Detection of powder bed defects in selective laser sintering using convolutional neural network

Abstract

The presence of defects in a powder bed fusion (PBF) process can lead to the formation of flaws in consolidated parts. Powder bed defects (PBDs) have different sizes and shapes and occur in different locations in the built area. Those variations pose great challenges to their detection. In this study, a deep convolution neural network was applied to detect three typical types of PBDs in a selective laser sintering (SLS) process, namely warpage, part shifting, and short feed, which were intentionally generated by varying the process conditions. Images of the powder bed were captured using a digital camera, which were split into three single-channel images corresponding to the color channels in the color image. A deep residual neural network was then used to extract multiscale features, and a region proposal network was adopted to detect the object-level defect bounding box. In the final stage, a fully convolutional neural network was proposed to generate instance-level defect regions in the bounding box. Our results demonstrated that this method had higher accuracy and efficiency and was able to cope with geometrical distortion and image blurring, in comparison to the defect detection methods reported previously. Also, the detection system was cost-effective and could be easily installed outside the chamber of a PBF system. This study lays the groundwork for the development of a variety of automated technologies for additive manufacturing, such as real-time powder layer quality inspection and 3D quality certificate generation for finish parts.