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.
Highlights
Within the automotive field, the assembling process between the tire and its corresponding wheel rim represents an industrial activity which requires highly engineered technologies
The paper is focused on the application of image processing techniques used to detect possible defects obtained after the tire-wheel rim assembling process
The comparison of the Discrete Fourier Transform (DFT), K-Means and Long Short-Term Memory-Fully Connected (LSTM-FC) neural network algorithms reveal the possibility to in-line monitor and identify the produced defects
Summary
The assembling process between the tire and its corresponding wheel rim represents an industrial activity which requires highly engineered technologies Such technologies are continuously improving to enhance the efficiency in production. Starting from the analysis of the tire-wheel rim assembling process [4], image processing techniques can provide important insights about possible defects, visible as patterns, which may occur on the external surface of the tire [5]. Those analyses are combined with the Ethernet network [6], as enabling Industry 4.0 technology. Many companies are implementing the combination of Ethernet systems and Internet of Things (IoT) [7] to Information 2020, 11, 257; doi:10.3390/info11050257 www.mdpi.com/journal/information
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
