Defect identification in friction stir welding using discrete wavelet analysis

Kumar, Ujjwal; Yadav, Inderjeet; Kumari, Shilpi; Kumari, Kanchan; Ranjan, N. S.; Kesharwani, Ram Kumar; Jain, Rahul; Kumar, Sachin; Pal, Srikanta; Chakravarty, Debasish; Pal, Surjya K.

doi:10.1016/j.advengsoft.2015.02.001

Cited by 64 publications

(22 citation statements)

References 16 publications

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“…The size of the tunneling discontinuity decreased significantly by increasing the plunge depth. Kumar et al (2015) discussed discontinuity detection during FSW using discrete wavelet transform of force and torque signals. Their work showed that the discontinuities present in friction stir welds produce sudden changes in the features of the force signals.…”

mentioning

confidence: 99%

Force measurement-based discontinuity detection during friction stir welding

Shrivastava

Zinn

Duffie

et al. 2017

Journal of Manufacturing Processes

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The objective of this work is to develop a method for detecting the creation of discontinuities (i.e., voids, volume defects) during friction stir welding. Friction stir welding is inherently cost effective, however, the need for significant weld inspection can make the process cost prohibitive. A new approach to weld inspection is required in which an in-situ characterization of weld quality can be obtained, reducing the need for post-process inspection. To this end, friction stir welds with subsurface voids and without voids were created. The subsurface voids were generated by reducing the friction stir tool rotation frequency and increasing the tool traverse speed to create "colder" welds. Process forces were measured during welding, and the void sizes were measured post-process by computerized tomography (i.e., 3D X-ray imaging). Two parameters, based on frequency domain content and timedomain average of the force signals, were found to be correlated with void size. Criteria for subsurface void detection and size prediction were developed and shown to be in good agreement with experimental observations. With the proper choice of data acquisition system and frequency analyzer the occurrence of subsurface voids can be detected in real time.

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mentioning

confidence: 99%

Force measurement-based discontinuity detection during friction stir welding

Shrivastava

Zinn

Duffie

et al. 2017

Journal of Manufacturing Processes

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“…[9] & fig. [10] in addition to this it is more beneficial to use conical pin shape plunge as its produce higher material velocity as compared to cylindrical shape plunge and they found advancing side having more plastic strain as compare to retreating side which indicates asymmetry nature of the FSW process. [11].…”

Section: Forces and Torquementioning

confidence: 99%

“…Ujjwal et al 2015 [10] have studied Defect identification in friction stir welding using discrete wavelet analysis to evaluate the transformation of Force and torque signal. For this purpose, aluminum alloy AA1100 are used whose specification was 200mm × 80mm × 2.5mm.…”

Section: Defect Identificationmentioning

confidence: 99%

A Review paper on Friction Stir Welding (FSW)

Dewangan¹,

Chakrawarti²

2020

RJET

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Friction Stir Welding (FSW) is a new solid-state welding technique which finds application in various industries. It involves the joining of metals without any help of fusion or filler material. The joint between two metals can be formed through the combined action of frictional heating and mechanical deformation which is developed due to a rotating tool. The material that flows around the tool undergoes an extreme level of plastic deformation. Rotation generates enough friction and the friction generates sufficient heat to plastify the material. FSW is handy for welding aluminum alloys. However, interest is growing in utilizing the process in a wider range of applications that also employ non aero engine metallic materials. Therefore, it is the objective of this to provide a broad view of the capabilities of the FSW process for joining metals. This review article covers various work performed on friction stir welding, and the basics of the process and the fundamental aspects of operating an FSW. This includes a description of the different parameters that have been involved in this process. And also studied the material flow, forces, and other output responses, including defects, and strain rates that is produced.

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“…It was observed from the microscopic examinations and fracture locations of the weld that plastic material flow can be changed by varying tool tilt angle. Kumar et al [9] used discrete wavelet transform on force and torque signals for detecting the fault occurred during Friction Stir Welding process.…”

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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Defect identification in friction stir welding using discrete wavelet analysis

Cited by 64 publications

References 16 publications

Force measurement-based discontinuity detection during friction stir welding

Force measurement-based discontinuity detection during friction stir welding

A Review paper on Friction Stir Welding (FSW)

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

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