The laser powder bed fusion (LPBF) process has the ability to manufacture intricate geometries. However, the fabrication of complex geometries using the LPBF process for industrial applications remains challenging in terms of achieving consistent microstructure and mechanical properties. Experimental investigations show that in addition to process parameters, geometrical factors such as shape and size of parts impact the thermal history, microstructure, defect structure, and thus mechanical and fatigue properties. As a result, the standardization, qualification, and certification of additively manufactured (AM-ed) parts encounters substantial obstacles posed by the obscure impact of design parameters on component quality. In this paper, an artificial intelligence framework is proposed to predict the melt pool depth of the additively manufactured parts considering the geometrical information of the parts. More specifically, a deep Multilayer Perceptron and Convolutional Neural Network (MLP-CNN) framework is designed to predict melt pool depth for any part geometries when printing metallic parts via LPBF using metadata (i.e., numerical and image data). Quite often the melt pool depth generated during the AM processes is employed as a surrogate for thermal, defect- and micro-structural signatures. In melt pool depth prediction of complex geometries in LPBF, a digital twin environment using a finite element model (FEM) is developed and validated using experimental melt pool measurements for different geometries within multi-tracks and layers. The FEM was in agreement with experimentation with an error of less than 15%. Through the process simulation, a large dataset of melt pool depths was obtained for various part geometries and different process parameters. Next, the MLP-CNN framework was established to identify the parallel impact of part design and process parameters on melt pool depths. To enhance the performance and robustness of this model, data augmentation has been implemented by rotating and transferring geometries to artificially expand the dataset. Data augmentation helps to mitigate overfitting and promote better generalization, especially in the context of limited training data. After training, the proposed model is found to give accurate melt pool prediction even for new geometries not considered during training. The fusion of CNN and MLP has led the melt pool predictions for unseen geometries with an accuracy of 95%.

中文翻译：

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使用深度 MLP-CNN 和元数据集成对激光粉末床熔合中的熔池深度进行几何信息预测建模

激光粉末床熔融（LPBF）工艺能够制造复杂的几何形状。然而，在工业应用中使用 LPBF 工艺制造复杂几何形状在实现一致的微观结构和机械性能方面仍然具有挑战性。实验研究表明，除了工艺参数外，零件的形状和尺寸等几何因素也会影响热历史、微观结构、缺陷结构，从而影响机械和疲劳性能。因此，增材制造 (AM-ed) 零件的标准化、鉴定和认证遇到了设计参数对零件质量的模糊影响所带来的重大障碍。本文提出了一种人工智能框架，考虑零件的几何信息来预测增材制造零件的熔池深度。更具体地说，深度多层感知器和卷积神经网络 (MLP-CNN) 框架旨在使用元数据（即数字和图像数据）通过 LPBF 打印金属零件时预测任何零件几何形状的熔池深度。增材制造工艺过程中产生的熔池深度经常被用作热、缺陷和微观结构特征的替代指标。在 LPBF 中复杂几何形状的熔池深度预测中，使用有限元模型 (FEM) 的数字孪生环境是通过对多轨和层内不同几何形状的实验熔池测量来开发和验证的。有限元法与实验结果一致，误差小于 15%。通过工艺模拟，获得了各种零件几何形状和不同工艺参数的熔池深度的大型数据集。接下来，建立了 MLP-CNN 框架来识别零件设计和工艺参数对熔池深度的并行影响。为了增强该模型的性能和鲁棒性，通过旋转和传输几何图形来人为扩展数据集来实现数据增强。数据增强有助于减轻过度拟合并促进更好的泛化，特别是在训练数据有限的情况下。训练后，发现所提出的模型即使对于训练期间未考虑的新几何形状也能提供准确的熔池预测。 CNN 和 MLP 的融合引领了对未见过几何形状的熔池预测，准确率达到 95%。