基于目标检测的选区激光熔融成形过程熔池与飞溅监测

Melt Pool and Spatter Monitoring in Selective Laser Melting Forming Process Based on Target Detection

在选区激光熔融成形过程中,飞溅与熔池包含了能够体现加工质量的重要特征信息,从成形过程采集到的熔池图像中,获得这些信息,实现选区激光熔融的过程监测是近年来研究的重点之一。为了更加精确且有效地从图像中提取熔池和飞溅的信息,提出了一种基于YOLOv5目标检测模型,实现了对成形过程图像中飞溅与熔池的实时定位与捕获。首先,以YOLOv5s目标检测网络为基础,调整骨干网络的深度与宽度,修改检测头的数量。之后,引入自校正卷积与CBAM注意力机制模块,设计了新的特征整合结构,通过上述步骤,提升了网络的检测性能。将工业相机采集到的图像制作为目标检测数据集,进行模型的训练与测试,结果表明该网络能够从原始图像中对飞溅与熔池目标进行准确的定位,在具有良好的检测精度的同时,网络模型的参数量极少,更加符合工业应用的需求。网络的检测精度mAP@0.5:0.95达到了0.466,为基于图像的选区激光熔融过程监测提供了一种新的方法。

In the process of selective laser melting,spatter and melt pool contain important information which can reflect the processing quality.It is one of the research emphases in recent years to obtain this information from the melt pool images,which are collected in the processing process,and then realize the process monitoring of selective laser melting.In order to extract the information of melt pool and spatter more accurately and effectively,a target detection model based on YOLOv5 is proposed to realize the real-time location and capture of the spatter and melt pool from the processing image.Firstly,based on YOLOv5s target detection network,the depth and width of backbone network are adjusted,and the number of detection heads is modified.After that,Self-calibrated convolutions and CBAM attention module are introduced to design a new feature integration structure.Through the above steps,the detection performance of the network is improved.The images collected by industrial camera are made into target detection datasets for model's training and testing.The results show that the network can accurately locate the spatter and melt pool targets from the original image.With better detection accuracy,the network model has few parameters,which is more in line with the needs of industrial applications.The detection accuracy of mAP@0.5:0.95 reaches 0.466,thus provides a new method for the monitoring of selective laser melting process based on images.