A Review of the Image Classification Models Used for the Prediction of Bed Defects in the Selective Laser Sintering Process

Abstract

Defects formed during the spreading of powder, known as powder bed defects, are a major issue in additive manufacturing processes. Deep learning (DL)-based image classification models can be utilised to detect defects caused by the powder spreading process. The aim of this research was to review and compare the performance of the EfficientNet_v2 deep learning image classification model against the commonly used VGG-16 model on a selective laser sintering powder bed defects (SLS PBDs) dataset. It was observed that the EfficientNet_v2 model achieved higher performance than the commonly used VGG-16 model, with a defect prediction accuracy of 97.54% and model sensitivity of 96.3%.

Keywords

• additive manufacturing
• machine learning
• defects
• selective laser sintering