Automated fault detection for additive manufacturing using vibration sensors. Issue 5 (4th May 2021)

• Record Type: Journal Article
• Authors: Scheffel, Roberto Milton; Fröhlich, Antônio Augusto; Silvestri, Marco
• Is Part Of: International journal of computer integrated manufacturing. Volume 34:Issue 5(2021)
• Page Start: 500
• Page End: 514
• Publication Date: 2021-05-04
• Subjects: AI in manufacturing systems -- CIM architecture -- neural network applications -- automated fault detection -- additive manufacturing; Computer integrated manufacturing systems -- Periodicals; 670.427
• DOI: 10.1080/0951192X.2021.1901316
• Languages: English
• ISSNs: 0951-192X

Abstract

ABSTRACT: 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 pre-processing and without domain-specific knowledge for model building.