Transmission Condition Monitoring of 3D Printers Based on the Echo State Network

Abstract

Three-dimensional printing quality is critically affected by the transmission condition of 3D printers. A low-cost technique based on the echo state network (ESN) is proposed for transmission condition monitoring of 3D printers. A low-cost attitude sensor installed on a 3D printer was first employed to collect transmission condition monitoring data. To solve the high-dimensional problem of attitude data, feature extraction approaches were subsequently performed. Based on the extracted features, the ESN was finally employed to monitor transmission faults of the 3D printer. Experimental results showed that the fault recognition accuracy of the 3D printer was obtained at 97.17% using the proposed approach. In addition, support vector machine (SVM), locality preserving projection support vector machine (LPPSVM), and principal component analysis support vector machine (PCASVM) were also used for comparison. The contrast results showed that the recognition accuracies of our method were higher and more stable than that of SVM, LPPSVM, and PCASVM when collecting raw data via the low-cost attitude sensor.

Process monitoring for material extrusion additive manufacturing: a state-of-the-art review

“…In addition to the processing of conventional filaments, some studies have examined manufacturing processes for continuous fibers [69,70], pastes [71], and pellets [60,62,63,72,73]. Publications addressing MEX machines with delta [74][75][76][77][78][79][80][81][82][83][84][85][86][87][88] and robot arm [58,72,73,[89][90][91][92] kinematics are exceptions to the considered Cartesian systems. Some monitoring systems have been published several times and sometimes, several systems have been described in one publication.…”

A Framework for Multivariate Statistical Quality Monitoring of Additive Manufacturing: Fused Filament Fabrication Process

“…Sensors are widely applied to monitor 3D printing processes. Another application of sensors in AM processes is transmission condition monitoring, such as that presented in [28,29]. Moreover, K.Gomathi et al [30] monitored the resulting vibration during the motion of the 3D printer.…”

Fusing convolutional generative adversarial encoders for 3D printer fault detection with only normal condition signals

“…With the same application, in [12] fault diagnosis was proposed by using Support Vector Machines (SVM) with signals obtained from an attitude sensor. In [13,14] the 3D printer condition is estimated by Echo State Networks (ESN), while [15] introduced a method improving an extreme learning machine through a modified swarm optimizer, and [16] presented the feature reinforcement for improving the 3D printer condition classification with a success rate in the normal condition of 93.6%. In addition, the comparison results reported 66.7%, 67.5%, 90.1%, 32.0% and 21.3% with SAE+Softmax, ESN, SAE+ESN, SAE+SVM, and SVM, respectively, highlighting the difficulty to correctly detect a fault even with powerful pattern recognition algorithms and supervised learning approachs.…”