This research is about a study on the flow stress of Inconel 601 under hot deformation. For Inconel 601, hot compression tests on gleeble 3500 system under 925℃, 1050℃ and 1150℃ and 0.001/s, and 5/s of strain rates were done. The flow behavior of the Inconel 601 was studied and modeled. In this study, the flow stress was modeled using deep neural network and support vector regression algorithm. The flow stress of Inconel 601 was dependent on strain rate and temperature. It was found that both the deep neural network and support vector regression adequately described the flow stress variation of Inconel 601. However, the model by the support vector regression was found to be superior to the model by the deep neural network. The construction of the model by SVR was more efficient than the construction by DNN. Also the prediction accuracy of the model by SVR was better than the accuracy of the model by DNN. It is found that the MAPE(Mean absolute percentage error) of the DNN based model was 4.89% while the MAPE of the SVR based model was 1.98%.

ABSTRACT
 1. 서 론
 2. 고온변형시험
  2.1 시험준비
  2.2 시험결과
 3. 딥러닝 알고리즘
  3.1 심층신경망(DNN)
  3.2 서포트벡터 회귀문석(SVR)
 4. 결과 및 토의
 5. 결 론
 References

• 송신형(Department of Smart Automobile, Soonchunhyang University) | Shin-Hyung Song Corresponding Author