Toward laser-assisted cutting: A real-time segmentation method for reinforcing particles in particle-reinforced metal matrix composites

Particle-reinforced metal matrix composites (PRMMCs) are widely used because of their exceptional material properties. Online control of the laser field to soften and modify the reinforcing particles on the machined surface of the composites is an effective way to improve the machinability and machining quality of PRMMCs. A real-time segmentation method for reinforcing particles in PRMMCs is proposed. First, real-time acquisition of reinforcing particle images along the processing path is achieved using machine vision, and cutting region images are determined. Next, to improve the model’s ability to effectively segment the reinforcing particles in low-resolution images of the machining region, a reinforcing particle segmentation network (RPSNet) is proposed, incorporating a multimodal fusion and space-to-depth convolution module. Subsequently, position signals along the cutting direction are obtained by using a sliding window method. The effectiveness of each module and the performance of the model are analyzed and verified through comparative and ablation experiments. The results demonstrated that the proposed RPSNet achieved a mean average precision (mAP) of 95.4 % in segmenting reinforcing particles, with an inference time of 5.8 ms. In comparison to other methods, it demonstrated better real-time performance and accuracy. Additionally, the proposed method can convert image information into position signals, thus enabling real-time control of the laser for softening and modifying the reinforcing particles.