Dynamic condition monitoring for 3D printers by using error fusion of multiple sparse auto-encoders

Abstract

With the development of 3D printers, the healthy condition is becoming more and more crucial for the printing quality. In this research, anerror fusion of multiple sparse auto-encoders (EFMSAE) is developed to monitor the condition of the 3D printer dynamically. To this end, an attitude sensor which contained 9 channels is employed for collectingprinter condition data of 3-axial angular velocity, 3-axial vibratory acceleration and 3-axial magnetic field intensity, simultaneously. To make use of the information of multiple sensorsmounted on the moving platform of the printer,multiple sparse auto-encoders (SAEs)are employed for the deep learning of these data. To integrate these information for extracting ingredients incondition monitoring, square prediction error (SPE)is applied as an error fusion tool to fuse multiple SAEs. The value of SPEis used as an indicator to describe the operation status of the printer. Both simulated and delta 3D printer experiments were carried for evaluating the performance of the addressed method. The results show that the present EFMSAE is capable of effectively monitoringthe dynamic healthycondition for 3D printers.