Abstract
An unsupervised approach to classify surface defects in wire rod manufacturing is developed in this paper. The defects are extracted from an eddy current signal and classified using a clustering technique that uses the dynamic time warping distance as the dissimilarity measure. The new approach has been successfully tested using industrial data. It is shown that it outperforms other classification alternatives, such as the modified Fourier descriptors.
Highlights
Wire rods made using the hot rolling technique can present surface defects
This has been a very tedious and time-consuming task, because it required unwinding long wire rod coils, searching the surface defects by visual inspection and classifying and putting them in correspondence with the signal recorded by the eddy current inspection system
The corresponding eddy current signals associated with them have been represented in the complex impedance plane and labeled as defects belonging to Classes A, B, C and D, respectively
Summary
Wire rods made using the hot rolling technique can present surface defects. Several techniques have been applied to detect the surface defects that appear during wire rod manufacturing. Approaches based on image processing have been proposed in [1,2]. To computer vision-based techniques, the eddy current nondestructive technique is effectively used to detect surface defects [3]. The basic instrument for eddy current inspection is a coil fed with an alternating electric current. The complex impedance of the coil Z0 changes in accordance with the eddy current redistribution due to material defects or inhomogeneities [4,5]
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