Detection of Printing Errors in 3D Printers Using Artificial Intelligence and Image Processing Methods

This article aims to employ artificial intelligence and image processing methods for the detection of print errors in three-dimensional (3D) printers. 3D printers represent a technology that offers various advantages; however, errors may occur during or after the printing process. These errors can impact the print quality, reliability, and functionality. Therefore, it is crucial to detect and prevent printing errors. In this research, image processing and artificial intelligence methods will be utilized to automatically identify, classify, and measure printing errors. These methods will take input in the form of images of the printing process or prints and provide output indicating the presence, type, size, and location of printing errors. These approaches have the potential to facilitate, expedite, and reduce the cost of detecting printing errors. The scope of this research is the application of artificial intelligence and image processing methods for the detection of print errors in 3D printers. The limitations of this research include considerations such as the performance, complexity, flexibility, compatibility, reliability, and validity of the employed methods.