Online monitoring of friction stir welding (FSW) is inevitable due to the increasing demand of this process. Also the machine vision system has industrial importance for monitoring of manufacturing processes due to its non-invasiveness and flexibility. Therefore, in this research, an attempt has been made to monitor friction stir welding process by analyzing the weld surface images. Here, discrete wavelet transform has been applied on FSW images to extract useful features for describing the good and defective weld. These obtained features have been fed to support vector machine based classification model for classifying good and defective weld with 99\% and 97\% accuracy with Gaussian and polynomial kernel, respectively. © 2015 The Society of Manufacturing Engineers. Published by Elsevier Ltd. All rights reserved.

Bhat N.N.

Kumari K.

Dutta S.

Pal S.K.

Pal S.

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Shiv Nadar University

Journal of Manufacturing Processes

Friction stir welding (FSW) is a new entrant in welding technology and getting a defect-free weld is the final objective. But different defects are generated due to various reasons and needs to be analyzed to eliminate them. The aim of the research work is to identify and classify different kinds of surface defects generally encountered during the FSW process using digital image processing techniques. The defects on the surface of the weld are identified using image pyramid and image reconstruction algorithms. Further, using these algorithms the defects can be classified into voids, grooves, cracks, key-hole and flash with the help of unique features of each kind of defect. Vertical intensity plot and the area plot of the defect blobs are represented for the proper localization and analysis of severity of defects. © 2016 The Society of Manufacturing Engineers. Published by Elsevier Ltd. All rights reserved.

Classification and identification of surface defects in friction stir welding: An image processing approach

This article discusses on the detection of fault occurred during friction stir welding using discrete wavelet transform on force and torque signals. The work pieces used were AA1100 aluminum alloys of thickness 2.5 mm. The plates were 200 mm in length and 80 mm in width. Presence of defect in welding causes sudden change in force signals (Z-load), thus it is easier to detect such abrupt changes in a signal using discrete wavelet transform. Statistical features like variance and square of errors of detail coefficients are implemented to localize the defective zone properly as it shows better variations (in defective area) than the detail coefficient itself. © 2015 Elsevier Ltd. All rights reserved.

Advances in Engineering Software

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CIRP Journal of Manufacturing Science and Technology

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