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In-situ droplet inspection and closed-loop control system using machine learning for liquid metal jet printing

Title:

In-situ droplet inspection and closed-loop control system using machine learning for liquid metal jet printing

Wang, Tianjiao, Kwok, Tsz-Ho ORCID: https://orcid.org/0000-0001-7240-1426, Zhou, Chi and Vader, Scott (2018) In-situ droplet inspection and closed-loop control system using machine learning for liquid metal jet printing. Journal of Manufacturing Systems, 47 . pp. 83-92. ISSN 02786125

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Official URL: http://dx.doi.org/10.1016/j.jmsy.2018.04.003

Abstract

Liquid Metal Jet Printing (LMJP) is a revolutionary three-dimensional (3D) printing technique in fast but low-cost additive manufacturing. The driving force is produced by magneto-hydrodynamic property of liquid metal in an alternating magnetic field. Due to its integrated melting and ink-jetting process, it can achieve 10x faster speed at 1/10th of the cost as compared to current metal 3D printing techniques. However, the jetting process is influenced by many uncertain factors, which impose a significant challenge to its process stability and product quality. To address this challenge, we present a closed-loop control framework by seamlessly integrating vision-based technique and neural network tool to inspect droplet behaviours and accordingly stabilize the printing process. This system automatically tunes the drive voltage applied to compensate the uncertain influence based on vision inspection result. To realize this, we first extract multiple features and properties from images to capture the droplet behaviour. Second, we use a neural network together with PID control process to determine how the drive voltage should be adjusted. We test this system on a piezoelectric-based ink-jetting emulator, which has a very similar jetting mechanism to the LMJP. Results show that significantly more stable jetting behavior can be obtained in real-time. This system can also be applied to other droplet related applications owing to its universally applicable characteristics.

Divisions:Concordia University > Gina Cody School of Engineering and Computer Science > Mechanical, Industrial and Aerospace Engineering
Item Type:Article
Refereed:Yes
Authors:Wang, Tianjiao and Kwok, Tsz-Ho and Zhou, Chi and Vader, Scott
Journal or Publication:Journal of Manufacturing Systems
Date:2018
Funders:
  • Center of Excellence in Materials Informatics and the Natural Sciences & Engineering Research Council of Canada (NSERC)
Digital Object Identifier (DOI):10.1016/j.jmsy.2018.04.003
Keywords:Additive manufacturing, Metal jetting, Process inspection, Closed-loop control, Vision, Neural network
ID Code:985959
Deposited By: TSZ HO KWOK
Deposited On:11 Feb 2020 17:01
Last Modified:08 Apr 2020 00:00
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