On-line detection system of the abnormal states in a machining process needs to be developed to implement the IMS(Intelligent Manufacturing System). High productivity and efficient quality control can be achieved through the on-condition maintenance for normal tool condition. Generally it is difficult to determine the exact point of time for a tool change because a tool wear grows gradually on the contrary to other abnormal states such as tool fracture, chattering etc. In this article, the shape variation of cutting force signal generated by a insert during face milling was investigated along with a tool wear. The variance, skewness and kurtosis were used as the shape parameters to describe the shape variation and, consequently, utilized as the features to monitor a tool wear. Experimental results showed that the shape parameters could discriminate the tool condition reliably between a fresh tool and a worn tool. As a result, we proposed the method to diagnose a tool wear by combining these parameters with a neural network algorithm.

This paper presents a study of the influence of cutting parameters on surface finish obtained by face milling. Cutting conditions have significantly great effect on surface quality in high-precision machining. Fundamentally, surface roughness are strongly correlated with cutting parameters such as feed rate, cutting speed and depth of cut. Optimal selection of the cutting parameters can obtain better surface roughness. Therefore, optimization of the cutting parameters in a face milling based on the parameter design of the Taguchi method is adopted in this paper to improve surface roughness. And optimal cutting parameters based on the results of the S/N ratio and ANOVA analyses to performance high-precision machining is obtained and verified by confirmation test.