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송신형 순천향대학교 기초과학연구소 2019 순천향자연과학연구 논문집 Vol.25 No.1
Alloy 321 stainless steel is frequently used in high temperature environments. Thus, it is important to understand and model high temperature deformation of 321 stainless steel. This research performed hot deformation test for 321 stainless steel under temperature of 700℃-900℃ and strain rate of 0.0002/s-0.02/s. The deformation of 321 stainless steel was strain rate and temperature sensitive. Flow stress of 321 stainless steel during hot deformation was modeled using decision tree algorithm.
송신형,최우천 한국정밀공학회 2015 International Journal of Precision Engineering and Vol. No.
This work studies the effects of the tool edge radius in a micro-blanking process with negative clearance by using finite element method. For the study, a simulation model of blanking of AL6061-T6 foil with tools (punch and die) having a large edge radius comparable to the thickness of the foil was prepared. Using the model, micro-blanking process with various tool edge radii are simulated to study the resultant blanking forces and blank quality. From the simulation studies, it is found that a large edge radius of a blanking punch have effect on the blanking forces. Especially, a projecting edge was observed at the edge of the finished blank. This projecting edge is considered to be caused by negative clearance and a large edge radius of a die. Overall summary and discussion on the effect of a rounded tool edge on the blanking force and blank deformation is presented.
인공신경망을 이용한 인콜로이 825 합금의 고온 변형 거동 연구
송신형,김용배,이승용 한국생산제조학회 2018 한국생산제조학회지 Vol.27 No.1
In this research, a constitutive study of the high-temperature deformation behavior of Incoloy 825 alloy was performed using an artificial neural network (ANN). For the study, a high-temperature compression test on Incoloy 825 was carried out on a Gleeble 3500 system at temperatures ranging from 950-1,150°C and strain rates of 0.2/s and 2/s. After the compression test, the study of the flow stress was conducted for various temperatures and strain rates. The flow stress variation during the deformation of Incoloy 825 was dependent on the deformation temperature and strain rate. The flow stress at various deformation temperatures and strain rates was modeled using the Hollomon-type equation. The constitutive behavior of Incoloy 825 during hot temperature deformation was modeled using an ANN.
FEM Investigation on Thermal Effects on Force in High- Speed Blanking of Mild Steel
송신형,최우천 한국정밀공학회 2016 International Journal of Precision Engineering and Vol.17 No.5
This study consists of a numerical investigation of high-speed blanking of mild steel. A finite element model of the high-speed blanking process was developed using ABAQUS/explicit, and the strain rate and temperature-dependent behavior of the work material were included in the model by using the Johnson-Cook hardening model for mild steel. The blanking simulations had a punch speed ranging from 30 mm/s to 60 mm/s to study the influence of the thermal behavior of mild steel on the blanking force. In addition, the influence that the clearance, thickness of the work material and tool edge radius had on the blanking force was studied while changing the punch speed. The simulations revealed that the blanking force decreases when the punch speed increases beyond a certain amount as a result of thermal softening of the mild steel. Also, the clearance, material thickness and tool edge radius were observed to influence the development of the temperature. The results of this study can help understand the high-speed blanking process in order to design an apparatus for further research into high-speed blanking.
Deep Neural Network와 Support Vector Regression을 이용한 인콜로이 825의 고온변형 연구
송신형 조선대학교 공학기술연구원 2019 공학기술논문지 Vol.12 No.1
Incology825 is a nickel-based alloy with good corrosion resistance as well as heat resistance. For effective application of Incoloy825, the flow behavior of the Incoloy 825 during hot deformation needs to be studied and the efficient flow model developed. In this study, hot deformation test was carried out under the temperature of 925℃, 1050℃ and 1150℃ and strain rate of 0.003/s and 3/s. Deep neural network and support vector regression algorithm was thereafter used to model the flow behavior of Incoloy825. The research found that both algorithms describes the flow stress of incoloy825 well. Comparison between deep neural network and support vector regression was done.
심층신경망과 서포트벡터 회귀분석을 이용한 인코넬 601의 고온변형 연구
송신형 한국기계기술학회 2019 한국기계기술학회지 Vol.21 No.1
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%.