Phased Array Ultrasonic Testing for Post-Weld and OnLine Detection of Friction Stir Welding Defects

Huggett, Daniel J.; Dewan, Mohammad W.; Wahab, Muhammad Abdul; Okeil, Ayman M.; Liao, T. Warren

doi:10.1080/09349847.2016.1157660

Cited by 30 publications

(13 citation statements)

References 27 publications

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“…(8a and b). The 2. results showed that the overall contrast in the image improved, and the dynamic range of the pixel gray value expanded, which enhanced the overall contrast of the image. Binarization threshold was selected by using Otsu's method for Fig.…”

Section: Edge Detection Of 2d Gray Image For Weld Defectsmentioning

confidence: 76%

Using a New Magnetic Flux Leakage Method to Detect Tank Bottom Weld Defects

Cui¹,

Xing²,

Jiang³

et al. 2017

TOPEJ

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Background:The weld is an important connection part of the tank bottom but during the process of manufacturing and through its use, it frequently produces defects and brings serious hidden danger in the process of safety production. Objective:This paper develops a new magnetic flux leakage testing system for tank bottom weld defects and proposes an extraction method for the weld defect. It can be used in the detection and visual evaluation of the weld defects. Method:A continuous non-contact scanning method is used in the rectangular slot defect in the different regions of the weld by using a new magnetization system that is vertical to the travelling direction. The characteristics of the weld and the defect are transformed into accurate two-dimensional grayscale graphics through grayscale linear transformation. This is done through the combination of histogram equalization, Otsu's method of binaryzation, morphologically removing small objects, edge detection, and then structuring a morphologically optimized edge extraction algorithm for edge detection on the grayscale. The displayed grayscale outline locates and quantifies the defects. Conclusion:The results indicated that this method can directly indicate the defect shape, location and other information, the visual display of the magnetic flux leakage testing of the weld defects was also realized. It solved difficulties associated with the magnetic flux leakage method being used in the weld testing and showed how weld detection equipment can be used in the detection and visual evaluation of the weld defects.

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Section: Edge Detection Of 2d Gray Image For Weld Defectsmentioning

confidence: 76%

Using a New Magnetic Flux Leakage Method to Detect Tank Bottom Weld Defects

Cui¹,

Xing²,

Jiang³

et al. 2017

TOPEJ

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“…Lack of penetration, tunnel formation, formation of voids, lack of fusion, surface grooves and kissing bonds are various other defects formed during Friction Stir Welding process [6]. Huggett et al [7] used Nondestructive evaluation (NDE) techniques of phased array ultrasonic testing (PAUT) and digital Xray radiography for detecting the defects in Friction Stir welded Aluminum Alloy 2219-T87 specimens. Chen et al [8] investigated about the welding defects in Friction Stir Welded Aluminum alloy 5456 joints with the help of optical microscopy (OM), energy-dispersive X-ray spectroscopy (EDS) and scanning electron microscope (SEM).…”

Section: Introductionmentioning

confidence: 99%

Detection of Surface Defects in Friction Stir Welded Joints by Using a Novel Machine Learning Approach

Mishra¹,

Dutta²

2020

Applied Engineering Letters

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The Friction stir welding process is a new entrant in welding technology. The FSW joints have high strength and helps in weight saving considerably than the other joining process as no filler material is added during welding. The weld quality is affected because of various kinds of defects occurring during the FSW process. Defects like cavity, surface grooves and flash could occur due to inappropriate set of process parameters which results in excessive or insufficient heat input. Defects analysis can be done by several non-destructive methods like immersion ultrasonic techniques, X-ray radiography, thermography, eddy current testing, synchrotron technique etc. In the present work the image processing techniques are applied over the test samples to detect the surface defects like pin holes, surface grooves etc.

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“…17,[23][24][25][26][27][28][29] It was also suggested that the peak temperature had to be within 80-90% of the solidus temperature of the alloy to avoid the void formation. 25 Diverse experimental studies such as ultrasonic 30 and radiographic 31 detections of voids and theoretical analysis of forces 26,32,33 were undertaken to understand the origin of void formation. Although progress made in the previous work to identify the important variables such as temperature, strain rate, torque, and maximum shear stress on the pin that affect the void formation, no rigorous mechanistic explanation, or criterion for the void formation have emerged.…”

Section: Introductionmentioning

confidence: 99%

Conditions for void formation in friction stir welding from machine learning

Mukherjee

DebRoy

2019

npj Comput Mater

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Friction stir welded joints often contain voids that are detrimental to their mechanical properties. Here we investigate the conditions for void formation using a decision tree and a Bayesian neural network. Three types of input data sets including unprocessed welding parameters and computed variables using an analytical and a numerical model of friction stir welding were examined. One hundred and eight sets of independent experimental data on void formation for the friction stir welding of three aluminum alloys, AA2024, AA2219, and AA6061, were analyzed. The neural network-based analysis with welding parameters, specimen and tool geometries, and material properties as input predicted void formation with 83.3% accuracy. When the potential causative variables, i.e., temperature, strain rate, torque, and maximum shear stress on the tool pin were computed from an approximate analytical model of friction stir welding, 90 and 93.3% accuracies of prediction were obtained using the decision tree and the neural network, respectively. When the same causative variables were computed from a rigorous numerical model, both the neural network and the decision tree predicted void formation with 96.6% accuracy. Among these four causative variables, the temperature and maximum shear stress showed the maximum influence on void formation.

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Phased Array Ultrasonic Testing for Post-Weld and OnLine Detection of Friction Stir Welding Defects

Cited by 30 publications

References 27 publications

Using a New Magnetic Flux Leakage Method to Detect Tank Bottom Weld Defects

Using a New Magnetic Flux Leakage Method to Detect Tank Bottom Weld Defects

Detection of Surface Defects in Friction Stir Welded Joints by Using a Novel Machine Learning Approach

Conditions for void formation in friction stir welding from machine learning

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