| About this Abstract |
| Meeting |
MS&T23: Materials Science & Technology
|
| Symposium
|
Materials Processing and Fundamental Understanding Based on Machine Learning and Data Informatics
|
| Presentation Title |
B-2: Simple Data Analytics Approach Coupled with Physics-based Model
for Improved Prediction of Creep Rupture Life |
| Author(s) |
Taejoo Lee, Yoon Suk Choi, Chang Ho Lee |
| On-Site Speaker (Planned) |
Taejoo Lee |
| Abstract Scope |
Creep rupture life is important parameter for high-temperature materials like heat resistant steel and Ni-based superalloy. Therefore, various physics-based and empirical models were developed. Recently, there are also increasing cases of using machine learning models to predict creep rupture life. However, machine learning models are not suitable for small or non-uniform dataset. The proposed approach involves linear regression with four features as a major algorithm that exhibited superior creep rupture life prediction. In particular, the approach was useful to assess the fidelity of the Laron-Miller relation for a given creep rupture life dataset and to find an optimum Larson-Miller constant that minimizes a deviation from the ideal Larson-Miller relation. An analytical model was also developed based on curve fitting of Larson-Miller parameters calibrated by the optimum Larson-Miller constant. The proposed analytical model gave additional improvement in creep rupture life prediction. |