一种基于机器学习的理论方法,用于估计在成型过程中LLDPE的物理性质
1School of Mechanical and Electrical Engineering, Xi'an Traffic Engineering Institute, Xi'an, 710300, Shanxi, China. zhongfan@xjy.edu.cn.
Scientific reports
|September 27, 2024
概括
预测线性聚乙烯的机械性能,如拉力强度,冲击强度和弹性强度,是使用先进的回归模型进行优化. 鱼优化算法调整提高了材料设计的模型精度.
科学领域:
- 聚合物科学与工程 聚合物科学与工程
- 材料科学 材料科学 材料科学
- 计算材料科学科学 计算材料科学
背景情况:
- 准确预测聚合物机械性能对于材料设计和制造至关重要.
- 烤箱停留时间是影响线性聚乙烯机械特性的一个关键因素.
- 需要优化技术来提高计算模型的预测准确性.
研究的目的:
- 研究对线性聚乙烯的拉力强度,冲击强度和屈曲强度的预测.
- 用鱼优化算法 (WOA) 优化各种回归模型的性能评估.
- 根据烤箱停留时间确定最适合预测特定机械性能的模型.
主要方法:
- 使用了一个数据集,输入的是烤箱停留时间,输出是机械性能.
- 使用WOA应用和调整多层感知器,K-最近邻居 (KNN),支向量回归 (SVR),多项式回归 (PR) 和Theil-Sen回归.
- 使用平均绝对误差 (MAE),根平均平方误差 (RMSE) 和平均绝对相对偏差 (AARD) 评估模型性能.
主要成果:
- 鱼优化算法调整的多项式回归 (WOA-PR) 在预测拉力方面表现出卓越的性能.
- 鱼优化算法调整的K-最近邻居 (WOA-KNN) 显示出对冲击强度预测的最佳准确性和可靠性.
- 鱼优化算法调整的支向量回归 (WOA-SVR) 证明最有效地预测曲强度,最大限度地减少错误.
结论:
- 模型选择和超参数优化对于准确的聚合物性质预测至关重要.
- 优化的回归模型,特别是WOA-PR,WOA-KNN和WOA-SVR,为预测线性聚乙烯的特定机械性能提供了有效的解决方案.
- 这些发现支持了聚合物制造工艺和材料开发的进步,通过精确的属性预测.
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