对用于预测和优化层纤维增强聚合物复合材料的机械行为的机器学习实施的审查
Sherif Samy Sorour1, Chahinaz Abdelrahman Saleh2, Mostafa Shazly1
1Department of Mechanical Engineering, The British University in Egypt, El Sherouk City, Cairo, 11837, Egypt.
Heliyon
|July 23, 2024
概括
机器学习 (ML) 增强了复合材料的纤维增强聚合物 (FRP) 分析. 机器学习模型为FRP材料提供了准确,具有成本效益的优化和设计见解.
科学领域:
- 材料科学 材料科学 材料科学
- 机械工程 机械工程
- 计算科学 计算科学
背景情况:
- 纤维增强聚合物 (FRP) 是各种工业中广泛使用的先进复合材料.
- 分析FRP的机械行为对于其有效的设计和应用至关重要.
- 传统的分析方法可能是计算密集型和耗时的.
研究的目的:
- 审查机器学习 (ML) 在分析FRP复合材料的机械行为中的应用.
- 提供ML算法及其在FRP设计和优化中的实现的概述.
- 突出ML在提高FRP分析的准确性和效率方面的潜力.
主要方法:
- 对最近使用ML进行FRP分析的研究进行了全面的文献综述.
- 分类和讨论各种ML算法及其适合于不同的FRP相关问题.
- 分析展示在FRP设计和优化中的ML应用的案例研究.
主要成果:
- 在分析FRP机械行为时,ML技术表现出高的预测准确性.
- 机器学习模型在培训阶段后显著降低了计算成本.
- 使用ML成功优化复合材料的机械性能已经被证明.
结论:
- ML是优化和深入分析层状FRP的强大工具.
- 选择合适的ML算法和神经网络架构对于成功实现至关重要.
- 需要进一步的研究才能充分利用ML在FRP复合材料领域的潜力.
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