A Machine Learning Tool to Monitor and Forecast Results from Testing Products in End-of-Line Systems

The massive industrialization of products in a factory environment requires testing the product at a stage before its exportation to the sales market. For example, the end-of-line tests at Continental Advanced Antenna contribute to the validation of an antenna’s functionality, a product manufactured...

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Bibliographic Details
Published in:Applied sciences 2023-02, Vol.13 (4), p.2263
Main Authors: Nunes, Carlos, Nunes, Ricardo, Pires, E. J. Solteiro, Barroso, João, Reis, Arsénio
Format: Article
Language:English
Subjects:
Algorithms
Anomalies
Antennas
Artificial intelligence
Automation
data analysis
Data mining
Design
end-of-line testing
Fault detection
Forecasts and trends
Industrial development
industry
Information storage
Learning algorithms
Machine learning
Manufacturing
Predictive maintenance
Product quality
Product testing
Production lines
Quality assurance
Quality control
Research methodology
Science
Trends
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Summary:The massive industrialization of products in a factory environment requires testing the product at a stage before its exportation to the sales market. For example, the end-of-line tests at Continental Advanced Antenna contribute to the validation of an antenna’s functionality, a product manufactured by this organization. In addition, the storage of information from the testing process allows the data manipulation through automated machine learning algorithms in search of a beneficial contribution. Studies in this area (automatic learning/machine learning) lead to the search and development of tools designed with objectives such as preventing anomalies in the production line, predictive maintenance, product quality assurance, forecast demand, forecasting safety problems, increasing resources, proactive maintenance, resource scalability, reduced production time, and anomaly detection, isolation, and correction. Once applied to the manufacturing environment, these advantages make the EOL system more productive, reliable, and less time-consuming. This way, a tool is proposed that allows the visualization and previous detection of trends associated with faults in the antenna testing system. Furthermore, it focuses on predicting failures at Continental’s EOL.
ISSN:2076-3417
2076-3417
DOI:10.3390/app13042263