Enhancing additive manufacturing with computer vision: a comprehensive review

As an all-in-one process that blended design, materials, and manufacturing, additive manufacturing (AM) adopted a bottom-up stacking approach, thereby avoiding excessive infrastructure and complex production steps. However, the different process stages of AM were influenced by multi-scale factors such as material characteristics, parameter settings, and process stability, resulting in uncertainty. Therefore, the effective containment and accurate evaluation of automated production processes were crucial. With the rapid development and widespread application of artificial intelligence (AI) technologies in the Industry 4.0 era, computer vision (CV) techniques, as an important component, could achieve multi-level understanding of the real world through traditional machine learning (ML) and efficient deep learning (DL) approaches based on information-dense cross-media data such as images, videos, and three-dimensional models. Hence, the organic integration of CV and AM was of great significance for the virtual simulation, global perception, real-time monitoring, and quality measurement of the machining processes. According to the chronological order and logical relationship of manufacturing engineering, this paper organized and summarized the application of CV techniques in material characteristics perception and stacking process simulation before the implementation of projects, in situ inspection, and law exploration during the manufacturing processes, as well as product evaluation and defect detection after processing, which demonstrated its powerful performance and great potential in the intelligence and integration of AM.