摘要
为了能够快速而准确地选择加工参数,并且保证产品的加工质量,提出了基于BP网络模型的车削参数智能选择方法。应用正交试验获取影响加工表面粗糙度的切削参数如吃刀量、进给量、转速和刀具圆角大小各因素的映射关系,建立不同加工阶段的切削数据库。通过BP网络模型学习和训练,不仅能智能的选择加工参数,而且能够划分产品加工阶段。将试验数据和BP网络预测的数据进行回归分析,验证了BP模型的实用性。
In order to select cutting parameters quickly and accurately and ensure processing quality of products,an intelligent selection method of cutting parameters based on BP neural network model is proposed.Process parameters such as cutting depth,feed,spindle speed and tool fillet size effect surface roughness by using orthognal experiment. Different processing stages of the cutting parameter databases are created based on test data. BP neural networks modle can not only choose the processing parameters intelligently,but divide processing stages by learning and training fuctions. The test data and BP network prediction data regression analysis is to verify the practicality of BP model.
出处
《组合机床与自动化加工技术》
北大核心
2017年第2期157-160,共4页
Modular Machine Tool & Automatic Manufacturing Technique
基金
河北省自然科学基金项目(E2010000052)
关键词
正交试验
切削参数
车削加工
BP网络
回归分析
orthogonal experiment
cutting parameter
turning process
BP neural networks
regression analysis
