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使用合成数据和XGBoost,对PLA/SCG/复合材料的机械性能进行多目标优化

Atthaphon Ariyarit¹, Attasit Wiangkham², Phatthawit Siripaiboonsub¹

¹School of Mechanical Engineering, Institute of Engineering, Suranaree University of Technology Muang Nakhon Ratchasima 30000 Thailand.

|November 3, 2025

概括

这项研究优化了聚乳酸 (PLA) 复合材料与消耗的咖啡粉 (SCG) 和西兰,实现了增强的抗拉强度和硬度. 使用XGBoost和NSGA-II的数据驱动方法确定了可持续生物复合材料的最佳配方.

科学领域:

材料科学材料科学材料科学
聚合物科学聚合物科学
可持续材料可持续材料

背景情况:

聚乳酸 (PLA) 复合材料提供了可生物降解的替代品.
消耗的咖啡粉 (SCG) 是一种可持续的增强填充剂.
西兰合剂 (VTMS) 可以提高填充剂-矩阵的兼容性.

研究的目的:

使用数据驱动方法优化PLA/SCG复合材料的组成.
为了增强机械性能,特别是抗拉强度和Shore D硬度.
开发一种具有提高性能的可持续生物复合材料.

主要方法:

75个PLA/SCG/西兰复合样本的制造和测试.
开发一个多输出XGBoost回归模型用于属性预测.
使用动,高斯噪声和KDE来增强训练数据.
应用NSGA-II用于拉伸强度和硬度的多目标优化.

主要成果:

实现的抗拉强度在26.5-57.9 MPa之间,海岸D硬度为77.5-80.8.
XGBoost模型显示出高预测精度 (拉伸强度为R^2 = 0.884,硬度为R^2 = 0.908).
确定了帕雷托最佳成分,有利于更高的PLA含量和中等的SCG和西兰.
最佳的配方产生了53.33 MPa的抗拉强度和80.06 Shore D硬度.

结论:

结合的XGBoost-NSGA-II框架为优化生物复合材料性能提供了一个有效的策略.
这种数据驱动的方法尽量减少实验的努力,同时最大限度地提高所需的特性.
优化的PLA/SCG/复合材料是一个有前途的可持续材料解决方案.

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