Automatic defect detection for 3D printing processes, which shares many characteristics with change detection problems, is a vital step for quality control of 3D printed products. However, there are some critical challenges in the current state of practice. First, existing methods for computer vision-based process monitoring typically work well only under specific camera viewpoints and lighting situations, requiring expensive pre-processing, alignment, and camera setups. Second, many defect detection techniques are specific to pre-defined defect patterns and/or print schematics. In this work, we approach the defect detection problem using a novel Semi-Siamese deep learning model that directly compares a reference schematic of the desired print and a camera image of the achieved print. The model then solves an image segmentation problem, precisely identifying the locations of defects of different types with respect to the reference schematic. Our model is designed to enable comparison of heterogeneous images from different domains while being robust against perturbations in the imaging setup such as different camera angles and illumination. Crucially, we show that our simple architecture, which is easy to pre-train for enhanced performance on new datasets, outperforms more complex state-of-the-art approaches based on generative adversarial networks and transformers. Using our model, defect localization predictions can be made in less than half a second per layer using a standard MacBook Pro while achieving an F1-score of more than 0.9, demonstrating the efficacy of using our method for in-situ defect detection in 3D printing.

翻译：3D打印过程的自动缺陷检测与变化检测问题具有诸多共同特征，是确保3D打印产品质量的关键环节。然而，当前实践仍面临一些严峻挑战：首先，现有基于计算机视觉的过程监测方法通常仅适用于特定相机视角和光照条件，需要昂贵的预处理、配准和相机设置；其次，许多缺陷检测技术局限于预定义的缺陷模式和/或打印示意图。本研究提出一种新型半孪生深度学习模型，通过直接比较期望打印的参考示意图与实际打印的相机图像来解决缺陷检测问题。该模型通过图像分割任务，精确识别不同类型缺陷相对于参考示意图的位置。我们的模型设计能够实现跨域异质图像比较，同时对相机角度、光照等成像设置中的扰动具有鲁棒性。关键在于，这种简单架构易于预训练以提升新数据集上的性能，且优于基于生成对抗网络和Transformer的复杂先进方法。使用我们的模型，在标准MacBook Pro上每层缺陷定位预测耗时不到0.5秒，同时F1分数超过0.9，证明了该方法在3D打印原位缺陷检测中的有效性。