Unsupervised droplet identification during the pulsed laser enhanced GMAW process

Wang, Zhenzhou; Yi, Huang; Zhang, Yuming

doi:10.1007/s00170-012-4580-7

Cited by 4 publications

(1 citation statement)

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“…The variation in model parameters due to process disturbances in the laser-fusion phenomenon could be captured by recursively updating the matrix parameters, using the matrix-forgetting factor approach. Na [8] introduced the discrete Hammerstein model identification of the nonlinear laser welding processes using the least-squares method [9]. Shen et al [10] established a bilinear model for laser welding systems that uses the correlation-based least-squares method and also designed a minimum-variance adaptive controller for the system based on feedback linearization to obtain acceptable unbiased estimates for the unknown parameters.…”

Section: Introductionmentioning

confidence: 99%

Nonlinear Identification and Control of Laser Welding Based on RBF Neural Networks

Wei¹,

Zhao²,

Shi³

et al. 2022

Computer Systems Science and Engineering

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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 indepth 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.

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Section: Introductionmentioning

confidence: 99%