对3D打印复合材料过程参数的效果调查 拉伸性能通过注意力机制增强的多模态卷积神经网络来提高
Zeyuan Gao1, Zhibin Han2, Yaoming Fu1
1College of Aviation Engineering, Civil Aviation Flight University of China, Chengdu 641400, China.
Polymers
|January 28, 2026
概括
本研究引入了注意力增强的多模态卷积神经网络 (ATT-MM-CNN),用于预测融沉积模型 (FDM) 打印复合材料的拉伸性能. 该模型准确预测机械性能,预测的提升超过17.3%.
科学领域:
- 添加剂制造 添加剂制造 添加剂制造
- 材料科学 材料科学 材料科学
- 人工智能的人工智能
背景情况:
- 沉积建模 (FDM) 是聚合物和复合材料的关键增材制造方法.
- 预测FDM零件的机械性能是具有挑战性的,因为有多个影响印刷参数.
- 碳纤维增强聚乳酸 (PLA-CF) 复合材料越来越多地用于FDM应用.
研究的目的:
- 开发一种先进的深度学习模型,用于预测FDM打印的PLA-CF复合材料的拉伸性能.
- 研究关键打印参数对这些复合材料机械性能的影响.
- 建立一个可靠的框架,以优化FDM过程参数,以提高材料性能.
主要方法:
- 设计和实施了一种增强注意力的多模式卷积神经网络 (ATT-MM-CNN).
- 使用四个打印参数 (层厚度,喷嘴温度,材料流速,打印速度) 的256种组合和拉伸试验数据创建了一个多式数据集.
- 该模型集成了多模式特征和注意力机制,以捕捉复杂的参数-性能关系.
主要成果:
- 在所有评估指标 (准确性,精度,回忆,F1得分) 上,ATT-MM-CNN实现了高性能,超过0.95.
- 与基线模型相比,预测准确度明显提高了至少17.3%.
- 该模型有效地学习了印刷参数和拉伸性能之间的非线性关系.
结论:
- ATT-MM-CNN提供了一种有效可靠的方法来预测FDM打印复合材料的拉伸性能.
- 开发的框架促进了基于FDM的增材制造过程参数优化.
- 这项研究强调了人工智能在增强增材制造中复合材料属性的可预测性和控制方面的潜力.
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