基于机器学习的含LPSO相镁合金腐蚀

Corrosion of magnesium alloy containing LPSO phase based on machine learning

含LPSO相镁合金具备优异的综合力学性能,在轨道交通、航空航天、生物医疗等领域具有广泛的应用前景,然而其易腐蚀的特性极大制约了该类合金的广泛应用。本文聚焦机器学习辅助研究合金腐蚀这一新兴领域,首先重点概述了机器学习在合金腐蚀研究等领域的应用进展,以期通过机器学习方法构建含LPSO相镁合金腐蚀预测模型,阐明LPSO相腐蚀机理;其次系统总结了镁合金中LPSO相含量、形状、结构、分布等显微组织对合金腐蚀性能的影响,为建立准确可靠的腐蚀模型提供借鉴与理论指导。最后,讨论了机器学习在含LPSO相镁合金腐蚀领域的发展机遇和挑战,展望了其发展前景。

Magnesium alloy containing LPSO phase has excellent comprehensive mechanical properties and has a wide application prospect in rail transit,aerospace,biomedical and other fields,but its corrosive properties greatly restrict the wide application of this type of alloy.This paper focused on the emerging field of machine learningassisted research on alloy corrosion,and first summarized the application progress of machine learning in the fields of alloy corrosion research,in order to construct a corrosion prediction model of magnesium alloy containing LPSO phase through machine learning methods and clarified the phase corrosion mechanism of LPSO.Secondly,the influence of LPSO phase content,shape,structure and distribution on the corrosion properties of the alloy was systematically summarized,which provided reference and theoretical guidance for establishing an accurate and reliable corrosion model.Finally,the development opportunities and challenges of machine learning in the field of corrosion of magnesium alloy containing LPSO phase were discussed,and the development prospects were prospected.