Abstract
A laser beam is a heat source with a high energy density; this technology has been rapidly developed and applied in the field of welding owing to its potential advantages, and supplements traditional welding techniques. An in-depth analysis of its operating process could establish a good foundation for its application in China. It is widely understood that the welding process is a highly nonlinear and multi-variable coupling process; it comprises a significant number of complex processes with random uncertain factors. Because of their nonlinear mapping and self-learning characteristics, artificial neural networks (ANNs) have certain advantages in comparison to traditional methods in the field of welding. Laser welding is a nonlinear dynamic process; these processes still pose a major challenge in the field of control. Therefore, establishing a stable model is a prerequisite for achieving accurate control. In this study, the identification and control of radial basis function neural networks in laser welding processes and self-tuning PID control methods are proposed to improve weld quality. Using a MATLAB simulation, it is shown that the proposed method can obtain a good description of the level of nonlinear dynamic control, and that the algorithm identification accuracy is high, practical, and effective. Using this method, the weld width quickly reaches the expected value and the system remains stable, with good robustness. Further, it ensures the stability and dynamic performance of the welding process and improves weld quality.
Highlights
The ‘Made in China 2025’ initiative put forth two strategic development demands, namely, ‘green manufacturing’ and ‘smart manufacturing’
This paper presents an radial basis function (RBF) neural network and self-tuning PID nonlinear identification and control method for laser welding
The results regarding system identification based on the RBF neural network and self-tuning PID control based on the RBF neural network perform well
Summary
The ‘Made in China 2025’ initiative put forth two strategic development demands, namely, ‘green manufacturing’ and ‘smart manufacturing’. This paper presents an RBF neural network and self-tuning PID nonlinear identification and control method for laser welding. This technique helps eliminate welding uncertainties and improves weld quality. It helps to enhance the smart-control level in the welding process and increases the reliability of the product It provides the basis for adjusting the welding parameters online and controlling the weld-seam quality in real-time. The different parameters of laser pulses can be adjusted (such as width, energy, frequency, and power) to ensure optimal welding in a short time along with high weld quality. We design the nonlinear identification and control based on RBF neural networks and the self-tuning PID control based on these two variables. This allows the network to be trained rapidly, avoiding the local optimum, and achieving a good nonlinear approximation
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