不同的电线弧增材制造组件的温度演变的可转移性 通过机器学习制造的组件
Håvard Mo Fagersand1, David Morin1, Kjell Magne Mathisen1
1Department of Structural Engineering, Norwegian University of Science and Technology (NTNU), 7491 Trondheim, Norway.
Materials (Basel, Switzerland)
|April 9, 2024
概括
机器学习模型预测了线弧增材制造 (WAAM) 中的温度历史. 这种数据驱动的方法通过将缺陷和残余应力降至最低,以改善零件生产,从而优化WAAM流程.
科学领域:
- 材料科学与工程 材料科学与工程
- 制造过程 制造过程 制造过程
- 计算建模 计算建模
背景情况:
- 电弧增材制造 (WAAM) 具有重要的工业潜力,但需要优化以减轻温度梯度引起的应力和缺陷.
- 实时流程控制对于一致的WAAM结果至关重要,需要数据驱动的预测方法.
研究的目的:
- 开发和评估机器学习 (ML) 模型,用于预测WAAM生产的部件的温度历史.
- 评估ML模型在不同的几何形状和工艺参数上的可转移性,包括条条长,沉积层和热源速度.
主要方法:
- 利用有限元 (FE) 模拟来生成ML模型的训练和测试数据集.
- 开发了多层感知子 (MLP) 模型,以预测WAAM过程中的温度演变.
- 在不同的模拟场景中使用平均绝对百分比误差 (MAPE) 评估模型性能.
主要成果:
- 基线MLP模型在培训和测试数据方面实现了高精度 (MAPE<0.7%).
- 模型性能表现出良好的可转移性,增加了条形长度或层数 (MAPE < 3.22%),尽管变化性增加.
- 预测不同扫描速度的温度历史结果的准确性较低,一些模型显示更高的MAPE (高达14.91%).
结论:
- 简单的MLP模型证明了WAAM的有效温度历史预测,突出了数据驱动过程优化的潜力.
- 该研究证实了基于ML的温度预测在WAAM中的可转移性,为控制残余应力和微观结构缺陷提供了一条途径.
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