NpjComput.Mater.：小資料機器學習耦合相場模擬：解鎖鑄造工藝最佳化的密碼

海歸學者發起的公益學術平臺

分享資訊，整合資源

交流學術，偶爾風月

在材料加工領域，鑄造工藝的最佳化一直是提升產品質量的關鍵。然而，傳統的經驗試錯法和實驗設計方法在最佳化多因素多水平的鑄造工藝時成本高昂，鑄造數值模擬技術難以建立工藝-效能之間定量關係。機器學習為工藝最佳化帶來新思路，但鑄造工藝資料稀缺、特徵擴充套件困難導致模型精度低，限制了其應用。

來自中北大學和北京科技大學材料基因工程高精尖創新中心的趙宇宏教授團隊，提出了一種新穎的小資料集機器學習耦合相場模擬的方法來最佳化鑄造工藝。該方法透過最近鄰搜尋來改進拉丁超立方抽樣中的分層隨機抽樣，並將其與貝葉斯最佳化相結合。以水雷燃料艙隔板件的擠壓鑄造工藝最佳化為例，他們透過該方法利用僅25個樣本的超小資料集實現了工藝的最佳化。相對於隨機取樣、間隔取樣、正交設計和中心複合設計等傳統方法，該方法能更均勻覆蓋工藝引數空間，減少大約50%資料量，可在更少資料量下實現超過五因素五水平的工藝最佳化。他們利用該方法和6次實驗迭代確定了最優工藝引數，使隔板件的抗拉強度達到239.7MPa，延伸率達到12.2%，相比初始資料集的最優值分別提升了17.6%和18.4%。同時，採用夏普利加性解確定了壓力和溫度是影響隔板件強度和效能的關鍵引數，進一步結合溫度/壓力下凝固枝晶生長相場模擬揭示了壓力和溫度對鑄件凝固過程中微觀結構演變的影響機制，解決了機器學習模型物理可解釋性弱的問題。

Fig. 1 Flow chart for optimizing
squeeze casting process

Fig. 2 Process parameter and casting
properties distribution obtained through RLHS

Fig. 3 Performance of different
prediction models and distribution of initial training and test sets

該研究為鑄造行業提供了一種高效、低成本的工藝最佳化策略，有助於推動鑄造行業向智慧化、精準化發展。該文近期發表於npj Computational Materials11: 27 (2025)，英文標題與摘要如下，點選左下角“閱讀原文”可以自由獲取論文PDF。

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

Xiaolong Pei, Jiaqi Pei, Hua Hou & Yuhong Zhao

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, reduces the data volume by approximately 50%, and achieves process optimization beyond five factors-five levels with fewer data. Through RLHS and 6 iterations of experiments, the optimal process was identified, and the ultimate tensile strength (UTS) of partition casting under the optimal process reached 239.7 MPa, with an elongation (EL) of 12.2%, showing increases of 17.6% and 18.4% over the optimal values in the initial dataset. Finally, a combination of Shapley additive interpretation (SHAP) and phase-field method (PFM) of solidification dendrite growth was used to address the issue of weak physical interpretability in ML models.