v-ASVR을 이용한 공구라이프사이클 최적화

• 영문명: Tool Lifecycle Optimization using v-Asymmetric Support Vector Regression
• 발행기관: 한국산업경영시스템학회
• 저자명: 이동주(Dongju Lee)
• 간행물 정보: 『산업경영시스템학회지』제43권 제4호, 208~216쪽, 전체 9쪽
• 주제분류: 공학 > 산업공학
• 파일형태: PDF
• 발행일자: 2020.12.30

영문 초록

With the spread of smart manufacturing, one of the key topics of the 4th industrial revolution, manufacturing systems are moving beyond automation to smartization using artificial intelligence. In particular, in the existing automatic machining, a number of machining defects and non-processing occur due to tool damage or severe wear, resulting in a decrease in productivity and an increase in quality defect rates. Therefore, it is important to measure and predict tool life. In this paper, -ASVR (-Asymmetric Support Vector Regression), which considers the asymmetry of -tube and the asymmetry of penalties for data out of -tube, was proposed and applied to the tool wear prediction problem. In the case of tool wear, if the predicted value of the tool wear amount is smaller than the actual value (under-estimation), product failure may occur due to tool damage or wear. Therefore, it can be said that -ASVR is suitable because it is necessary to overestimate. It is shown that even when adjusting the asymmetry of -tube and the asymmetry of penalties for data out of -tube, the ratio of the number of data belonging to -tube can be adjusted with . Experiments are performed to compare the accuracy of various kernel functions such as linear, polynomial. RBF (radialbasis function), sigmoid, The best result isthe use of the RBF kernel in all cases

목차

1. 서 론

2. 기존의 v-SVR과 비대칭적 v-SVR

3. 제안하는 v-ASVR

4. 실 험

5. 결 론

References

키워드

Tool Life Prediction -Support Vector Regression Machine Learning Smart Manufacturing