Sand-bed defect recognition for 3D sand printing based on deep residual network

• 作 者: Lan-xiu Wang Xuan-pu Dong Shu-ren Guo
• 机 构: State Key Laboratory of Materials Processing and Die & Mould Technology,School of Materials Science
• 出 处: 中国铸造
• 发表日期: 2021年4期
• 关键词: 3D sand printing sand-bed defects deep residual network sand-bed images

The 3D sand printing (3DSP),by binder jetting technology for rapid casting,has a pivotal role in promoting the development of the traditional casting industry as a result of producing high-quality and economical sand molds.This work presents an approach for monitoring and analyzing powder sand-bed images to serve as a real-time control system in a 3DSP machine.A deep residual network (ResNet) is used to classify the defects occurring during the powder spreading stage of the process.Firstly,a pre-trained network was applied as the initial parameter;then it was fine-tuned on the labelled defective sample dataset to accomplish the task,which defines the sand-bed defects induced in the 3DSP processing.Furthermore,the recognition and positioning of sand-bed defects were readily achieved by dividing the sand-bed images into blocks.Experiments show that the fine-tuned network has a 98.7％ classification accuracy on the validation dataset of sand-bed defects and 95.4％ recognition accuracy for the sand-bed images.