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

Xia, M. and Shao, H. and Huang, Z. and Zhao, Z. and Jiang, F. and Hu, Y. (2022) Intelligent Process Monitoring of Laser-Induced Graphene Production with Deep Transfer Learning. IEEE Transactions on Instrumentation and Measurement, 71: 3516409. ISSN 0018-9456

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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.