高速铣削工件表面粗糙度蚁群-BP神经网络建模  

Modeling of high speed milling workpiece by surface roughness method

作　　者：祁翔 张心光 吕泽正 QI Xiang;ZHANG Xinguang;LV Zezheng(School of Mechanical and Automotive Engineering,Shanghai University of Engineering Science,Shanghai 201620,China)

机构地区：[1]上海工程技术大学机械与汽车工程学院,上海201620

出　　处：《智能计算机与应用》2021年第1期130-133,共4页Intelligent Computer and Applications

摘　　要：由于蚁群算法具有很好的多样性、兼容性和正反馈,故十分适合用于BP神经网络学习率的优化,从而建立蚁群-BP神经网络。训练样本对是以实验1、实验3、实验5、实验7、实验9、实验11、实验13和实验15下的高速铣削试验数据组成的,并用高速铣削实验中的工件表面粗糙度来建模。使用创建的高速铣削工件表面粗糙度预测模型来对实验2和实验6状态中的高速铣削工件表面粗糙度进行预测,通过对比预测结果和试验结果,可发现蚁群-BP神经网络能够十分有效地对高速铣削工件表面粗糙度进行建模预测。Because ant colony algorithm has good robustness,compatibility and positive feedback,it is very suitable for optimizing the learning rate of BP neural network,so as to establish ant colony-BP neural network.The training sample pair is composed of high-speed milling test data under experiment 1,experiment 3,experiment 5,experiment 7,experiment 9,experiment11,experiment 13 and experiment 15,and the surface roughness of workpiece in high-speed milling experiments is used to model.The surface roughness prediction model of high-speed milling workpiece is established to predict the surface roughness of highspeed milling workpiece in experiment 2 and experiment 6.By comparing the prediction results with the experimental results,it is found that ant colony-BP neural network can effectively model and predict the surface roughness of high-speed milling workpiece.

关 键 词：高速铣削 表面粗糙度 预测 蚁群-BP人工神经网络 

分 类 号：U491.1[交通运输工程—交通运输规划与管理]