首页|Reports Outline Machine Learning Findings from Polytechnic University Milan (Exploratory Analysis and Evolutionary Computing Coupled Machine Learning Algorithms for Modelling the Wear Characteristics of Az31 Alloy)
Reports Outline Machine Learning Findings from Polytechnic University Milan (Exploratory Analysis and Evolutionary Computing Coupled Machine Learning Algorithms for Modelling the Wear Characteristics of Az31 Alloy)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews - Investigators discuss new findings in Machine Learning. According to news originating from Milan,Italy, by NewsRx correspondents, research stated, “The wear resistance of magnesium alloys is one of itskey technological properties that could limit their practical application. In accordance with ASTM G99-95astandard, this study used a pin-on-disc method to analyze the wear behavior of ascast AZ31 magnesiumalloy under dry-sliding conditions.”
MilanItalyEuropeAlgorithmsCyborgsEmerging TechnologiesLight MetalsMachine LearningMagnesiumParticle Swarm OptimizationPolytechnic University Milan
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