정면 밀링 공정에서 절삭력 신호 형상 변화를 이용한 공구마모 검출에 관한 연구
A Study on Tool Wear Detection Using Shape Variation of Cutting Force Signal in Face Milling
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초록
On-line detection system of the abnormal states in a machining process needs to be developed toimplement the IMS(Intelligent Manufacturing System). High productivity and efficient quality control canbe achieved through the on-condition maintenance for normal tool condition. Generally it is difficult todetermine the exact point of time for a tool change because a tool wear grows gradually on the contraryto 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 millingwas investigated along with a tool wear. The variance, skewness and kurtosis were used as the shapeparameters to describe the shape variation and, consequently, utilized as the features to monitor a toolwear. Experimental results showed that the shape parameters could discriminate the tool condition reliablybetween a fresh tool and a worn tool. As a result, we proposed the method to diagnose a tool wear bycombining these parameters with a neural network algorithm.
Abstract
On-line detection system of the abnormal states in a machining process needs to be developed toimplement the IMS(Intelligent Manufacturing System). High productivity and efficient quality control canbe achieved through the on-condition maintenance for normal tool condition. Generally it is difficult todetermine the exact point of time for a tool change because a tool wear grows gradually on the contraryto 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 millingwas investigated along with a tool wear. The variance, skewness and kurtosis were used as the shapeparameters to describe the shape variation and, consequently, utilized as the features to monitor a toolwear. Experimental results showed that the shape parameters could discriminate the tool condition reliablybetween a fresh tool and a worn tool. As a result, we proposed the method to diagnose a tool wear bycombining these parameters with a neural network algorithm.
참고문헌 (9)
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- 변동해, "신경망을 이용한 고경도강 선삭시 공구 마멸 검출에 관한 연구", 한국기계기술학회지, 14(2), pp.67~74 (2012), 한국기계기술학회[DOI]