Rights statement: ©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.
Accepted author manuscript, 915 KB, PDF document
Available under license: CC BY: Creative Commons Attribution 4.0 International License
Final published version
Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
}
TY - JOUR
T1 - Intelligent Process Monitoring of Laser-Induced Graphene Production with Deep Transfer Learning
AU - Xia, M.
AU - Shao, H.
AU - Huang, Z.
AU - Zhao, Z.
AU - Jiang, F.
AU - Hu, Y.
N1 - ©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.
PY - 2022/6/27
Y1 - 2022/6/27
N2 - 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.
AB - 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.
U2 - 10.1109/TIM.2022.3186688
DO - 10.1109/TIM.2022.3186688
M3 - Journal article
VL - 71
JO - IEEE Transactions on Instrumentation and Measurement
JF - IEEE Transactions on Instrumentation and Measurement
SN - 0018-9456
M1 - 3516409
ER -