基于边缘夹角附加损失函数的熔池形貌检测方法

Detection method of molten pool shape based on additional loss function of edge included angle

针对弧焊增材制造过程中传统的熔池检测方法依赖经验参数、准确率低、识别时间较长的问题,提出了一种基于边缘夹角附加损失函数的熔池形貌检测方法,实现对熔池快速而精准的检测和识别.首先,通过特征金字塔网络融合多种特征在多个尺度上表征熔池,摆脱对经验参数的依赖;其次,使用Point Rend神经网络模块,基于细粒度特征及粗预测掩码对采样点优化,减少熔池目标检测及识别所需时间;再次,研究了边缘夹角附加损失函数,在角度空间上最大化分类间隔,使网络提取到的特征具有更强的可分性,进而改善模型识别的精度;最后,利用实际熔池监测数据进行试验测试.结果表明,该方法识别精度高,精度达97.85%,当存在熔滴覆盖干扰时,也可以实现精确检测与识别;对比熔池的检测宽度和实测宽度,绝对误差在0.36 mm以内,试验结果验证了该方法的有效性和可靠性.

Aiming at the problems of traditional molten pool detection methods in arc welding additive manufacturing process,such as relying on empirical parameters,low accuracy and long recognition time,a molten pool shape detection method based on additional loss function of edge included angle is proposed to realize rapid and accurate detection and recognition of molten pool.Firstly,the molten pool is represented on multiple scales by integrating multiple features with feature pyramid network,and the dependence on empirical parameters is avoided.Secondly,Point Rend neural network module is used to optimize the sampling points based on fine-grained features and coarse prediction mask to reduce the time required for molten pool target detection and recognition.Thirdly,the additional loss function of edge included angle is studied to maximize the classification interval in the angle space,which makes the features extracted by the network more separable and improves the recognition accuracy of model.Finally,tests are carried out using the actual molten pool monitoring data,and the results show that the proposed method has a high recognition accuracy,the accuracy rate is 97.85%.In the presence of molten droplet coverage interference,it can also achieve an accurate detection and recognition performance.Compared with the detection width and measured width of molten pool,the absolute error is less than 0.36 mm.The experimental results have demonstrated that the method is effective and reliable.