Affiliation:
1. Faculty of Computer Science, Kazimierz Wielki University, Chodkiewicza 30, 85-064 Bydgoszcz, Poland
Abstract
The tire industry plays a key role in ensuring safe and efficient transportation. With 1.1 billion vehicles worldwide relying on tires for optimum performance, tire quality control is of paramount importance. In recent years, the integration of artificial intelligence (AI) has revolutionized various industries, and the tire industry is no exception. In this article, we take a look at the current state of quality control in the tire industry and the transformative impact of AI on this crucial process. Automatic detection of tire defects remains an important and challenging scientific and technical problem in industrial tire quality control. The integration of artificial intelligence into tire quality control has the potential to transform the tire industry, leading to safer, more reliable, and more sustainable tires. Thanks to continuous progress and a proactive approach to challenges, the tire industry is prepared for a future in which artificial intelligence will play a key role in delivering high-quality tires to consumers around the world.
Funder
Kazimierz Wielki University
Subject
Fluid Flow and Transfer Processes,Computer Science Applications,Process Chemistry and Technology,General Engineering,Instrumentation,General Materials Science
Reference47 articles.
1. Horng, M.-F., Kung, H.-Y., Chen, C.-H., and Hwang, F.-J. (2020). Deep Learning Applications with Practical Measured Results in Electronics Industries. Electronics, 9.
2. Chang, C.-Y., Srinivasan, K., Wang, W.-C., Ganapathy, G.P., Vincent, D.R., and Deepa, N. (2020). Quality Assessment of Tire Shearography Images via Ensemble Hybrid Faster Region-Based ConvNets. Electronics, 9.
3. Shearography and its applications—A chronological review;Sirohi;Light Adv. Manuf.,2022
4. Applications of Shearography for Non-Destructive Testing and Strain measurement;Int. J. Comb. Optim. Probl. Inform.,2020
5. Chang, C.-Y., Su, Y.-D., and Li, W.-Y. (2022). Tire Bubble Defect Detection Using Incremental Learning. Appl. Sci., 12.
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