Research on Intelligent Monitoring Technique of Machining State for Surface Grinder Based on Bayesian Network

China Mechanical Engineering ›› 2011, Vol. 22 ›› Issue (11): 1269-1273.

Previous Articles Next Articles

Research on Intelligent Monitoring Technique of Machining State for Surface Grinder Based on Bayesian Network

Lin Feng1;Jiao Huifeng2;Fu Jianzhong2

1.Quzhou University, Quzhou, Zhejiang, 324000

2.The Zhejiang Province Key Lab of Advanced Manufacturing Technology, Zhejiang University,Hangzhou, 310027

Online:2011-06-10 Published:2011-06-17
Supported by:
National Science and Technology Major Project ( No. 2009ZX04001-131)

基于贝叶斯网络的平面磨削状态智能监测技术研究

林峰1;焦慧锋2;傅建中2

1.衢州学院，衢州，324000

2.浙江大学浙江省先进制造技术重点实验室，杭州，310027

基金资助:
国家科技重大专项(2009ZX04001-131)
National Science and Technology Major Project ( No. 2009ZX04001-131)

Abstract

Abstract:

In order to solve the problem of predicting workpiece quality and identifying blunt level of wheel in surface grinding, a Bayesian network model for monitoring grinding states of surface grinder was set up. The model can realize predict workpiece quality and identify blunt level of wheel by detecting kurtosis of acoustic emission with known grinding parameters and workpiece material. The results provide reference for NC system to optimize parameters. The technique obtains good effect by experiments on CNC surface grinder.

Key words: surface grinder, Bayesian network, acoustic emission, prediction of roughness

摘要：

为解决平面磨削过程中工件表面粗糙度预测和砂轮钝化监测困难的问题，利用贝叶斯网络建立了平面磨削状态智能监测模型。该模型在获取系统磨削用量和工件材料的基础上，在线提取磨削声发射信号的峭度系数，可以有效预测工件粗糙度和识别砂轮钝化状态，为数控系统调节加工参数提供参考。该模型在平面磨床的磨削监测试验中取得了良好的效果。



关键词:

平面磨削, 贝叶斯网络, 声发射技术, 粗糙度预测

CLC Number:

TG582

LIN Feng-1, JIAO Hui-Feng-2, FU Jian-Zhong-2.

Research on Intelligent Monitoring Technique of Machining State for Surface Grinder Based on Bayesian Network

[J]. China Mechanical Engineering, 2011, 22(11): 1269-1273.

林峰1, 焦慧锋2, 傅建中2.

基于贝叶斯网络的平面磨削状态智能监测技术研究

[J]. 中国机械工程, 2011, 22(11): 1269-1273.

[1]	TONG Zhaojing, WANG Pengchao, FAN Yongkui, HAN Guangyang, WANG Ziqi. Fault Diagnosis Method of Rolling Bearings Based on Improved Refined Composite Multiscale Sample Entropy and Bayesian Network [J]. China Mechanical Engineering, 2025, 36(12): 2952-2959.
[2]	WU Guanwu, WU Liqun, WANG Hongcheng, WANG Yaxing, GUO Jinshuai, ZHANG Zheng, WU Jiaxin, LI Liangliang. A Vortex Focused Transmission Method for Bearing Fault Acoustic Emission Signals [J]. China Mechanical Engineering, 2025, 36(01): 78-86.
[3]	CHEN Dongning, HU Yanlong, YAO Chengyu, WANG Kuantong, MA Lei, . Multi-dimensional Dynamic Bayesian Network and Its Importance Measure Analysis Method [J]. China Mechanical Engineering, 2022, 33(19): 2340-2346.
[4]	WANG Kang, QI Jinping, ZHOU Yahui, LI Shaoxiong, ZHAO Ruihu, GUO Hao. Reliability Analysis of Train Control Center Based on Discrete-time Bayesian Network [J]. China Mechanical Engineering, 2021, 32(04): 390-398.
[5]	CHEN Dongning1,2;HOU Annong3;YAO Chengyu3;HOU Xin1,2;XING Ran3. A Novel Dynamic Bayesian Network Analysis Method [J]. China Mechanical Engineering, 2020, 31(12): 1394-1406,1414.
[6]	ZHANG Lijun;HAN Lin;ZHANG Dexiao;ZHANG Junwei;ZHANG Xuqiang;ZHANG Xin;XIANG Hengfu. Tribological Performance Analysis of Circular Hammerhead-bar Friction Pair in Precision Blanking [J]. China Mechanical Engineering, 2020, 31(04): 410-416,424.
[7]	XIE Yingxing;WANG Chengyong;ZHENG Lijuan;SUI Jianbo. AE Signal Characteristics of High-speed Milling Hardened Steels with Coated Tools [J]. China Mechanical Engineering, 2019, 30(17): 2030-2039.
[8]	GONG Xiaorong, LI Xiaobin, YIN Chao. Optimization Decision Method of Machine Tool Resources Based on Bayesian Network under Cloud Manufacturing Environments [J]. China Mechanical Engineering, 2018, 29(20): 2438-2445.
[9]	WANG Lihua1;YANG Jiawei1;ZHANG Yonghong1;ZHAO Xiaoping2;XIE Yangyang1. Tool Wear Condition Recognition Based on SDAE [J]. China Mechanical Engineering, 2018, 29(17): 2038-2045.
[10]	QI Jinping1, 2;LI Xingyun1. Failure Risk Analysis of EMU Pantograph Systems [J]. China Mechanical Engineering, 2018, 29(07): 824-828.
[11]	CHEN Bing1,2;WANG Ge3;LIU Jianming4;DENG Zhaohui1,2;ZHAO Qingliang5. Precision Truing of Arc-shaped Diamond Wheel and On-line Monitoring of Truing Processes by AE Signals [J]. China Mechanical Engineering, 2017, 28(18): 2242-2249.
[12]	Zhang Qiang, Wang Haijian, Li Liying , Wen Xuezhen , Nguyen Viet Tuyen, . Failure Diagnosis of Shearer Picks Based on Information Fusion from Multi Sensors [J]. China Mechanical Engineering, 2016, 27(17): 2334-2340.
[13]	Li Guanghai, Jiao Jinping, Wang Xinjian, He Cunfu, Wu Bin. Acoustic Emission Behavior of 12MnNiVR Steel under Stretching [J]. China Mechanical Engineering, 2016, 27(13): 1760-1766.
[14]	Zhang Ruijun, Zhang Lulu, Wang Xiaowei, Wang Nannan. Research on Reliability of Multi-state System Based on Interval Triangular Fuzzy Bayesian Networks [J]. China Mechanical Engineering, 2015, 26(8): 1092-1097.
[15]	Wang Yan, Yang Lin, Ding Chengcai, Ding Chengye. Simulation and Experiments of WEDM-LS Based on Truncated Cone-shaped Thermal Conduction Model [J]. China Mechanical Engineering, 2015, 26(5): 598-605.

Research on Intelligent Monitoring Technique of Machining State for Surface Grinder Based on Bayesian Network

基于贝叶斯网络的平面磨削状态智能监测技术研究

PDF

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 15

Recommended Articles

Metrics