Vision-based real-time layer error quantification for additive manufacturing

Quality assurance of Additive Manufacturing (AM) products has become an important issue as the AM technology is extending its application throughout the industry. However, with no definite measure to quantify the error of the product and monitor the manufacturing process, many attempts are made to propose an effective monitoring system for the quality assurance of AM products. In this research, a novel approach for quantifying the error in real-time is presented through a closed-loop vision-based tracking method. As conventional AM processes are open-loop processes, we focus on the implementation of real-time error quantification of the products through the utilization of a closed-loop process. Three test models are designed for the experiment, and the tracking data from the camera will be compared with the G-code of the product to evaluate the geometrical errors. The results obtained from the camera analysis will then be validated through comparison of the results obtained from a 3D scanner.