基于磨损监测保持切削加工表面质量稳定的实时控制研究

doi:10.3901/JME.2020.11.240

机械工程学报 ›› 2020, Vol. 56 ›› Issue (11): 240-248.doi: 10.3901/JME.2020.11.240

• 制造工艺与装备 • 上一篇

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基于磨损监测保持切削加工表面质量稳定的实时控制研究

廖小平¹, 陈楷¹, 鲁娟^1,2

1. 广西大学制造系统与先进制造技术重点实验室南宁 530004;
2. 北部湾大学机械与船舶海洋工程学院钦州 535011

收稿日期:2019-09-10 修回日期:2019-12-06 出版日期:2020-06-05 发布日期:2020-06-12
通讯作者: 鲁娟(通信作者),女,1988年出生,博士研究生。主要研究方向为数字化设计、智能制造。E-mail:lujuan3623366@163.com
作者简介:廖小平,男,1965年出生,博士,教授,博士研究生导师。主要研究方向为数字化设计、智能制造。E-mail:xpfeng@gxu.edu.cn
基金资助:
国家自然科学基金（51665005）和广西高校中青教师科研基础能力提升（2020KY10014）资助项目。

Research on Real-time Control of Machining Surface Quality Stability Based on Wear Monitoring

LIAO Xiaoping¹, CHEN Kai¹, LU Juan^1,2

1. Guangxi Key Laboratory of Manufacturing Systems and Advanced Manufacturing Technology, Guangxi University, Nanning 530004;
2. College of Mechanical and Marine Engineering, Beibu Gulf University, Qinzhou 535011

Received:2019-09-10 Revised:2019-12-06 Online:2020-06-05 Published:2020-06-12

摘要/Abstract

摘要： 在切削加工过程中，由于磨损会随时间的变化不断积累，导致加工质量产生波动。针对这个问题，提出了在大数据背景下，基于磨损监测对加工表面质量进行实时控制的方法。通过历史数据库建立切削力信号与磨损之间的映射关系，刀具磨损反映当前加工状态，将当前加工状态的加工质量与客户需求进行比较，从而优化加工参数使加工质量尽可能接近客户需求。优化模型是利用广义回归神经网络的建模原理进行建模，使得优化问题能通过非线性规划求解，并能快速做出调控决策。研究还对TC18材料进行大量铣削试验，试验结果验证了方法的可靠性，也证明了此方法能做到对加工状态变化的快速响应。此研究解决了现有研究中不能同时保证控制精度和响应时间的问题，并为在线智能控制切削加工表面质量提供了新思路。

关键词: 粗糙度, 大数据, 广义回归神经网络, 磨损, 质量稳定控制

Abstract: In the process of cutting, the wear will accumulate with the change of time, resulting in the fluctuation of machining quality. In order to solve this problem, a real-time control method of surface quality based on wear monitoring is proposed under the background of large data. The mapping relationship between cutting force signal and wear is established through historical database. Tool wear reflects the current processing state. By comparing the processing quality of the current processing state with customer demand, the processing parameters are optimized to make the processing quality as close as possible to customer demand. The optimization model is based on the modeling principle of generalized regression neural network, so that the optimization problem can be solved by non-linear programming and the control decision can be made quickly. A large number of milling experiments have been carried out on TC18 material. The experimental results verify the reliability of the method, and also prove that the method can respond quickly to the change of processing state. This research solves the problem that the control accuracy and response time cannot be guaranteed simultaneously in the existing research, and provides a new idea for online intelligent control of cutting surface processing quality.

Key words: roughness, big data, generalized regression neural network, wear, quality stability control

中图分类号:

TP273

廖小平, 陈楷, 鲁娟. 基于磨损监测保持切削加工表面质量稳定的实时控制研究[J]. 机械工程学报, 2020, 56(11): 240-248.

LIAO Xiaoping, CHEN Kai, LU Juan. Research on Real-time Control of Machining Surface Quality Stability Based on Wear Monitoring[J]. Journal of Mechanical Engineering, 2020, 56(11): 240-248.

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基于磨损监测保持切削加工表面质量稳定的实时控制研究

Research on Real-time Control of Machining Surface Quality Stability Based on Wear Monitoring

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