Optimizing casting process using a combination of small data machine learning and phase-field simulations

Abstract It has been a challenge to employ machine learning (ML) to optimize casting processes due to the scarcity of data and difficulty in feature expansion. Here, we introduce a nearest neighbor search method to optimize the stratified random sampling in Latin hypercube sampling (LHS) and propose a new revised LHS coupled with Bayesian optimization (RLHS-BO). Using this method, we optimized the squeeze-casting process for mine fuel tank partition castings for the first time with an ultra-small dataset of 25 samples. Compared to traditional methods such as random sampling, interval sampling, orthogonal design (OD), and central composite design (CCD), our approach covers the process parameter space more,…

• NN
  National Natural Science Foundation of China

  Awards: 52275390, 52074246, 52375394
• ND
  National Defense Basic Scientific Research Program of China

  Award: JCKY2020408B002