基于表面粗糙度的缸体打磨控制专家系统研究  

Expert system for cylinder grinding control based on surface roughness

作　　者：顾寄南[1] 潘甜 

机构地区：[1]江苏大学制造业信息化研究中心,江苏镇江212000

出　　处：《机电工程》2018年第3期219-223,共5页Journal of Mechanical & Electrical Engineering

基　　金：江苏省科技成果转化专项资金资助项目(BA2015026)

摘　　要：针对当前铸件后处理打磨过程中高污染、低效率和传统人工打磨出现的低质量、高成本等问题,对打磨过程各环节进行了研究,并对打磨过程进行了总结与优化。结合专家知识和操作经验,从机器人打磨过程中的表面粗糙度出发,设计和开发了应用于缸体表面打磨控制的专家系统;采用BP神经网络模型对打磨结果进行了预测,着重论述了打磨控制方法的改进,实现了加工参数的优化;在打磨专家系统实现过程中,对专家系统主要功能作了充分分析和详细设计,使用Java编程语言实现了系统功能,采用My SQL数据库进行了数据存储。研究结果表明:该系统可有效降低打磨过程中的表面粗糙度,提高表面质量;这对减轻工人劳动强度、提高打磨效率、降低环境污染、节约生产成本有着十分重要的意义。Aiming at the problems such as high quality,low efficiency and low quality and high cost of the traditional casting process,the grinding process was studied and the grinding process was summarized and optimized. Based on the expert knowledge and operating experience,the expert system for the grinding control of the cylinder surface was designed and developed from the surface roughness during the robot grinding process. The BP neural network model was used to predict the grinding results,and the improvement of the grinding control method was discussed,and the optimization of the machining parameters was realized. In the process of grinding expert system,the main function of the system was fully analyzed and designed in detail,using the Java programming language to achieve system function,using My SQL database for data storage. The results indicate that the system can effectively reduce the surface roughness during grinding and improve the surface quality. This is to reduce the labor intensity,improve the efficiency of grinding,reduce environmental pollution,saving production costs have a very important significance.

关 键 词：缸体打磨 专家系统 BP神经网络 

分 类 号：TH161.14[机械工程—机械制造及自动化] TP273[自动化与计算机技术—检测技术与自动化装置]