Self-Monitoring of 3D Printing Performance using Machine Learning


External Member

Luke Connal (Research School of Chemistry)


A new 3D printing method that allows for printing electronic devices within a single machine aims to improve reliability and reproducibility while reducing failure rates, down-time and waste. The technology has several advantages over existing 3D printing methods by allowing simultaneous monitoring of the printing process in real time, which thus allows for simultaneous quality control, a high degree of reproducibility and avoids defects in the printed structure by utilising a feedback algorithm.  It is envisaged that this method can add to the current tools in electronics manufacturing and even has the potential to replace many, if not all, of the current multistep and environmentally unfriendly procedures, but problems arise.  Due to the nature of the technology with each print job a specific signal can be generated and assessed, but there is currently no way to both identify and anomaly (and stop to correct production) and gain insights into what caused it. 

This is an idea task for interpretable classification where real-time data can be used to identify conditions that lead to problems and rank the importance of experimental features that should be controlled in the lab.  In this project machine learning will be to develop a classifier to allow self-monitoring and in-situ quality control of the prints.  In collaboration with researchers in the Research School of Chemistry, this model will contribute to a first-of-its-kind self-learning 3D printer with the potential to revolutionise printed electronics.



python programming, COMP1730/6730, COMP3430/8430, COMP3670/6670


machine learning, classification, data science, 3D printing, additive manufacturing, python

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