Using Machine Learning to Predict the Wear Rate of Corundum-Filled LM30 Aluminum Composites at High Temperatures

Engineering

• Aluminum Oxide 100%
• Durables 25%
• Experimental Trial 25%
• Learning System 100%
• Mean Absolute Error 25%
• Predictive Ability 25%
• Radial Basis Function 25%
• Root Mean Square Error 25%
• Rotors 25%
• Support Vector Machine 25%
• Wear Property 25%
• Wear Resistance 25%

Material Science

• Aluminum Alloy 33%
• Brakes 33%
• Composite Material 100%
• Materials Design 33%
• Wear Property 33%
• Wear Resistance 33%

Chemical Engineering

• Corundum 100%
• Learning System 100%
• Support Vector Machine 25%