A layer-wise multi-defect detection system for powder bed monitoring: Lighting strategy for imaging, adaptive segmentation and classification

Abstract

Powder bed defects usually inevitably appear in the process of powder spreading during laser powder bed fusion owing to the characteristics of the powder material and the performance of the spreading equipment. This may lead to instability with regard to subsequent processes and the quality of the final part. Defect detection based on the imaging process is an effective way to achieve non-contact, efficient, and accurate online monitoring, and it has received widespread attention. In this paper, an imaging method for online collection of powder bed is proposed based on the experiments of various lighting strategies, whose influences on defect analysis are evaluated. Subsequently, an adaptive segmentation algorithm for defect extraction is proposed that automatically searches for the best threshold by evaluating the gray histogram of the powder bed image. Finally, different convolution neural networks were applied to implement the classification of the defects, and their performances were evaluated and compared. The results of the on-site experiments demonstrate that the proposed method has good accuracy and efficiency in the multi-defect detection of a powder bed.

Keywords

• Adaptive segmentation
• Multi-defect detection
• Lighting strategy
• Online monitoring
• Additive manufacturing