Detection of balling levels on the surface of SLM formed parts based on finite depth separable convolution network

Abstract

To meet the real-time requirements of balling levels detection in selective laser melting processes, a modified detection model, called Finite Depth Separable Convolution Network (F-DSCNet), is proposed by optimizing the existing benchmark model (BM) with two lightweight structures: Depth Separable Convolution (DSC) and Global Average Pooling (GAP). This model balances the effect of reducing model’s parameters and increasing model’s structural complexity brought by DSC on the computation and convergence speed of the model, and only introduces DSC in the higher-level convolution layers of the BM. In addition, the GAP structure is adopted instead of the fully connected layer to further reduce the number of parameters and accelerate model training and convergence. The experimental results show that the F-DSCNet model not only maintains high recognition accuracy but also significantly improves the model’s computation and convergence speed, as well as the recognition response time of a single image, exhibiting strong practicality for engineering applications.