Development and comparison of machine-learning algorithms for anomaly detection in 3D printing using vibration data

Abstract

3D printing is an emerging technology that converts digital models directly into physical objects. However, abnormal vibrations during the 3D printing process significantly affect the product quality, and also lead to possible failures of the printer components. This paper aims at developing machine-learning algorithms for anomaly detection or abnormal behavior of a 3D printer using vibration data. The proposed algorithms utilize vibration data from a sensor mounted on the printer. Data are then trained and validated developing four machine-learning algorithms to detect anomalies due to the structural or mechanical defects of the printer. Performances of the proposed four algorithms were evaluated and compared. It was found that the proposed long short-term memory (LSTM) algorithm has the best accuracy of 97.17% as compared to other algorithms. The novelty of the present work lies in detecting anomalies with high accuracy due to structural or mechanical faults in 3D printers using a low-cost sensor. The significance of the current work lies in its ability to achieve error-free 3D printing, resulting in less material waste, reduced human intervention and costs, and improved product quality by detecting potential anomalies during printing. The proposed algorithm terminates the printing if any anomaly is detected.