摘要
为了通过预选切削参数来准确预测残余应力,从而提前调整切削参数以提高零件加工精度,分别从宏观预测和微观分析两个角度展开研究。宏观上为了提高残余应力预测精度,提出以45#钢为研究对象,构建优化扩展系数SPREAD后的径向基函数(RBF)神经网络,并将预测结果与实验值和有限元仿真值对比,证明了较有限元预测铣削残余应力而言,进行SPREAD优化后的RBF神经网络预测具有较高的准确性和优越性;微观上建立分子动力学(MD)模型,对相同条件下的铣削过程进行模拟仿真,将模拟结果与实验结果进行对比,发现残余应力在宏观上与微观之间存在负相关的潜在联系,为通过工艺处理调整工件微观结构以改善残余应力提供可行性验证。
To accurately predict the residual stress according to the pre-selected cutting parameters before machining,so as to improve the performance of the workpiece by adjusting the cutting parameters in advance,the research was carried out from the perspectives of macro prediction and micro analysis.At the macro level,to improve the prediction accuracy of residual stress,the Radial Basis Function(RBF)neural network model was constructed after the expansion coefficient SPREAD optimized with 45#steel as the research object,and then the prediction results was compared to the experimental value and the finite element simulation to prove the prediction after the SPREAD optimization had high accuracy and superiority.At the micro level,a molecular dynamics model was established to simulate the milling process under the same conditions.The simulation results of molecular dynamics were compared with the experimental results,and the result showed that there had existed a potential negative correlation between micro and macro of milling residual stress,which provided a certain feasibility to improve the residual stress by adjust the microstructure of the workpiece with process treatment.
作者
靳岚
张雪峰
谢黎明
JIN Lan;ZHANG Xuefeng;XIE Liming(School of Mechanical and Electrical Engineering, Lanzhou University of Technology, Lanzhou 730050, China)
出处
《计算机集成制造系统》
EI
CSCD
北大核心
2022年第5期1385-1392,共8页
Computer Integrated Manufacturing Systems
基金
国家自然科学基金资助项目(51965035)。
关键词
表面残余应力
铣削
径向基函数
神经网络预测
分子动力学
仿真
surface residual stress
milling
radial basis function
neural network prediction
molecular dynamics
simulation
