Dataset for anomalies detection in 3D printing

Nowadays, Internet of Things plays a significant role in many domains. Especially, Industry 4.0 is making a great usage of concepts like smart sensors and big data analysis. IoT devices are commonly used to monitor industry machines and detect anomalies in their work. In this paper we present and describe a set of data streams coming from working 3D printer. Among others, it contains accelerometer data of printer head, intrusion power and temperatures of the printer elements. In order to gain data we lead to several printing malfunctions applied to the 3D model. Resulting dataset can therefore be used for anomalies detection research.

[1]  Yuma Koizumi,et al.  Unsupervised Detection of Anomalous Sound Based on Deep Learning and the Neyman–Pearson Lemma , 2018, IEEE/ACM Transactions on Audio, Speech, and Language Processing.

[2]  Jing Yang,et al.  3D Printer Optical Detection System Based On DLP Projection Technology , 2019, 2019 Chinese Automation Congress (CAC).

[3]  Laurent Itti,et al.  Inertial Machine Monitoring System for Automated Failure Detection , 2018, 2018 IEEE International Conference on Robotics and Automation (ICRA).

[4]  Dieter Roller,et al.  Vision based error detection for 3D printing processes , 2016 .

[5]  Robert Brzoza-Woch,et al.  The FogDevices platform - a comprehensive hardware solution for IoT applications , 2019 .

[6]  Ryan B. Wicker,et al.  A study to detect a material deposition status in fused deposition modeling technology , 2015, 2015 IEEE International Conference on Advanced Intelligent Mechatronics (AIM).

[7]  Christian Beecks,et al.  Unsupervised Anomaly Detection in Production Lines , 2018, ML4CPS.

[8]  W. Marsden.  I and J , 2012 .