Weld defect identification in friction stir welding through optimized wavelet transformation of signals and validation through X-ray micro-CT scan

Roy, Rohan Basu; Ghosh, Alekhya; Bhattacharyya, Soham; Mahto, Raju Prasad; Kumari, Kanchan; Pal, Surjya K.; Pal, Srikanta

doi:10.1007/s00170-018-2519-3

Cited by 33 publications

(11 citation statements)

References 34 publications

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“…Therefore, some other statistical parameter is needed for identifying the defective region and to show which mother wavelet can do that by extracting the maximum amount of information about the defect from the raw signal. According to the available literature [15,19], the square of errors has been used as a metric to identify defects which necessarily indicates the variance of data points. As pointed in another literature [34], the kurtosis of the detail coefficients obtained by applying DWT on a signal represents the presence of outliers better than the variance and at the same time, it also eliminates the transient noise in the signal, if any.…”

Section: Methodsmentioning

confidence: 99%

“…Moreover, this work also does not justify the use of db8 as the mother wavelet. The force signal and power consumption by the machine over the welding period have been utilized in another study for differentiating the defective and defect-free welds in the FSW process [19]. The said signals have been analyzed via DWT, where the detailed coefficients have been extracted up to six levels.…”

Section: Introductionmentioning

confidence: 99%

“…The Fourier transform technique can be applied to find the frequency components. However, upon transforming, the time information is lost, and they are effective for stationary signals [19]. For identification of the frequencies present in a signal with the time information, time-frequency analysis is needed.…”

Section: Introductionmentioning

confidence: 99%

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Weld defect localization in friction stir welding process

et al. 2020

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The article attempts to detect the defects in friction stir welding (FSW) process by analyzing the signal acquired during welding. The said welding technique utilizes pressure and heat developed by the usage of a non-consumable tool. Thus, the axial force signal carries a lot of information about the physical process, and hence, it could be used to identify the weld defects. Signal analysis has been performed by using wavelet-based techniques. Before this analysis, a methodology has been followed to select the best mother wavelets suitable for the signal. The results of defect identification have been validated by mapping the processed signal with the actual weld quality.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Weld defect localization in friction stir welding process

et al. 2020

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“…The prior research in this regard reports strategies developed for monitoring the welding process.Methods were devised to classify defective and defect-free welds using information derived from acoustic emission (AE), and longitudinal and transverse force signals [11,12].A few works presented methods to localize welding defects using AE, axial force, power, and spindle torque signals [12][13][14][15][16][17][18]. Information derived from AE signal was also utilized in detecting voids in the weld jointline [19,20].…”

Section: Introductionmentioning

confidence: 99%

Industry 4.0 Application in Manufacturing for Real-Time Monitoring and Control

Mishra

Priyadarshi

Das

et al. 2022

JDMD

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Modern manufacturing aims to reduce downtime and track process anomalies to make profitable business decisions. This ideology is strengthened by Industry 4.0, which aims to continuously monitor high-value manufacturing assets. This article builds upon the Industry 4.0-concept to improve the efficiency of manufacturing systems. The major contribution is a framework for continuous monitoring and feedback-based control in the friction stir welding (FSW) process. It consists of a CNC manufacturing machine, sensors, edge, cloud systems, and deep neural networks, all working cohesively in real-time. The edge device, located near the FSW machine, consists of a neural network that receives sensory information and predicts weld quality in real-time. It addresses time-critical manufacturing decisions. Cloud receives the sensory data if weld quality is poor, and a second neural network predicts the new set of welding parameters that are sent as feedback to the welding machine. Several experiments are conducted for training the neural networks. The framework successfully tracks process quality and improves the welding by controlling it in real-time. The system enables faster monitoring and control achieved in less than 1 second. The framework is validated through several experiments.

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“…The stress wave detection requires the sensors to be nailed to the polymer-modified wood, which would cause damage. The X-ray inspection equipment 2 Advances in Polymer Technology costs too much and also has a safety concern; e.g., if used improperly, it can cause radiant damage to human body [16].…”

Section: Introductionmentioning

confidence: 99%

Imaging of Internal Defects of Polymer-Modified Wood Using Total Focusing Method

Sun

Zhou

Jiao

et al. 2019

Advances in Polymer Technology

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Polymer modification can improve the stability and corrosion resistance of wood, but it could create defects inside wood during the modification processing. Detection of defects inside polymer-modified wood can reduce wood losses and prevent the occurring of defects. Data simulation and tomographic imaging of polymer-modified wood internal defects were carried out using electromagnetic waves with nondestructive testing. This study constructed the polymer-modified wood models, simulated the electromagnetic scattering wave, and used the total focusing method to perform tomography of the defects in the polymer-modified wood. By analyzing the imaging characteristics of different types of defects, the effectiveness of electromagnetic waves in the detection of internal defects of polymer-modified wood was proved. This method can be extended to test internal defects of other high molecular polymers.

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Weld defect identification in friction stir welding through optimized wavelet transformation of signals and validation through X-ray micro-CT scan

Cited by 33 publications

References 34 publications

Weld defect localization in friction stir welding process

Weld defect localization in friction stir welding process

Industry 4.0 Application in Manufacturing for Real-Time Monitoring and Control

Imaging of Internal Defects of Polymer-Modified Wood Using Total Focusing Method

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