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识别激光粉床融合添加剂制造模型中的不确定性
Felipe Lopez1, Paul Witherell2, Brandon Lane3
1Department of Mechanical Engineering, University of Texas at Austin, Austin, Texas 78712.
Journal of mechanical design (New York, N.Y. : 1990)
|March 25, 2024
概括
本研究解决了增材制造 (AM) 模型中的不确定性,特别是激光粉床融合 (L-PBF). 它确定了不确定性源,并提出了量化和减少不确定性源的方法,以改善零件合格和工艺控制.
科学领域:
- 材料科学与工程 材料科学与工程
- 计算机建模和模拟
- 制造过程 制造过程 制造过程
背景情况:
- 增材制造 (AM) 模型对于设计和工艺规划至关重要,但往往缺乏精度和准确性信息.
- 模型不确定性对于验证AM模型,合格部件和管理过程变异性至关重要.
- 激光粉床融合 (L-PBF) 是一个关键的AM技术,其中模型不确定性对结果产生重大影响.
研究的目的:
- 讨论L-PBF模型中不确定性的起源和传播.
- 介绍各种来源的不确定性量化的技术.
- 探索使用在线测量来减少预测不确定性的方法.
主要方法:
- 确定四个主要不确定性来源:建模假设,未知模拟参数,数值近似和校准数据测量误差.
- 简要介绍了为每个已识别的来源量化不确定性的技术.
- 开发和应用估计算法以减少在线测量的预测不确定性.
主要成果:
- 在L-PBF中单轨实验的模型不确定性的量化.
- 基于模拟的研究对在线估计对悬挂结构的影响.
- 展示了不确定性量化和减少技术,使用一个短暂的,随机的热模型来预测池宽度.
结论:
- 了解和量化L-PBF模型中的不确定性对于可靠的AM流程至关重要.
- 在线测量和估计算法可以有效地减少预测不确定性.
- 提出的概念为改善L-PBF的估计和控制提供了一条途径.
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