Comparative Analysis among Discrete Fourier Transform, K-Means and Artificial Neural Networks Image Processing Techniques Oriented on Quality Control of Assembled Tires

Abstract

The present paper discusses a comparative application of image processing techniques, i.e., Discrete Fourier Transform, K-Means clustering and Artificial Neural Network, for the detection of defects in the industrial context of assembled tires. The used Artificial Neural Network technique is based on Long Short-Term Memory and Fully Connected neural networks. The investigations focus on the monitoring and quality control of defects, which may appear on the external surface of tires after being assembled. Those defects are caused from tires which are not properly assembled to their respective metallic wheel rim, generating deformations and scrapes which are not desired. The proposed image processing techniques are applied on raw high-resolution images, which are acquired by in-line imaging and optical instruments. All the described techniques, i.e., Discrete Fourier Transform, K-Means clustering and Long Short-Term Memory, were able to determine defected and acceptable external tire surfaces. The proposed research is taken in the context of an industrial project which focuses on the development of automated quality control and monitoring methodologies, within the field of Industry 4.0 facilities. The image processing techniques are thus meant to be adopted into production processes, giving a strong support to the in-line quality control phase.

Analysis of the Possibilities of Tire-Defect Inspection Based on Unsupervised Learning and Deep Learning

“…A comparison of the calculated radii is illustrated in Figure 8 (left). There were significant errors in the computed green (6) and blue (7) curves, compared with the red curve, in the calculated radius values, as shown on the left side of Figure 8. Solving for the radius is possible through empirical or analytical methods, where empirical methods are based on a manually entered radius value with the main goal of approximating the computed curves to red curves as closely as possible.…”

“…The shapes of the curves were displaced in the x-axis, caused by the method for gaining the 𝑥 value, due to the uneven character of the curve. The manual corrections were carried out by displacing the 𝑥 value for ( 9), the renumbering of 𝜑 by a value in (6), and renumbering of 𝜑 by a value in (4). The results after such replacements are shown in Figure 9 (right), with the displacement values for specific curves.…”

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