Yun-Ming Zhu

Work place: Jiangsu University, Zhenjiang, China



Research Interests: Engineering


Yun-Ming Zhu born in Longyou, Zhejiang, China, in 1974. Received B.S. degree from Mechanical design and manufacture at Jiangsu Engineering College in 1997, and received M.S degree from Mechanical design and theory at Jiangsu University of Science and Technology in 2000, received Ph.D. degree from Machinery manufacturing and automation at Jiangsu University in 2006, Zhenjiang, China.
He is currently a associate professor in the School of Mechanical Engineering at Jiangsu University. His archival journal publications including: [1] Application and Analysis of RBF Neural Network for Burr Prediction in Micro-machining. Applied Mechanics and Materials, v 37-38, p 171-175, 2010, Advances in Engineering Design and Optimization. [2] Network database system for metal cutting burr, Advanced Materials Research,vols, 24-25, p 7-12, 2007. [3] The Effect of Shear Strain on Transformation between Burr and Negative Burr, Advanced Materials Research,vols, 53-54, p 89-93, 2008. Recent research topics are forming mechanism and control technology of burr in precision cutting, virtual manufacture and numerical manufacturing technology.

Dr. Zhu was awarded progress in science and technology for achievements on burr mechanism and controlling from: [1]from Ministry of Education of China in 2008, [2] Machinery Industry Commission of China in 2006.

Author Articles
Application of Neural Network on Burr Expert System in Micro-machining

By Yun-Ming Zhu Jun-Ping Chen Gang Zheng

DOI:, Pub. Date: 8 Feb. 2011

The demands placed by designers on workpiece performance and functionality are increasing rapidly. Important aspects of manufacturing’s contribution to the fulfillment of these demands are the conditions at the work piece edges. However, Burrs are often created on the workpiece edges in micro-machining. In many cases, time consuming and expensive deburring processes have to be applied in order to ensure the desired part functionality. Burrs make troubles on production lines in terms of deburring cost, quality of products and cutting tool wear. To prevent problems caused by burrs in micro-machining, prediction and control of burr size is desirable. Experimental studies show that burr formation in micro-milling is a highly complex process depending on a number of parameters such as material properties, tool geometry and cutting parameters. It is very difficult to establish the relationship between burr sizes and cutting conditions. A web-based micro-machining burr expert system for burr sizes prediction and control was developed using ASP.NET platform. Burrs types and sizes prediction and cutting conditions optimization for burr controlling which based on the reasoning method of BP neural networks are realized. Operation results show that the system is reliable. It provides a new technology for burrs modelling and controlling.

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