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工程科学与技术:2013,45(3):164-170
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磨料水射流切割可视化BP神经网络模型研究
(1.重庆大学 煤矿灾害动力学与控制国家重点实验室;2.重庆大学 复杂煤气层瓦斯抽采国家地方联合工程实验室;3.四川大学 制造科学与工程学院)
Study on Visual BP Neural Network Cutting Model for Abrasive Water Jet
(1.State Key Lab. of Coal Mine Disaster Dynamics and Control,Chongqing Univ.;2.National & Local Joint Eng. Lab. of Gas Drainage in Complex Coal Seam,Chongqing Univ.;3.School of Manufacturing Sci. and Eng.,Sichuan Univ.)
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投稿时间:2012-09-10    修订日期:2012-11-26
中文摘要: 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。
Abstract:It is difficult to establish a cutting model using traditional mathematical methods for the abrasive water jet because of the complex 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 nozzle 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 visualization 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.
文章编号:201200700     中图分类号:    文献标志码:
基金项目: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)
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汤积仁,卢义玉,孙惠娟.磨料水射流切割可视化BP神经网络模型研究[J].工程科学与技术,2013,45(3):164-170.
Tang Jiren,Lu Yiyu,Sun Huijuan.Study on Visual BP Neural Network Cutting Model for Abrasive Water Jet[J].Advanced Engineering Sciences,2013,45(3):164-170.