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《工程(英文)》 >> 2023年 第23卷 第4期 doi: 10.1016/j.eng.2022.09.015

基于内嵌物理信息深度学习模型的增材制造工艺参数及熔池尺寸预测

School of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing 210094, China

收稿日期: 2021-12-30 修回日期: 2022-08-23 录用日期: 2022-09-29 发布日期: 2023-02-16

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摘要

熔池特征对激光粉末床熔融(PBF)的打印质量有显著影响,打印参数和熔池尺寸的定量预测对LPBF中复杂过程的智能控制至关重要。然而由于高度非线性,打印参数和熔池尺寸的双向预测一直极具挑战。为了解决此问题,本工作融合典型实验、机理模型和深度学习研究激光PBF过程中关键参数和熔池特性的正向和逆向预测。实验提供基础数据,机理模型显著增强数据集,多层感知器(MLP)深度学习模型则根据实验和机理模型构建的数据集预测熔池尺寸和工艺参数。结果表明可以实现熔池尺寸和工艺参数的双向预测,最高预测准确率接近99.9%,平均预测准确率超过90.0%。此外,MLP模型的预测准确率与数据集的特征密切相关,即数据集的可学习性对预测准确率有至关重要的影响。通过机理模型增强数据集后的最高预测精度为97.3%,而仅使用实验数据集时的最高预测精度只有68.3%。MLP模型的预测准确率在很大程度上取决于数据集的质量。研究结果表明使用MLP进行复杂相关性的双向预测对于激光PBF是可行的,本工作为选定智能增材制造的工艺条件和结果提供了一个新颖而实用的框架。

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