Name
Title | ||
|---|---|---|
Intelligent Process Monitoring of Laser-Induced Graphene Production With Deep Transfer Learning |
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. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/TIM.2022.3186688 | IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT |
Keywords | DocType | Volume |
Graphene, Three-dimensional displays, Manufacturing, Production, Process monitoring, Laser modes, Cameras, Deep transfer learning, laser-induced graphene (LIG), process monitoring, semi-supervised learning | Journal | 71 |
ISSN | Citations | PageRank |
0018-9456 | 0 | 0.34 |
References | Authors | |
0 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Min Xia | 1 | 0 | 1.69 |
| Haidong Shao | 2 | 63 | 10.49 |
| Zheng Huang | 3 | 0 | 0.34 |
| Zhe Zhao | 4 | 0 | 0.34 |
| Feilong Jiang | 5 | 0 | 0.34 |
| Yaowu Hu | 6 | 0 | 0.34 |