Abstract
Due to the limitations of production conditions, there is a certain probability that workpiece product has internal defects, which will have a certain impact on the performance of workpiece. Therefore, the internal defects detection of workpiece is essential. This study proposed a defect recognition method based on industrial computed tomography (CT) image to identify the internal defects of workpiece. The block fractal algorithm was used to locate the defect parts of the image, then the improved k-means clustering algorithm was used to segment the defect parts, and feature vector was extracted by Hu invariant moments. Finally, the firefly algorithm and radial basis function (RBF) neural network were combined to identify the defect. It was found from the experiments that the algorithm in this study had the accuracy of 97.89%, which proved the reliability of the algorithm and provided some suggestions for the defects recognition.
Highlights
Defect detection plays a very important role in the industrial field
Liu et al [4] optimized subtractive clustering method (SCM) by Akaike information criterion (AIC) and constructed radial basis function (RBF) model by using the obtained AIC-SCM algorithm, which improved the adaptability of the RBF model
In this study, based on the industrial computed tomography (CT) image, the defect was obtained through the localization and segmentation of the defect image, the feature extraction was conducted by using Hu invariant moments, and the RBF neural network which was optimized by using the firefly algorithm was used for recognizing defects to explore the reliability of this method in defect recognition
Summary
Defect detection plays a very important role in the industrial field. Through defect detection, product quality can be effectively improved. Alimohamadi et al [1] proposed a new defect detection method based on the optimal Gabor wavelet filters, which combined with morphological analysis. Chen et al [2] proposed a new defect detection method based on dual-tree complex wavelet transform (DT-CWT) and took advantage of near shift-invariance of DT-CWT to extract weak defect feature. In this study, based on the industrial CT image, the defect was obtained through the localization and segmentation of the defect image, the feature extraction was conducted by using Hu invariant moments, and the RBF neural network which was optimized by using the firefly algorithm was used for recognizing defects to explore the reliability of this method in defect recognition
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