Xingyu Gao

The arc sound signal is one of the most important aspects of information related to pattern identification regarding the penetration state of ship robotic GMAW; however, arc sound is inevitably affected by noise interference during the signal acquisition process. In this paper, an improved wavelet threshold denoising method is proposed to eliminate interference and purify the arc sound signal. The non-stationary random distribution characteristics of GMAW noise interference are also estimated by using the high-frequency detail coefficients in different domains after wavelet transformation, and a mode of measuring scale that is logarithmically negatively correlated with the wavelet decomposition scale is created to update the threshold. The gradient convergent threshold function is established using the natural logarithmic function structure and concave–convex gradient to enable the nonlinear adjustment of the asymptotic rate. Further, some property theorems related to the optimized threshold function are proposed and theoretically proven, and the effectiveness and adaptability of the improved method are verified via the denoising simulation of speech synthesis signals. The four traditional denoising methods and our improved version are applied in the pretreatment of the GMAW arc sound signal, respectively. Statistical analysis and short-time Fourier transform are used to extract eight-dimensional time and frequency domain feature parameters from the denoised signals with randomly time-varying characteristics, and the extracted joint feature parameters are used to establish a nonlinear mapping model of penetration state identification for ship robotic GMAW using the pattern classifiers of RBFNN, PNN and PSO-SVM. The simulation results yielded by visual penetration classification and the multi-dimensional evaluation index of the confusion matrix indicate that the improved denoising method proposed in this paper achieves a higher accuracy in the extraction of penetration state features and greater precision in the identification of pattern classification.

To solve the knowledge modelling problem of strong nonlinear characteristics such as multivariable, high coupling and stochastic interference in the GMAW process of hull structures, a clustering similarity particle filter (CSPF) method based on the consistency principle of the state trajectory is proposed to establish a knowledge model for welding dynamics. The modelling mechanism of the stochastic process is selected to construct a Hammerstein model with uncertain noise in the relationship between the welding parameters and geometric characteristics of the weld pool, and the Hammerstein model is identified using a step response test of constant specification and two parameter identification algorithms: the recursive least squares method and the final prediction error criterion. Thus, the state space equation of the accurate identification structure is provided for knowledge process modelling. Relying on the particle filter algorithm as the core of the modelling framework and theoretical analysis, SIS filtering and GPF prediction methods are adopted to obtain the combined trajectory formed by the current (original) and future multistage (modified) spatial state information, and the state similarity between the combined trajectory and the actual system trajectory is measured by the cluster analysis method. A new proposed distribution is generated under the guidance of similarity measurement to improve the particle degradation phenomenon, update the first-order Markov process with new observation information to compensate and modify the importance weight calculation, replace the resampling strategy to eliminate the particle depletion problem, and then establish the welding knowledge model of state tracking and forming prediction. Through simulation and experimentation of hull structure GMAW, it is concluded that both the training effect of state tracking and the prediction accuracy of weld formation can meet the process requirements of ship welding by particle filter and its improved method. Meanwhile, integrated with the convergence theorem of the CSPF algorithm, compared with the standard particle filter and auxiliary particle filter, the training effect of state tracking is better in the application of the CSPF method in the process knowledge modelling of hull structure GMAW, and the prediction results of weld forming have the advantages of higher accuracy, stronger robustness, and better timeliness.

Image processing and feature information extraction based on the visual perception of the weld pool are considered essential components of intelligent welding quality monitoring of hull structure gas metal arc welding (GMAW). The unstable characteristics, such as large spatter, much smoke, and strong arc light during hull structure GMAW, lead to the blurring of image acquisition and the difficulty of contour extraction of the weld pool. The present study is aimed at addressing the practical issues from two perspectives, i.e., a spectrum‐visual‐sensing acquisition system and an image‐processing and feature extraction algorithm. First of all, by analyzing the light energy distribution law and acquiring the optical parameters relevant to the cut‐off composite dimming and near‐infrared narrowband filtering, spectral sensing is employed in establishing models of arc light radiation to detect the strength of continuously distinctive spectral lines. Besides, an appropriate high‐speed charge‐coupled device (CCD) camera is selected to build a visual acquisition system, which can reduce the external interference of the arc light on the image acquisition of the weld pool. Afterwards, the implementation of an image‐processing fusion model based on the spatial information fuzzy C‐means (FCM) clustering analysis and Sobel edge detection operator accompanies the investigation of the geometric aspects of the weld pool image. In terms of clear segmentation of the interest region, the edge detection and accurate extraction of the target contour are successfully obtained. In the subsequent section, the Hough transform analysis is adopted to establish the geometric feature extraction model of the weld pool, with corner detection, conversion, and camera calibration as the core technology. Additionally, the left and right views of the image contour are calibrated to achieve the lossless conversion of corner pixels and physical coordinates. Finally, three other distinct image‐processing methods are designed to compare the segmentation effect of the edge contour with the fusion model, and then, the extraction accuracy of the geometric features of the weld pool is verified. The interference of the arc light and smoke has been demonstrated to be substantially diminished, which is attributable to the visual‐sensing system during image acquisition of the weld pool. The results of edge fusion of the weld pool image show that based on the GMAW using the FCM‐Sobel fusion method, a superior extraction accuracy of geometric features characterized by smoothness, continuousness, no breakpoints, and less noise has fulfilled the engineering requirements.