Arc Sound Research Articles

Welding Penetration Monitoring for Ship Robotic GMAW Using Arc Sound Sensing Based on Improved Wavelet Denoising

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.

Weld Defect Detection of a CMT Arc-Welded Aluminum Alloy Sheet Based on Arc Sound Signal Processing

The cold metal transfer (CMT) process is widely used in thin plate welding because of its characteristics of low heat input and stable arc. In actual production, a larger weld gap, misalignment, or other problems due to assembly error lead to serious welding defects, such as burn-through and a lack of fusion. The arc sound contains a wealth of information related to the quality of the weld. This work analyzes the mechanism of CMT arc sound generation, as well as the correlation between the time–frequency spectrum of the arc sound signal and welding quality. This paper studies the extraction of the multi-channel time–frequency spectrum of an arc sound and inputs it to a custom convolutional neural network for the CMT welding defect identification of thin aluminum alloy plates. The experimental result shows that the average accuracy of the proposed model is 91.49% in the defect identification of a CMT arc-welded aluminum alloy sheet, which is higher than that of the single-channel time–frequency convolutional neural network and other traditional classification models.

Penetration Recognition in GTAW Welding Based on Time and Spectrum Images of Arc Sound Using Deep Learning Method

The weld penetration rate is an important evaluation criterion for welding quality. However, it is difficult to identify the weld penetration state during GTAW welding process. This paper presents a new penetration recognition method based on time and spectrum images of arc sound using deep learning for DC GTAW welding. The time domain and spectrum images of the three penetration states from the non-periodic arc sound were used as the dataset for the penetration prediction model. VGG16, AlexNet, and custom convolutional neural network (CNN) were used to extract image features, and softmax was used to classify images for penetration recognition. The influence of image feature extraction networks, input methods, and different sampling methods on the recognition accuracy was deeply analyzed. The results show that the overall validation accuracy of the proposed model is approximately 96.2%. Particularly, the validation accuracy of the model in the excessive penetration state is approximately 100%. This study provides a new and feasible method for the online detection of weld penetration during the GTAW welding process.

Weld Seam Deviation Prediction of Gas Metal Arc Welding Based on Arc Sound Signal

Weld seam deviation prediction is the key to weld seam tracking control, which is of great significance for realizing welding automation and ensuring welding quality. Aiming at the problem of weld seam deviation prediction in GMAW (gas metal arc welding), a method of weld seam deviation prediction based on arc sound signal is proposed. By analyzing the feature of the arc sound signal waveform, the time domain feature of the arc sound signal is extracted. The wavelet packet analysis method is used to analyze the time-fre- quency domain feature of the arc sound signal, and the wavelet packet energy feature is extracted. The time domain feature and wavelet packet energy feature are used to establish the feature vector, and the BP (back propagation) neural network is used to realize the weld seam deviation prediction. The results show that the method proposed in this paper has a good weld seam deviation prediction effect, with a mean absolute error of 0.234 mm, which provides a new method for GMAW weld seam recognition.

Penetration state identification of lap joints in gas tungsten arc welding process based on two channel arc sounds

Abstract Lap joints commonly exist in the welding processes during manufacturing of automobiles and ships. Their asymmetric structures make it extraordinary difficult to monitor the penetration states online. In this paper, a two-channel arc sound signal acquisition device is developed and a binaural auditory perception model is proposed to identify the penetration states of lap joints. Firstly, an auditory peripheral model is built to filter the two-channel arc sound signals and decompose them into a series of frequency bands. After that, with simulating the energy transformation and lateral inhabitation of auditory system, the frequency distributions of the two-channel arc sound signals are obtained. Subsequently, the energy differences of the two-channel sounds are obtained and decomposed into multi-scales by an auditory cortex model. Finally, a support vector machine is built to recognize the lap bead penetration states. This study provides a new feasible approach to identify penetration states of lap weld joints.

A Novel Convolutional Neural Network Based on Time–Frequency Spectrogram of Arc Sound and Its Application on GTAW Penetration Classification

Strict industry demands necessitate better penetration classification in gas tungsten arc welding of aluminum alloys. An innovative classification method, namely time-frequency spectrogram based convolutional neural network (TF-CNN), has been developed in this article. The logarithmic time-frequency spectrograms are used to interpret the raw arc sound data in the time-frequency domain and served as the input of an optimized convolutional neural network (CNN) model for welding quality classification. The leaky rectified linear unit activation function and root mean square prop optimizer are utilized to improve the property of CNN. Effective arc sound features are extracted automatically by the CNN model. The proposed method is experimentally verified to recognize four penetration states with an average accuracy of 98.2%, which significantly outperforms the four typical comparison models. The generation mechanism and characteristics of arc sound are discussed as the adaptive feature extraction of TF-CNN. These studies expand the application scope of CNN to intelligent welding and achieve notable effects.

Weld bead penetration identification based on human-welder subjective assessment on welding arc sound

Utilizing arc sound signals to monitor the penetration states of weld bead is a promising method. However, the correlation between penetration states and arc sounds is relatively weak, and arc sounds easily affected by background noise. To distinguish the penetration states of weld bead from arc sound signals, a novel method was developed in this paper. This method utilized the subjective assessments of human welders on arc sounds to build mathematical model, and to identify penetration states. Firstly, a dissimilarity matrix of arc sound pairs was made according to the subjective assessments of human welders on these sound pairs. Subsequently, the correlations of penetration states with auditory features of arc sounds were obtained based on this dissimilarity matrix. Finally, a support vector machine was built to identify the penetration states. The results show that the proposed method has a high correct rate in identifying the penetration states of weld bead.

