Additive Manufacturing (AM) has emerged as a manufacturing process that allows the direct production of samples from digital models. To ensure that quality standards are met in all manufactured samples of a batch, X-ray computed tomography (X-CT) is often used combined with automated anomaly detection. For the latter, deep learning (DL) anomaly detection techniques are increasingly, as they can be trained to be robust to the material being analysed and resilient towards poor image quality. Unfortunately, most recent and popular DL models have been developed for 2D image processing, thereby disregarding valuable volumetric information. This study revisits recent supervised (UNet, UNet++, UNet 3+, MSS-UNet) and unsupervised (VAE, ceVAE, gmVAE, vqVAE) DL models for porosity analysis of AM samples from X-CT images and extends them to accept 3D input data with a 3D-patch pipeline for lower computational requirements, improved efficiency and generalisability. The supervised models were trained using the Focal Tversky loss to address class imbalance that arises from the low porosity in the training datasets. The output of the unsupervised models is post-processed to reduce misclassifications caused by their inability to adequately represent the object surface. The findings were cross-validated in a 5-fold fashion and include: a performance benchmark of the DL models, an evaluation of the post-processing algorithm, an evaluation of the effect of training supervised models with the output of unsupervised models. In a final performance benchmark on a test set with poor image quality, the best performing supervised model was MSS-UNet with an average precision of 0.808 $\pm$ 0.013, while the best unsupervised model was the post-processed ceVAE with 0.935 $\pm$ 0.001. The VAE/ceVAE models demonstrated superior capabilities, particularly when leveraging post-processing techniques.

翻译：增材制造作为一种制造工艺，能直接从数字模型生产样品。为确保批次中所有样品符合质量标准，常将X射线计算机断层扫描与自动异常检测相结合。在异常检测方面，深度学习技术因其可训练至对分析材料具有鲁棒性、对低质量图像具有弹性而日益受到青睐。然而，当前大多数流行的深度学习模型专为二维图像处理而开发，忽略了有价值的三维体积信息。本研究重新审视了近期用于X-CT图像增材制造样品孔隙率分析的监督模型（UNet、UNet++、UNet 3+、MSS-UNet）与无监督模型（VAE、ceVAE、gmVAE、vqVAE），并将其扩展至接受三维输入数据，采用三维块处理流程以降低计算需求、提升效率与泛化能力。监督模型使用Focal Tversky损失函数进行训练，以应对训练数据集中低孔隙率导致的类别不平衡问题。无监督模型的输出经后处理以减少因无法充分表征物体表面而产生的误分类。研究结果通过五折交叉验证，包括：深度学习模型的性能基准测试、后处理算法评估、以及用无监督模型输出训练监督模型的效果评估。在最终针对低质量图像测试集的性能基准测试中，表现最佳的监督模型为MSS-UNet，平均精度达0.808 ± 0.013；最佳无监督模型为经后处理的ceVAE，平均精度达0.935 ± 0.001。VAE/ceVAE模型展现出卓越能力，尤其在采用后处理技术时。