Defect classification of radius shaping in the tire curing process using Fine-Tuned Deep Neural Network

Zendi Iklima, Bugi Nur Rohman, Rahmat Muwardi, Asif Khan, Zody Arifiansyah


The curing process or vulcanization process is the final stage of the tire manufacturing process, where the properties of the tire compound change from rubber-plastic material to become elastic by forming cross-links in its molecular structure. The green tire is formed in the curing process, which is placed on the bottom mould. The inside of the green tire surrounds the bladder. The top mould will close to carry out the next curing process. In closing the mould, there is a shaping process of forming a green tire placed on the bladder and given a proportional pressure. Improper or abnormal radius shaping results cause seventy percent of product defects. This paper proposed abnormal detection of radius shaping in the curing process using Fine-tuned Deep Neural Network (DNN). Several DNN models have been examined to analyze an optimized DNN model for abnormal detection of radius shaping in the curing process. The fine-tuned DNN architecture has been exported for the curing system. The DNN was trained with a training accuracy of 97.88%, a validation accuracy of 95%, a testing accuracy of 100%, and a loss of 4.93%.


Abnormal Detection; Deep Neural Network; Fined Tuned; Radius Shaping;

Full Text:




  • There are currently no refbacks.

Published by:
Fakultas Teknik Universitas Mercu Buana
Jl. Raya Meruya Selatan, Kembangan, Jakarta 11650
Tlp./Fax: +62215871335
p-ISSN: 1410-2331
e-ISSN: 2460-1217
Journal URL:
Journal DOI: 10.22441/sinergi

Creative Commons License

Journal by SINERGI is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License

Analytics Made Easy - StatCounter
View My Stats

The Journal is Indexed and Journal List Title by: