Characterisation of friction stir welds by logistic regression using fractal and wavelet features

ABSTRACT

This paper presents a method for automatic detection of defective welds based on feature extraction using fractal analysis and wavelet transform with Logistic Regression classifier. The proposed methodology consists of pre-processing, Weld seam extraction, and feature extraction followed by classification process. The image obtained from the welded region is converted to grayscale, followed by binarisation under pre-processing. The process of Weld seam extracts the region of interest (ROI), from which features are obtained that describe the texture and irregularities present in the welded image. The simulation study analyses Friction Stir Welding images of two different aluminium grades (AA1100 with AA6061-T6) and that of brass and copper. In the experiments, a Logistic Regression model to perform classification using the features is employed. This is the first work where Logistic Regression has been applied for weld defect detection in Friction Stir Welding. Experimental results show the effectiveness of the proposed classification process which uses less number of powerful features. The classifier has achieved more than 97% accuracy, indicating potential use of the proposed method for the detection of defects in the friction stir welded images.

KEYWORDS:

• Friction stir welding
• fractal dimension
• lacunarity
• wavelet transform
• linear regression classification
• defect

Disclosure statement

No potential conflict of interest was reported by the authors.