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머신 러닝을 이용한 레이저 용접의 용입깊이 모델링 및 공정변수 맵
고범수(Bum-su Go),유현정(Hyeonjeong You),방희선(Hee-seon Bang),김철희(Cheolhee Kim) 대한용접·접합학회 2021 대한용접·접합학회지 Vol.39 No.4
Penetration control is an important factor in determining the weld quality in keyhole mode laser welding, which enables deep penetration. In this study, machine learning models and neural network models were developed by using 380 published welding data which were constructed for steel base metals under the following welding conditions: a laser power of 0.3-16.7 kW, a welding speed of 0.3-20.0 m/min, and a bead diameter of 0.05-0.78 mm. A machine learning model SVM (supported vector machine) could accurately predict the penetration depth with a coefficient of determination, R² of 0.95. A shallow neural network model with five nodes in only one hidden layer was developed with a slightly improved accuracy with R² of 0.98. It was confirmed that neither model was overfitted, and process parameters (welding speed and beam diameter) maps with penetration depth contours were provided for a laser power of 2-8 kW.