Asian Journal of Research in Social Sciences and Humanities
Year : 2016, Volume : 6, Issue : 11
First page : ( 1190) Last page : ( 1198)
Online ISSN : 2249-7315.
Article DOI : 10.5958/2249-7315.2016.01262.4

Defect Analysis in Friction Stir Welding Process using Wavelet Transformation Technique

Rabi J*, Balusamy T**

*Research Scholar, Department of Mechanical Engineering, V.M.K Engineering College, Salem, India

**Professor, Department of Mechanical Engineering, Government College of Engineering, Salem, India

Online published on 9 November, 2016.

Abstract

Vibration sources are found in machining operations, which can predict the machining characteristics and operating conditions. These sources were found in mixed form with machine spindle excitation and external disturbances. This contaminates the monitoring predictability. This study deals with the independent component analysis to separate the source signals for identifying the pre-induced defects in the friction stir welding process. Experiments were carried out for defect induced and defect free work piece of Al6082 aluminum alloy with the acquisition of vibration signals during the friction stir welding process. These signals were analyzed using the proposed method with the decomposition of the acquired mixed component signals using continuous wavelet transform called pseudo channel signals. These signals were used to acquire the independent component signals by ICA analysis. This signal is denoised using discrete wavelet transform to achieve the redundant signal with the removal of white Gaussian noise. Modal analysis is carried using finite element method (FEM) to extract the natural frequency of the source components, which impacts the welding process. The projected technique is used for examining and diagnosing the tunnel defects in the FSW process. It is established that the separated source components successfully expose the defects induced in the aluminum alloy work-piece. This proves the separation process to be more robust and accurate for Friction stir welding based process monitoring.

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