cutting force of turning additive manufacturing Inconel 718 under different cutting parameters,and establishes the prediction model of cutting force.The results show that the influence degree of cutting parameters on cutting force follows the order:depth of cut a_(p)>feed f>cutting speed v_(c),and the error between simulation and cutting test results is 3.66%,which verifies the reasonableness and accuracy of the simulation model.The cutting force prediction model is established based on RBF and BP neural networks respectively,and the average prediction accuracy of the two neural network prediction models is 96.274%and 95.484%,both of which have high prediction accuracy,while the RBF neural network model has a higher prediction accuracy,which provides a theoretical basis for the prediction of cutting force of Inconel 718 for turning laser melting additive manufacturing.

关 键 词：车削 增材制造Inconel 718 切削力 切削参数 预测模型 

分 类 号：TG51[金属学及工艺—金属切削加工及机床] TH16[机械工程—机械制造及自动化]