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.
목차
Abstract 1. 서론 2. 공구 수명 3. 형상 계수 4. 실험 장치 및 방법 4.1 실험 장치 4.2 실험 방법 5. 실험 결과 및 분석 6. 결론 References
키워드
Shape Variation(형상 변화)Cutting Force Signal(절삭력 신호)Tool Wear(공구마모)Skewness(왜도)Kurtosis(첨도)Neural Network(신경회로망)