The development of computer vision and in-situ monitoring using visual sensors allows the collection of large datasets from the additive manufacturing (AM) process. Such datasets could be used with machine learning techniques to improve the quality of AM. This paper examines two scenarios: first, using convolutional neural networks (CNNs) to accurately classify defects in an image dataset from AM and second, applying active learning techniques to the developed classification model. This allows the construction of a human-in-the-loop mechanism to reduce the size of the data required to train and generate training data.

翻译：计算机视觉与视觉传感器原位监测技术的发展，使得从增材制造（AM）过程中能够采集大规模数据集。此类数据集可结合机器学习技术用于提升增材制造质量。本文探讨两种场景：其一，利用卷积神经网络（CNN）对增材制造图像数据集中的缺陷进行精确分类；其二，将主动学习技术应用于所开发的分类模型。通过构建人机协同机制，可显著缩减模型训练与训练数据生成所需的数据规模。