Industrial revolutions have historically disrupted manufacturing by introducing automation into production. Increasing automation reshapes the role of the human worker. Advances in robotics and artificial intelligence open new frontiers of human-machine collaboration. Such collaboration can be realized considering two sub-fields of artificial intelligence: active learning and explainable artificial intelligence. Active learning aims to devise strategies that help obtain data that allows machine learning algorithms to learn better. On the other hand, explainable artificial intelligence aims to make the machine learning models intelligible to the human person. The present work first describes Industry 5.0, human-machine collaboration, and state-of-the-art regarding quality inspection, emphasizing visual inspection. Then it outlines how human-machine collaboration could be realized and enhanced in visual inspection. Finally, some of the results obtained in the EU H2020 STAR project regarding visual inspection are shared, considering artificial intelligence, human digital twins, and cybersecurity.

翻译：工业革命历来通过将自动化引入生产来颠覆制造业。日益增长的自动化重塑了人类工人的角色。机器人技术与人工智能的进步开辟了人机协作的新领域。这种协作可通过人工智能的两个子领域实现：主动学习与可解释人工智能。主动学习旨在制定策略，帮助获取能更好训练机器学习算法的数据；而可解释人工智能则致力于使机器学习模型对人类可理解。本文首先阐述了工业5.0、人机协作以及质量检测（特别是视觉检测）领域的现有技术现状。随后概述了如何在视觉检测中实现并增强人机协作。最后，结合人工智能、人类数字孪生与网络安全，分享了欧盟H2020 STAR项目中关于视觉检测的部分研究成果。