Defect monitoring in additive manufacturing through OpenCV-based remote video access

Abstract

Errors in additive manufacturing are a significant obstacle to its widespread adoption in various application. The developments of 3D printed parts are prone to various types of defects, which are difficult to monitor in real-time through human vision. The use of 3D printers has increased in diverse fields, leading to a significant rise in the need of continuous monitoring during the manufacturing process. Prompt action should be taken to stop or correct errors immediately to prevent a defective part. However, continuous monitoring of 3D printer through human vision is impractical for longer tasks and cannot be accurate. Human inspection is not infallible and could introduce its own errors. Given the multitude of application, continuous defect monitoring is extremely challenging, particularly for longer tasks. Based on these constraints a suitable remote monitoring system through open-source computer vision has been proposed. Incorporating the concept of Internet of things, the video of printing process could be viewed by a user or controller from a remote location enabling to monitor the printing process. With the use of Haar classifier in the program, the printing defects that occur in the 3D printed part could be identified immediately through contours drawn over the defective part. Depending upon the severity of defect indicated, the user could take respective action to control the defect formation. With Industry 4.0 in the implementation stages, this method of video monitoring of defects in 3D printers could be an effective method for widespread use of additive manufacturing systems in industries.