Three-dimensional (3D) 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 3D 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 3D graphene. This paper 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 the proposed method can achieve the state-of-art performance of precise and robust monitoring for the quality of the LIG formation.
In this article, a kind of SMC artificial marble was prepared. In order to enhance the mechanical properties and prolong the using life of SMC artificial marble, some effects such as fiber content, filler content and molding temperature etc. on the mechanical properties were carefully studied, too. Results showed that the increase of fiber content could improve the impact strength of SMC artificial marble when the fiber length was 10mm and the increase of filler content would decrease the flexural strength of SMC artificial marble. And the molding temperature at the range of 130°C ~ 160°C had little influence on the mechanical properties of SMC artificial marble. Comparing with natural marble and casting marble, SMC artificial marble owed superior overall performance and it was much more suitable for industry production.
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