首页|New Machine Learning Study Results Reported from Faculty of Sciences and Technol ogy (Machine Learning Application for Wear Rate Prediction of Wc/co-based Cermet With Different Content of Ni, Cr, Tic, Tac, and Nbc)
New Machine Learning Study Results Reported from Faculty of Sciences and Technol ogy (Machine Learning Application for Wear Rate Prediction of Wc/co-based Cermet With Different Content of Ni, Cr, Tic, Tac, and Nbc)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News – Researchers detail new data in Machine Learning. According to news reporting originatingin Souk Ahras, Algeria, by Ne wsRx journalists, research stated, “Wear rate of WC/Co-based cermetmaterials un der severe tribological conditions is a critical thermomechanical property that can limit thepractical application of various tools and industrial machinery. I n this paper, three machine learning (ML)algorithms including Random Forest, Gr adient Boosting Regressor, and XGBoost are employed to predictthe wear rate of WC/Co-based cermets elaborated through powder metallurgy, utilizing dry friction undersevere pin-on-disk conditions at elevated temperatures.”
Souk AhrasAlgeriaCyborgsEmerging T echnologiesMachine LearningFaculty of Sciences and Technology
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