The Application of Convolutional Neural Networks (CNNs) to Recognize Defects in 3D-Printed Parts

• Main Authors: Hao Wen, Chang Huang, Shengmin Guo
• Format: Article
• Language: English
• Published: MDPI AG 2021-05-01
• Series: Materials
• Subjects: defect classification; defect detection; image segmentation; CNNs; YOLOv4; Detectron2
• Online Access: https://www.mdpi.com/1996-1944/14/10/2575

Cracks and pores are two common defects in metallic additive manufacturing (AM) parts. In this paper, deep learning-based image analysis is performed for defect (cracks and pores) classification/detection based on SEM images of metallic AM parts. Three different levels of complexities, namely, defect classification, defect detection and defect image segmentation, are successfully achieved using a simple CNN model, the YOLOv4 model and the Detectron2 object detection library, respectively. The tuned CNN model can classify any single defect as either a crack or pore at almost 100% accuracy. The other two models can identify more than 90% of the cracks and pores in the testing images. In addition to the application of static image analysis, defect detection is also successfully applied on a video which mimics the AM process control images. The trained Detectron2 model can identify almost all the pores and cracks that exist in the original video. This study lays a foundation for future in situ process monitoring of the 3D printing process.