Ti-6242 합금의 열간 단조를 위한 공정 매개변수 최적화
Optimizing Process Parameters for Hot Forging of Ti-6242 Alloy : A Machine Learning and FEM Simulation Approach
- 발행기관 서강대학교 일반대학원
- 지도교수 김낙수
- 발행년도 2024
- 학위수여년월 2024. 2
- 학위명 석사
- 학과 및 전공 일반대학원 기계공학과
- 실제URI http://www.dcollection.net/handler/sogang/000000076675
- UCI I804:11029-000000076675
- 본문언어 한국어
- 저작권 서강대학교 논문은 저작권 보호를 받습니다.
초록
In this study, we investigated the hot deformation behavior of Ti-6Al-2Sn-4Zr-2Mo (Ti-6242) alloy and propose a method to derive optimal hot process parameters for grain refinement and avoidance of flow instability. Microstructural Risk Index (MRI) was introduced as a microstructural evaluation index consisting of grain size, standard deviation of grain size, and flow instability. The initial temperature of the material and the stroke speed of the die were selected as design variables. Finite element analysis (FEA) was used to calculate grain size and flow instability and determine MRI of the forgings. The grain size model coefficient and flow instability were calculated based on the flow stress curve and verified with optical microscope (OM) and electron backscatter diffraction (EBSD) analysis results. The Deep Neural Network (DNN) model was used to determine the optimal process parameters for the forging process. The MRI prediction accuracy of the trained DNN models showed excellent performance at 97.01%. The MRI of the optimized process variables was improved by 7.95% compared to the minimum MRI of the training data set. Optimized process parameters can improve the quality of forgings through grain refinement and avoidance of flow instability regions. Keywords: As-forged Ti-6242 alloy; Hot deformation behavior; Dynamic recrystallization; Flow instability; FEM numerical simulation
more목차
1. Introduction 1
2. Material and experimental procedure 6
2.1 Material and hot compression tests 6
2.2 Flow stress curves 8
3. Modeling and analysis of Microstructural Risk Index (MRI) 10
3.1 DRX kinetic model 10
3.2 Flow instability criterion theory 14
3.3 Objective function for microstructure evaluation 17
4. Result and discussion 19
4.1 Analysis of FEM simulation 19
4.2 Verification of JMAK model . 20
4.3 Verification of flow instability criterion 23
4.4 Process parameter optimization 26
4.4.1 Optimal processing parameter design process 26
4.4.2 Optimization result 28
5. Conclusion 31
Appendix 33
Reference 38
