文章信息/Info

Title:

Applications of Machine Learning in Lightweight Alloy Research

作者:

張思雨；李日

河北工業大學材料科學與工程學院，天津300401

Author(s):

Siyu Zhang1；Ri Li

School of Materials Science and Engineering, Hebei University of Technology, Tianjin 300130, China

關鍵詞:

機器學習; 輕質合金; 性能預測; 合金設計; 工藝優化

Keywords:

Machine Learning;  Lightweight Alloys;  Performance Prediction;  Alloy Design;  Process Optimization

文獻標志碼:

A

摘要:

輕質合金以其低密度和高強度特性在航空航天、汽車、電子和建筑等領域具有重要應用。然而，傳統的基于經驗的“試錯法”和基于理論的模擬計算方法，需要進行大量實驗周期長、成本高，難以滿足現代輕質合金的發展需求。隨著人工智能和數據驅動技術的迅猛發展，機器學習作為目前人工智能領域應用最廣泛、發展最快的分支，已廣泛應用于材料科學，顯著加速了新材料的發現和優化。本文對機器學習計算在輕質合金研究中的應用進展進行了綜述，介紹了機器學習在材料研究中的工作流程，闡述了機器學習在輕質合金性能預測、合金設計以及工藝優化方面的研究進展及應用實例。最后，對當前機器學習在輕質合金領域的研究中面臨的挑戰進行了總結并對其發展前景進行了展望。

Abstract:

Lightweight alloys, with their low density and high strength characteristics, hold significant applications in aerospace, automotive, electronics, and construction fields. However, traditional experience-based "trial and error" methods and theoretical simulation methods require a large number of experiments, which are time-consuming and costly, making it difficult to meet the demands of modern lightweight alloy development. With the rapid development of artificial intelligence and data-driven technologies, machine learning, as the most widely used and fastest-growing branch of artificial intelligence, has been extensively applied in materials science, significantly accelerating the discovery and optimization of new materials. This paper reviews the progress of machine learning calculations in the research of lightweight alloys, introduces the workflow of machine learning in materials research, and elucidates the research progress and application examples of machine learning in predicting the properties of lightweight alloys, alloy design, and process optimization. Finally, the challenges faced by current machine learning research in the field of lightweight alloys are summarized, and the development prospects are discussed.