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

• 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
• ISSN: 2076-3417; 2076-3417
• DOI: 10.3390/app13042263

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.