机器学习在复合材料领域中的应用进展

Progress in Application of Machine Learning In Field of Composite Materials

复合材料因其密度低、比模量高、比强度高等优势成为汽车轻量化的重要材料。但因复合材料所涉及材料参数相对庞杂,成本高、周期长的传统复合材料研究方法已无法适应目前复合材料的发展趋势。近年来,基于数据挖掘的机器学习具有高效、高精等优势,为解决上述复合材料领域现存困境提供了新思路。通过阐述机器学习技术的基本原理、应用流程以及典型算法,总结其在复合材料领域的应用可行性。分析了机器学习在复合材料的微观结构表征、力学性能预测、复合材料优化设计、加工制造模拟速度四个方面的研究进展。分析表明,机器学习可用于复合材料研究领域,且具有较高的预测精度和可靠性。最后分析了机器学习在该领域的问题与挑战,为其未来研究方向和发展提出展望。

Composite material has become an important material for automotive lightweight due to its advantages of low density,high specific modulus and high specific strength.However,with the increase in the amount of material parameter data involved in composite materials and the aggravation of data complexity,the traditional research methods of composite materials with high cost and long cycle can no longer adapt to the current development trend of composite materials.In recent years,machine learning based on data mining has the advantages of high efficiency and high precision,which provides a new way to solve the existing difficulties in the field of composite materials.The basic principle,application flow and typical algorithm of machine learning technology were described,and the feasibility of its application in the field of composite materials was summarized.The research progress of machine learning in microstructure characterization,mechanical property prediction,optimal design of composite materials and simulation speed of machining and manufacturing were analyzed.The analysis show that machine learning can be used in the field of composite materials with high prediction accuracy and reliability.At the same time,the problems and challenges of machine learning in this field were analyzed,and the prospect of its future research direction and development were put forward.