논문 상세보기
Real-Time Prediction for Product Surface Roughness by Support Vector Regression KCI 등재
서포트벡터 회귀를 이용한 실시간 제품표면거칠기 예측
pp.117-124
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.
2. 실험장치
2.1 실험장비
2.2 실험방법
3. 공구마모와 제품표면거칠기
4. 제품표면거칠기 예측 및 고찰
4.1 GSVQR
4.2 GSVQR을 이용한 제품표면거칠기 예측
5. 결 론
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