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Updated: Feb 28, 2026

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在FDM制造的PLA组件中进行强大的断裂能量预测的Metaheuristic驱动的集体学习

Volkan Ates¹, Mehmet Eker², Ramazan Gungunes³

¹Department of Computer Engineering, Tarsus University, Mersin 33400, Turkey.

|February 27, 2026

概括

这项研究改善了沉积模型 (FDM) 打印的聚乳酸 (PLA) 部件的冲击性预测. 一个机器学习组合在预测断裂能量的准确性方面取得了37.3%的改进.

科学领域:

材料科学材料科学材料科学
制造业工程制造工程
聚合物科学聚合物科学

背景情况:

增材制造 (AM),特别是沉积建模 (FDM),提供了复杂几何形状的多功能制造.
聚乳酸 (PLA) 是一个受欢迎的可生物降解聚合物,因为它的刚性和可加工性.
用FDM打印的PLA表现出脆性和异构性,挑战其结构可靠性和断裂力学.

研究的目的:

通过使用冲击测试来评估FDM加工的PLA的冲击性.
通过Taguchi设计实验 (DoE) 调查制造参数对冲击行为和断裂能量的影响.
通过使用先进的机器学习框架,开发对冲击性的高准确性预测模型.

主要方法:

在FDM加工的PLA Izod标本上进行冲击试验.
塔古奇L27直角阵列用于设计实验 (DOE) 来研究制造参数.
一个两阶段的机器学习框架,涉及数学公式与自然启发的优化算法 (GWO,SMA,GSA,FPA,KH) 的混合,以及元启发式组合集成.

主要成果:

塔古奇能源部确定了影响冲击行为和断裂能量的关键制造参数.
混合机器学习组合显著提高了对冲击性的预测准确性.
平均绝对百分比误差 (MAPE) 达到5.0847%,比最好的单个模型相对改善37.3%.

结论:

该研究成功地优化了FDM打印的PLA对冲击性的预测.
先进的机器学习组合为了解和预测AM材料的机械性能提供了强大的工具.
这些发现对于在苛刻的应用中提高FDM打印PLA的结构可靠性至关重要.

关键词:

塔古奇的方法塔古奇的方法组合学习组合学习断裂能量断裂能量化沉积物的建模.灰狼优化器灰狼优化器机器学习是机器学习.优化的优化优化优化.多乳酸多乳酸是什么意思

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