Journal of the Korea Institute of Information and Communication Engineering (한국정보통신학회논문지)

The Korea Institute of Information and Commucation Engineering (한국정보통신학회)
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Degradation Quantification Method and Degradation and Creep Life Prediction Method for Nickel-Based Superalloys Based on Bayesian Inference

베이지안 추론 기반 니켈기 초합금의 열화도 정량화 방법과 열화도 및 크리프 수명 예측의 방법

  • Junsang, Yu (Data Analytics Team, Doosan Enerbility) ;
  • Hayoung, Oh (College of Computing and Informatics, Sungkyunkwan University)
  • Received : 2022.11.20
  • Accepted : 2022.12.02
  • Published : 2023.01.31
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Abstract

The purpose of this study is to determine the artificial intelligence-based degradation index from the image of the cross-section of the microstructure taken with a scanning electron microscope of the specimen obtained by the creep test of DA-5161 SX, a nickel-based superalloy used as a material for high-temperature parts. It proposes a new method of quantification and proposes a model that predicts degradation based on Bayesian inference without destroying components of high-temperature parts of operating equipment and a creep life prediction model that predicts Larson-Miller Parameter (LMP). It is proposed that the new degradation indexing method that infers a consistent representative value from a small amount of images based on the geometrical characteristics of the gamma prime phase, a nickel-base superalloy microstructure, and the prediction method of degradation index and LMP with information on the environmental conditions of the material without destroying high-temperature parts.

본 연구의 목적은 고온부품의 소재로 사용하는 니켈기 초합금인 DA-5161 SX에 대한 크리프시험으로 얻은 시편 의 주사전자현미경으로 촬영한 미세조직 단면의 이미지로부터 인공지능 기반 열화인덱스(Degradation Index)로 정 량화 하는 새로운 방법을 제시하고 운전 중인 기기의 고온부품의 구성품을 파괴하지 않고 베이지안 추론 기반 열화 도를 예측하는 모델과, Larson-Miller Parameter(LMP)를 예측하여 크리프 수명 예측 모델을 제안하는 것이다. 니켈 기 초합금 미세조직인 감마프라임 상(γ')의 기하학적 특징 및 베이지안 추론 기반으로 소량의 이미지로 일관성 있는 대푯값을 추론하는 새로운 열화인덱스 방법과 고온부품을 파괴하지 않고 소재의 환경조건 정보만으로 열화인덱스 와 LMP를 예측할 수 있는 방법을 제안한다.

Keywords

Acknowledgement

This work was supported by the Doosan Enerbility and the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIT) (No. NRF-2022R1F1A1074696).

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