Machine learning algorithms applied to intelligent tyre manufacturing

Intelligent manufacturing is a way to expand industrial manufacturing by integrating artificial intelligence and device technologies to provide great solutions to solve complex problems and improve industrial processes. Artificial intelligence has been used in intelligent manufacturing for monitorin...

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Bibliographic Details
Published in:International journal of computer integrated manufacturing 2024-04, Vol.37 (4), p.497-507
Main Authors: Acosta, Simone Massulini, Oliveira, Rodrigo Marcel Araujo, Sant'Anna, Ângelo Márcio Oliveira
Format: Article
Language:English
Subjects:
Artificial intelligence
industrial process
intelligent manufacturing
machine learning
tyre
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Summary:Intelligent manufacturing is a way to expand industrial manufacturing by integrating artificial intelligence and device technologies to provide great solutions to solve complex problems and improve industrial processes. Artificial intelligence has been used in intelligent manufacturing for monitoring and optimization processes, focusing on improving efficiency. This paper examines the predictive performance of six machine learning algorithms for modeling tyre weight in smart tire manufacturing from real data. The main contribution of this research is developing a scheme solution that uses machine learning algorithms to industrial processes in stored data large manufacturing processes, allowing the process engineer to manage the finished products and the process parameters. The proposed relevance vector machine is compared with other algorithms such as support vector machine, artificial neural network, k-nearest neighbors, random forest, and model trees. RVM algorithm presented the smallest measures of squared error and better performance than the other algorithms. This novel approach accurately predicts tyre weight patterns during production using machine learning algorithms to analyze relevant features and detect anomalies based on predicted process data.
ISSN:0951-192X
1362-3052
DOI:10.1080/0951192X.2023.2177734