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Published in:
The Methods and Approaches of Explainable Artificial Intelligence Read chapter Read first chapter

2021 | OriginalPaper | Chapter

Dataset for Anomalies Detection in 3D Printing

Authors : Tomasz Szydlo, Joanna Sendorek, Mateusz Windak, Robert Brzoza-Woch

Published in: Computational Science – ICCS 2021

Publisher: Springer International Publishing

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Abstract

Nowadays, the Internet of Things plays a significant role in many domains. Especially, Industry 4.0 is making significant 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. This paper presents and describes a set of data streams coming from a 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. The resulting dataset can therefore be used for anomalies detection research.

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previous chapter Renewable Energy-Aware Heuristic Algorithms for Edge Server Selection for Stream Data Processing
Footnotes
5
Additional sensors and devices are provided by the FogDevices platform. The video showing printing process is available online https://​youtu.​be/​SFBInVsVDgk.
 
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Metadata
Title
Dataset for Anomalies Detection in 3D Printing
Authors
Tomasz Szydlo
Joanna Sendorek
Mateusz Windak
Robert Brzoza-Woch
Copyright Year
2021
Book
Computational Science – ICCS 2021

Print ISBN: 978-3-030-77969-6

Electronic ISBN: 978-3-030-77970-2

Copyright Year: 2021

DOI
https://doi.org/10.1007/978-3-030-77970-2_50

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