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Intelligent Process Monitoring of Laser-Induced Graphene Production with Deep Transfer Learning

Research output: Contribution to Journal/MagazineJournal articlepeer-review

Published
  • M. Xia
  • H. Shao
  • Z. Huang
  • Z. Zhao
  • F. Jiang
  • Y. Hu
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Article number3516409
<mark>Journal publication date</mark>27/06/2022
<mark>Journal</mark>IEEE Transactions on Instrumentation and Measurement
Volume71
Number of pages9
Publication StatusPublished
<mark>Original language</mark>English

Abstract

Three-dimensional graphene has been increasingly used in many applications due to its superior properties. The laser-induced graphene (LIG) technique is an effective way to produce 3-D graphene by combining graphene preparation and patterning into a single step using direct laser writing. However, the variation in process parameters and environment could largely affect the formation and crystallization quality of 3-D graphene. This article develops a vision and deep transfer learning-based processing monitoring system for LIG production. To solve the problem of limited labeled data, novel convolutional de-noising auto-encoder (CDAE)-based unsupervised learning is developed to utilize the available unlabeled images. The learned weights from CDAE are then transferred to a Gaussian convolutional deep belief network (GCDBN) model for further fine-tuning with a very small amount of labeled images. The experimental results show that the proposed method can achieve the state-of-art performance of precise and robust monitoring for the quality of the LIG formation.

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©2022 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.