Laser powder bed fusion (LPBF) is an additive manufacturing (AM) process widely adopted in multiple industries for various purposes. When LPBF is used for part fabrication, determining the manufacturability of a specific design is a challenge. Therefore, this study aimed to identify a printable design using a novel approach to predict the potential printing failures of a given design via the LPBF process. A voxel-based convolutional neural network (CNN) model is developed for analyzing the design aspect, and a neural network (NN) model is applied to the process aspect. The two models are then combined to predict the manufacturability of the given design in the selected LPBF process settings. The validation samples were selected randomly, and the results verified that the developed model can accurately predict the manufacturability of the specific design. However, the proposed model is restricted by the computational power and the number of training datasets and therefore requires further investigation in this regard.

激光粉末床熔合（LPBF）是一种增材制造（AM）工艺，在多种行业中广泛用于各种目的。当将LPBF用于零件制造时，确定特定设计的可制造性是一个挑战。因此，本研究旨在通过一种新颖的方法来确定可印刷的设计，以通过LPBF流程预测给定设计的潜在印刷失败。开发了基于体素的卷积神经网络（CNN）模型来分析设计方面，并将神经网络（NN）模型应用于过程方面。然后将这两个模型结合起来，以预测在选定的LPBF工艺设置中给定设计的可制造性。验证样本是随机选择的，结果证明，所开发的模型可以准确地预测特定设计的可制造性。但是，提出的模型受到计算能力和训练数据集数量的限制，因此需要在这方面进行进一步研究。