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On this page
  • 1. Introduction
  • 2. Data source
  • 3. Result and discussion
  • 3.1 ML models with all features
  • 3.2 Feature selection
  • 3.3 Mathematical expressions
  • 3.4 ML model based on atomic features
  • 3.5 Development of anti-fatigue high-strength steel
  • 4. Conclusion

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  1. Industrial AI
  2. TempCore Journal

Machine learning of mechanical properties of steels

PreviousTempCore JournalNextOnline prediction of mechanical properties of hot rolled steel plate using machine learning

Last updated 3 years ago

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2020, Jie et. al., SCIENCE CHINA Technological Sciences

1. Introduction

본 논문은 5가지 ML 알고리즘(특히 랜덤 포레스트 및 Symbolic regression)을 사용해 steel의 4가지 기계적 특성을 예측

2. Data source

  • 일본 재료과학연구소(NIMS)에서 공개한 철강 데이터셋 사용

    • 세계 최대의 실험 데이터셋 중 하나임

  • fatigue strength, tensile strength, fracture strength, hardness, room temperature, chemical composition, proceeding condition 등의 정보 포함

  • 피로강도에 대한 얘기

  • heating rate and cooling rate는 사용할 수 없었음

  • 열처리 온도 3가지 포함: Normalizing(불림), Quenching, Tempering

  • 열처리 시간 불포함

  • 불림 처리, 퀜칭, 템퍼링 처리가 없는 데이터를 제외하여 360개의 데이터로 줄어듦

3. Result and discussion

3.1 ML models with all features

  • 4개의 ML 알고리즘 사용: RF, LLS(linear least-square), kNN, ANN

  • 16개의 feature 사용

  • 10-fold cross-validation

  • 성능 측정: R(correlation coefficient), RRMSE 사용

  • RF는 fracture strength에 대해 가장 좋은 예측

  • ANN은 fatigue strength, tensile strength에 대해 가장 좋은 예측

3.2 Feature selection

  • RFI: RF에 의해 계산된 importance of feature

    • Mo, Cr, NT, TT가 가장 중요한 feature

  • SRI: SR에 의해 계산된 importance of feature

    • TT, C, Cr, Mo가 가장 중요한 요소

  • RFI, SRI로 선택한 feature들로 ML 알고리즘 적용

    • SRI subset으로 RF 진행한 결과가 가장 성능이 좋았음

3.3 Mathematical expressions

  • SR(Symbolic Regression)이란

    • mathematical expression을 찾는 회귀 방법

  • SR의 결과

    • 높은 예측력을 보임

    • (3)~(6)에 따른면 템퍼링 온도가 낮을 수록 TS와 Hardness가 향상됨

3.4 ML model based on atomic features

  • 예측 성능을 일반화하기 위해 atomic feature를 사용

    • iron은 steel의 matrix임

    • steel의 합금원소는 iron matrix(용매) 내에서 '용질'로 작용해 합금원소와 화합물을 형성하거나 작은 cluster로 침전될 수 있음

    • r_i = 원소 i의 원자 반경

    • x_i = 원소 x의 pauling electroengativity

    • a_i는 다음과 같이 표현 가능

  • RF와 SR로 atomic feature 선택

    • RFI로 선택한 feature

      tVEC, dVEC-Fe, dVEC-C and TT for fatigue strength and hardness

      tVEC, aFe, dVEC-C and TT for tensile strength and fracture strength

    • SRI로 선택한 feature

      dVEC-C, dr-Fe, aFe and TT for all four mechanical properties

    • RF 및 SR로 선택된 feature를 RFI-AF, SRI-AF로 명명

  • SRI, RFI로 선택한 feature의 RF 모델 예측 결과

    • All-AF가 SRI-AF와 유사한 성능

  • SRI, RFI로 선택한 feature의 SR 결과

    • 합금 원소가 강의 강도를 향상시킴

    • 예측 결과

3.5 Development of anti-fatigue high-strength steel

  • eqs 3-6, 7-10으로 예측한 결과와 조금 차이남

4. Conclusion

위 내용 요약