Weld bead penetration state recognition in GMAW process based on a central auditory perception model

Arc sound signals contain abundant information about the welding process, so they are widely adopted to monitor the penetration states of weld bead online. However, the arc sounds are vulnerable to back ground noises, which limits the practical application of them in industry. In this study, a central auditory perception model was proposed. This model through simulating human auditory system to identify the penetration states of weld bead in GMAW process. Firstly, the auditory spectrums of the arc sounds are obtained through auditory peripheral model. Then the shape of auditory spectrums is analyzed by an auditory cortex model. Finally, according to the auditory cortical response, character vectors are built and used to identify the penetration states. The results show that the proposed method has a high accuracy rate in the identification of penetration states of weld bead. This research provides a new method for the online monitoring of welding quality.

Research on the seam tracking of narrow gap P-GMAW based on arc sound sensing

Arc sensor is important in the automation of narrow gap welding process because only the arc signal can reflect the position of the arc in the groove in real time, as well as the heat input to the side wall. Except the current and voltage of the arc, the deviation state of the welding arc can be identified by experienced welders by the associated arc sound, therefore, the arc sound signal presents a potential welding seam tracking signal. In this study, arc sounds were acquired and processed to examine the correlation with weld deviation during narrow gap pulsed gas metal arc welding (P-GMAW). The influences of groove sidewall on the arc sound signal and the sensitive of arc sound features to welding torch deviation were studied, and the short-time energy was determined as the best parameter to reflect the arc deviation in the narrow gap grooves. Furthermore, a system was established for seam tracking using the arc sound short-time energy and the results show that the arc sound can be used as the control signal to realize seam tracking to a certain degree of precision, thus, providing a new control signal for narrow gap P-GMAW welding seam tracking based on multi-information fusion.

Correlation of Arc Sound and Arc-sidewall Position in Narrow Gap MAG Welding

The control of arc-sidewall distance is the key to ensure good sidewall fusion in the process of narrow gap metal active gas welding(NG-MAG) welding. Through pretreatment and analysis of arc sound in NG-MAG pulse vertical welding, it is found that there exists periodic ringing attenuation phenomenon in the time-domain waveform, and its frequency is equal to the pulse frequency of welding power. The frequency distribution of arc sound energy varies from 0.3 k Hz to 12.0 k Hz. And energy mainly concentrates in two bands, range of 0.3-3.0 k Hz and 9.0-12.0 k Hz. Spectrum distribution of arc sound is highly relevant to arc-sidewall distance. On this basis, noise reduction and time-frequency analysis of arc sound are achieved by use of wavelet transform(WT) and joint time-frequency analysis(JTFA). The results show that low-frequency energy distribution of arc sound is always present, show intermittent-continuous-intermittent, with gradually increasing of arc-sidewall distance. While high-frequency energy distribution of arc sound only present arc-sidewall distance more than 2.6 mm, show intermittent-continuous. The monitoring and early warning of arc-sidewall distance can be realized based on time-frequency distribution of arc sound in NG-MAG pulse vertical welding, which provides a new way for condition monitoring in the process of NG-MAG welding.

Time-Domain Characteristics Analysis of Penetration Arc Sound Signal in MIG Welding

Many important time-domain characteristic parameters are extracted through the short-time analysis of arc sound signal in MIG butt welding with spray transfer, which can be used for the diagnosis on the weld penetration status. And the short-time autocorrelation function and short-time average amplitude difference function are adopted to pitch estimation. The analysis results show that the penetration status can be accurately recognized via the short-time energy, average magnitude, average zero-crossing and zero-to-energy and so on. Meanwhile, the pitch estimation of arc sound signal in experiments is at 220 points, that is 5 ms or 200 Hz in cycle. The methods and results provide a foundation for the diagnosis on penetration based on analysis of arc sound signal and have great theoretical meaning and practical value.

Relationship between Arc Sound Signal and Penetration Status in MIG Welding

Arc sound is well known as the potential source signals for monitoring the weld penetration status,which is very important to the welding process quality control and automatic production.The dynamic and non-stationary arc sound signal in MIG butt welding with spray transfer is selected as an object.The denoising principle and method based on wavelet transform are applied to arc sound signal,and the effect of noise reduction is achieved.Through a reasonable choice for sample data set on that basis,the internal relationship between arc sound signal and penetration status is analyzed from time domain,frequency domain and time-frequency domain.The energy and spectrum of arc sound signal are highly correlated with penetration status and signal energy variation.The signal from 1 500 Hz to 4 500 Hz can accurately detect the changes in penetration status in welding process.The results indicate the feasibility of the penetration state monitored by use of arc sound signal,and a cheap and effective way is provided for controlling the quality of MIG welding process.

ＭＡＧ溶接中の溶落ち現象に伴うアーク音の計測

ＭＡＧ溶接中の溶落ち現象に伴うアーク音の計測