热固复合材料的固化模拟和数据驱动的固化曲线预测
Chenchen Wu1,2, Ruming Zhang3, Pengyuan Zhao4
1School of Physics, Nanjing University of Science and Technology, Nanjing, 210094, China. wuchenchen@njust.edu.cn.
Scientific reports
|December 31, 2024
概括
这项研究开发了一种人工智能方法来预测复合材料固化. 一个基因算法优化的神经网络 (GA-BP) 在预测治愈度曲线方面表现出最高的准确性.
科学领域:
- 材料科学 材料科学 材料科学
- 化学工程是化学工程的重要组成部分.
- 人工智能的人工智能
背景情况:
- 模具对于航空航天和汽车热复合材料至关重要,影响成本和零件减少.
- 评估树脂固化需要治愈度曲线,但模拟需要准确的初始条件,并面临计算需求.
- 结合热传递和固化动力学模拟是耗时的,突出显示了对高效预测工具的需求.
研究的目的:
- 开发一种数据驱动的人工智能方法,用于预测成型复合材料的治愈度曲线.
- 为了比较不同的机器学习模型对治疗行为的预测准确度.
主要方法:
- 有限元模拟生成了温度时间和愈合时间度数据.
- 机器学习模型,包括支持向量回归 (SVR),逆向传播 (BP) 神经网络和基因算法优化BP (GA-BP) 网络,在模拟数据上进行了训练.
- 模型性能使用评估指数进行了验证.
主要成果:
- 针对特定的温度-时间概况,模拟的愈合度曲线与已公布的数据进行了验证.
- 与SVR和标准BP相比,GA-BP神经网络模型在预测治愈度曲线方面取得了最高的准确性.
- 验证指数证实了GA-BP模型的优越性能.
结论:
- 使用机器学习的数据驱动方法,特别是GA-BP网络,对于准确预测复合材料的愈度是有效的.
- 这种人工智能驱动的预测可以显著提高模拟复合材料固化过程的效率.
- 开发的模型为优化航空航天和汽车行业的制造工艺提供了一个有前途的解决方案.
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