Anzeige
Erschienen in:
2019 | OriginalPaper | Buchkapitel
Supervised Learning Approach for Surface-Mount Device Production
verfasst von : Eva Jabbar, Philippe Besse, Jean-Michel Loubes, Nathalie Barbosa Roa, Christophe Merle, Rémi Dettai
Erschienen in: Machine Learning, Optimization, and Data Science
Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.
Wählen Sie Textabschnitte aus um mit Künstlicher Intelligenz passenden Patente zu finden. powered by
Markieren Sie Textabschnitte, um KI-gestützt weitere passende Inhalte zu finden. powered by
Abstract
In this paper, we propose a decision-making tool based on supervised learning techniques that detects defects and proposes to the Surface-Mount Technology (SMT) operator a probability of being a false call. In this work, we compare four tree-based learning methods. The result of our experiments shows that a XGBoost model trained with our real-world dataset can accurately classify most real defects and false calls with an accuracy score of about 99.4% and a recall of about 98.6%. Moreover, we investigated the computing time of our prediction model and concluded that integration of our classification tool based on the XGBoost algorithm is realistic and feasible in the SMT production line. We believe that our tool will significantly improve the daily work of the SMT verify operator.