SYMPOSIA PAPER Published: 01 December 2022
STP164420210129

AIoT for Additive Manufacturing—Novel Edge and Fog AI Strategies and Architectures Applicable to Real-Time In Situ Monitoring

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In situ monitoring is an essential technique required to rationalize the use of expensive additive manufacturing (AM) by interrupting and correcting defects when the material is deposited rather than having to detect and correct them by postprocessing after the fact. Implementing in situ monitoring can be difficult for several reasons, all of which are linked to the feasibility of detecting manufacturing issues in real time so that corrective actions can be taken as and when required. Among these essential reasons, one could list the following: form factor, energy consumption, heat dissipation of the in situ equipment within the AM complete installation, the available network connectivity allowing for conventional processing approaches such as cloud computing, and the level of performance required to monitor, analyze, and take appropriate actions (e.g., alerting, interrupting the process) in real time. AIoT is an acronym denominating the convergence between AI techniques and IoT hardware, including sensors, computation, and automation. It refers to a panoply of new-generation devices and solutions that can provide novel approaches for tackling AM in situ monitoring, whether they are implemented entirely on one of several IoT components embedded in the AM equipment or in conjunction with computing power locally adjoined (on premises) to the AIoT capabilities within the AM equipment or even with remote computing power (either edge/fog or cloud computing, thus making the AIoT-based solution a hybrid solution). We consider the feasibility of all three options by illustrating and assessing the feasibility, with an adequate experimental protocol, of providing AIoT solutions capable of supporting the stringent in situ monitoring requirements for AM.

Author Information

Farret, Jérémie
Inmind Technologies Inc., Montreal, CA
Baghdadi, Niloofar
Inmind Technologies Inc., Montreal, CA
Ranjbar, Shahrzad, Amin
Inmind Technologies Inc., Montreal, CA
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Pages: 101–112
DOI: 10.1520/STP164420210129
ISBN-EB: 978-0-8031-7736-9
ISBN-13: 978-0-8031-7735-2