摘要
针对磨料水射流切割性能与影响因素间存在复杂的非线性关系,无法用传统数学方法建模的问题,基于BP人工神经网络理论,结合典型材料的切割实验结果,在考虑射流压力、磨料流量、切割靶距、工件厚度、磨料喷嘴直径与切割速度6个因素情况下,建立了磨料水射流切割BP神经网络模型。同时,基于Delphi开发出了可移植的磨料水射流切割速度人工神经网络预测单元,实现了所建网络模型的可视化,为实现网络模型与数控系统的集成提供条件。研究结果表明,该网络模型能快速、准确、可靠地预测切割速度,与数控系统相集成可实现对磨料水射流切割质量的有效控制。
It is difficult to establish a cutting model using traditional mathematical methods for the abrasive water jet because of the com plex nonlinear relationship between the cutting performance and the influencing factors. A BP neural network cutting model of abrasive water jet, which contains six influencing factors as jet pressure, abrasive flow, cutting target distance, work piece thickness, abrasive noz zle diameter and cutting speed, was established based on BP artificial neural network theory and the results of the cutting experiment with typical material. A portable abrasive water jet cutting speed artificial neural network prediction unit was developed to realize the vi sualization of the network model based on Delphi, providing conditions for the integration of network model and NC system. The results showed that the network model integrated with NC system can predict the cutting speed rapidly, and accurately reliably and realize the effective control of the cutting quality.
出处
《四川大学学报（工程科学版）》
EI
CAS
CSCD
北大核心
2013年第3期164-170,共7页
Journal of Sichuan University (Engineering Science Edition)
基金
国家科技重大专项资助项目(2011ZX05065-3)
国家自然科学基金资助项目(51104191)
重庆市自然科学基金资助项目(cstcjjA90004)
关键词
磨料水射流
切割模型
BP神经网络
可视化
abrasive water jet
cutting model
BP neural network
visualization
