Deep Learning-based 3D Printer Fault Diagnosis

Additive manufacturing, also familiar as 3D printing , is a manufacturing method based on material deposition and its preserving layer by layer which can be performed via various methods and materials . As it has many virtues compared to conventional manufacturing methods, the applications of the 3D printing have rapidly increased . The basic advantage of the 3D printing is the capability to build almost any geometric . On the other side, one of the main drawbacks is its lesser dimensional accuracy, as the precision of the 3D printing is affected by various factors which have limited its continuous development . One of the significant factor is the mechanical transmission of the 3D printer. Hence it is needed to track the transmission condition of the 3D printer despite being possessing precision components . 3D printing is a rapid prototyping with broad application prospects additive manufacturing technology, which is based on digital model files. The layer printing method is used to manufacture objects or products . According to the transmission structure, common 3D printers can be divided into parallel 3D printers and serial 3D printers . There are two types of printers, and the tandem mechanism can provide a large motion space, and the parallel mechanism 3D printer has a compact, the advantages of light weight, good rigidity and high transmission precision . No matter what kind of transmission mechanism 3D printer is used, after a period of time, its transmission accuracy may deteriorate. Parallel arm take the 3D printer as an example, the universal ball bearing and synchronous transmission on the transmission chain moving belts may cause transmission clearance due to wear or aging, thus affect the quality of the final printed product. Although it affects 3D printing products.