Industrial 3D Printing Quality Control System Based on Machine Vision

The work is devoted to improving the efficiency of the 3D printing quality control using machine learning and network technologies. Its relevance stems from the inherent limitations of quality control systems in additive manufacturing and the need to refine machine vision methods, models, and algorithms to automate and improve the control of the additive manufacturing process for technical products. Significant practical results have been obtained, demonstrating the effectiveness of a machine learning-based web service for 3D printing quality control. According to the results of calculating metrics for assessing the effectiveness of the proposed solution to classification and detection problems within the framework of a computational experiment, the F1-measure indicator is 0.94.