A cyber-physical production system for autonomous part quality control in polymer additive manufacturing material extrusion process

Miguel Castillo, Roberto Monroy and Rafiq Ahmad ()
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Miguel Castillo: University of Alberta
Roberto Monroy: University of Alberta
Rafiq Ahmad: University of Alberta

Journal of Intelligent Manufacturing, 2024, vol. 35, issue 8, No 4, 3655-3679

Abstract: Abstract This paper introduces a successful implementation of a Cyber-Physical Production System (CPPS) for large-format 3D printing, employing the 5C framework and Internet of Things (IoT) technology. The CPPS focuses on achieving autonomous part quality control by monitoring three critical categories: the thermal behavior of the material during printing deposition, faulty detection of contour's parts being produced, and machine integrity based on component performance. This study reveals that current temperature data on 3D printers does not accurately reflect the physical part deposition temperature by an average offset of 30%. Real-time thermal readings demonstrate potential for accurate monitoring and control of the printing process. Tests validate the CPPS’s efficacy in detecting faults in real-time, significantly enhancing overall part quality production by an accuracy detection of 99.7%. Integration of different cameras, image processing, and machine learning algorithms facilitates fault detection and self-awareness of printed parts, providing insights into the mechanical condition of the printer. The combination of machine learning and image processing reduces the need for continuous operator intervention, optimizing production processes and minimizing losses. In conclusion, the implemented CPPS offers a robust solution for achieving autonomous part quality control in large-format 3D printing, showcasing advancements in real-time monitoring, fault detection, and overall improvement in the additive manufacturing process for large scale production implementation. Graphical abstract

Keywords: FDM; Industry 4.0; Computer vision; IoT; Artificial intelligence; Mechanical properties; Thermal properties (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s10845-024-02389-0

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