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

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-be...

Full description

Saved in:
Bibliographic Details
Published in:China foundry 2021-07, Vol.18 (4), p.344-350
Main Authors: Wang, Lan-xiu, Dong, Xuan-pu, Guo, Shu-ren
Format: Article
Language:English
Subjects:
Analysis
Control systems
Digital signal processors
Engineering
Machines
Manufacturing
Materials Engineering
Metallic Materials
Powders
Processes
Research & Development
Technology application
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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 realtime 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.
ISSN:1672-6421
2365-9459
DOI:10.1007/s41230-021-1091-x