Recognition of DC01 Mild Steel Laser Welding Penetration Status Based on Photoelectric Signal and Neural Network

Abstract

Achieving online inspection and recognition of laser welding quality is essential for intelligent industrial manufacturing. The weld penetration status is an important indicator for assessing the welding quality, and the optical signal is the most common changing feature in the laser welding process. This paper proposes a new method based on a photoelectric signal and neural network for laser welding penetration status identification. A laser welding experimental system platform based on a photoelectric sensor is built, the laser welding experimental material is DC01 mild steel, and the photoelectric signal in the laser welding process is collected. The collected signal is then processed, and features are extracted using wavelet packet transform and probability density analyses. The mapping relationship between the signal features and weld penetration status is investigated. A deep learning convolutional neural network (CNN)-based weld penetration status recognition model is constructed, with multiple eigenvalue vectors as input, and the model training and recognition results are analyzed and compared. The experimental results show that the photoelectric signal features are highly correlated with the weld penetration status, and the constructed CNN weld penetration status recognition model has an accuracy of up to 98.5% on the test set, demonstrating excellent performance in identifying the quality of the laser welding. This study provides the basis for the online inspection and intelligent identification of laser welding quality.