서포트벡터 회귀를 이용한 실시간 제품표면거칠기 예측

• 영문명: Real-Time Prediction for Product Surface Roughness by Support Vector Regression
• 발행기관: 한국산업경영시스템학회
• 저자명: 최수진(Sujin Choi) 이동주(Dongju Lee)
• 간행물 정보: 『산업경영시스템학회지』제44권 제3호, 117~124쪽, 전체 8쪽
• 주제분류: 공학 > 산업공학
• 파일형태: PDF
• 발행일자: 2021.09.30

영문 초록

The development of IOT technology and artificial intelligence technology is promoting the smartization of manufacturing system. In this study, data extracted from acceleration sensor and current sensor were obtained through experiments in the cutting process of SKD11, which is widely used as a material for special mold steel, and the amount of tool wear and product surface roughness were measured. SVR (Support Vector Regression) is applied to predict the roughness of the product surface in real time using the obtained data. SVR, a machine learning technique, is widely used for linear and non-linear prediction using the concept of kernel. In particular, by applying GSVQR (Generalized Support Vector Quantile Regression), overestimation, underestimation, and neutral estimation of product surface roughness are performed and compared. Furthermore, surface roughness is predicted using the linear kernel and the RBF kernel. In terms of accuracy, the results of the RBF kernel are better than those of the linear kernel. Since it is difficult to predict the amount of tool wear in real time, the product surface roughness is predicted with acceleration and current data excluding the amount of tool wear. In terms of accuracy, the results of excluding the amount of tool wear were not significantly different from those including the amount of tool wear.

목차

1. 서론

2. 실험장치

3. 공구마모와 제품표면거

4. 제품표면거칠기 예측 및 고찰

5. 결론

키워드

Support Vector Regression Machine Learning Surface Roughness