Automated Fault Detection for Additive Manufacturing using Vibration Sensors

Scheffel, Roberto and Frölich, Antonio Augusto and Silvestri, Marco (2021) Automated Fault Detection for Additive Manufacturing using Vibration Sensors. International Journal of Computer Integrated Manufacturing, 34 (5). pp. 500-514. ISSN 1362-3052

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Online process control is a crucial task in modern production systems that use digital twin technology. The data acquisition from machines must provide reliable and on-the-fly data, reflecting the exact status of the ongoing process. This work presents an architecture to acquire data for an Additive Manufacturing (3D printer) process, using a set of consolidated Internet of Things (IoT) technologies to collect, verify and store these data in a trustful and secure way. The need for online monitoring and fault detection is addressed by the development of a classifier using Convolutional Neural Networks. This deep learning approach, using temporally aligned vibration data provided by the underlying architecture, allows raw data processing to detect patterns without signal preprocessing and without domain-specific knowledge for model building.

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