A Dual Neural Network for Defect Detection With Highly Imbalanced Data in 3-D Printing

Abstract

Digital light processing (DLP) is a popular additive manufacturing technology that uses light irradiation to fabricate 3-D devices via a projector to achieve laser-sensitive resin curing. However, the performance and reliability of DLP can be affected by internal defects such as printing errors and the accumulation of residual stress. Existing defect detection methods rely on monitoring the printed parts, which leads to resource wastage and struggles to effectively handle imbalanced defect data. In this article, we propose a defect detection method called dual neural network, which involves detecting defects in materials before the printing process to prevent resource wastage and serious consequences. Specifically, to handle the highly imbalanced class distribution problem in online DLP defect detection, dual neural network utilizes a domain learner and balance learner to effectively balance the information of the minority class and learn the generalization knowledge from the imbalanced defect dataset. Experimental results demonstrate the effectiveness of our proposed method, which has also been applied to real-world production equipment successfully.