Determination of the Cutting-Edge Microgeometry Based on Process Forces during Peripheral Milling of Ti-6Al-4V Using Machine Learning

Keyphrases

• Machine Learning 100%
• Residual Stress State 100%
• Ti-6Al-4V (Ti64) 100%
• Process Forces 100%
• Tool Wear 100%
• Peripheral Milling 100%
• Cutting Edge Microgeometry 100%
• Machine Learning Algorithms 50%
• Root Mean Square Error 50%
• In-process 50%
• Cutting Force 50%
• Tribological Properties 50%
• Surface Influence 50%
• Fatigue Life 50%
• Milling Tests 50%
• Cutting Edge Geometry 50%
• Service Quality Indicators 50%
• Tool Wear Prediction 50%
• Cutting Edge Rounding 50%
• Rounded Cutting Edge 50%
• Machining-induced Residual Stress 50%
• Forces Behavior 50%
• Hidden Values 50%

Engineering

• Cutting Edge 100%
• Ti-6al-4v 100%
• Learning System 100%
• Stress State 33%
• Residual Stress 33%
• Root Mean Square Error 16%
• Cutting Force 16%
• Process Parameter 16%
• Subsurface 16%
• Induced Residual Stress 16%
• Fatigue Life 16%
• Machine Learning Algorithm 16%