Deep learning‐based methods for detecting defects in cast iron parts and surfaces

Abstract

Abstract The large size tolerance and positional differences of burrs in cast iron blanks make it easy for traditional teaching polishing paths to cause overcutting or undercutting. Rapid and accurate identification of burrs and real‐time correction of polishing trajectories are key technical issues for achieving high‐precision polishing. Here, a deep learning‐based method for defect detection in cast iron parts and surfaces is proposed. Firstly, a self‐made dataset of cast iron parts and surface defects is created and annotated, and a variety of data augmentation methods are used to expand the number of samples in the original dataset, alleviating the problem of small sample size. Then, the coordinate attention mechanism is introduced into the backbone network to allocate more attention to the defect target. Finally, the bidirectional weighted feature pyramid network (BiFPN) is used in the feature fusion network to replace the original path aggregation network, improving the model's ability to fuse features of different sizes. Experimental results show that compared with the original model, the mean average precision (mAP) is increased by 3.1%, and the average precision (AP) in defect classification is increased by 7.6%, with an FPS of 112, achieving accurate and efficient real‐time detection of cast iron parts and surface defects.

Keywords

• convolutional neural nets
• deburring
• image fusion
• learning (artificial intelligence)