一个混合数据驱动的元启发式框架,以优化增材制造过程中的格子结构的压力
Tao Zhang1, Uzair Sajjad2, Akash Sengupta3
1School of 3D Printing, Xinxiang University, Xinxiang 453003, China.
Micromachines
|October 28, 2023
概括
这项研究通过控制设计和工艺参数来优化增材制造 (AM) 格子结构,以实现所需的机械性能. 一个深度学习算法成功预测了各种格子拓中的特定应变值的最佳参数.
科学领域:
- 材料科学与工程 材料科学与工程
- 机械工程 机械工程
- 添加剂制造 添加剂制造 添加剂制造
背景情况:
- 优化增材制造 (AM) 格子结构的机械性能对于先进的应用至关重要.
- 控制设计和过程参数显著影响格子结构性能.
研究的目的:
- 开发一个框架,以优化AM格子结构中的应变.
- 确定最佳的设计和工艺参数,以实现特定的机械性能.
主要方法:
- 使用深度学习驱动的遗传元启发算法.
- 研究了使用多种材料 (塑料,金属,聚合物) 和AM技术 (SLA,MJF,FDM,DMLS,SLM) 制造的各种格子拓 (海,蜂,凯尔文).
- 控制的参数包括应力,单元细胞大小,高度,宽度和相对密度.
主要成果:
- 通过优化不同格子结构的输入参数,成功实现了目标应变值.
- 提供了特定的最佳参数集,用于在海结构中实现2.8 × 10-6 mm/mm的应变.
- 证明了框架通过参数控制来定制机械性能的能力.
结论:
- 开发的深度学习框架有效地优化了AM晶格结构中的压力.
- 这种方法可以设计具有可预测和所需的机械质量的AM格子结构.
- 为工程师和研究人员在材料设计和制造方面提供了宝贵的工具.
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