Prediction of the surface quality of friction stir welds by the analysis of process data using Artificial Neural Networks

Keyphrases

• Artificial Neural Network 100%
• Surface Quality 100%
• Process Data 100%
• Friction Stir Welding 100%
• Convolutional Neural Network 60%
• Spindle Torque 60%
• Human Visual 40%
• Visual Inspection 40%
• Welding Surface 40%
• Topographic Analysis 40%
• Recurrent Neural Network 20%
• Neural Network Architecture 20%
• Force Signal 20%
• Sampling Rate 20%
• Fully Convolutional Neural Network 20%
• High Classification Accuracy 20%
• Rotational Speed 20%
• Surface Defects 20%
• Welding Speed 20%
• Aluminum Alloy 20%
• EN AW-6082 20%
• Joining Technology 20%
• Tool Shoulder 20%
• Friction Stir Welding Process 20%
• Weld Quality 20%
• Advanced Joining 20%
• Parameter Optimization Method 20%
• Flash Formation 20%
• Tool Probe 20%
• Surface Topography Analysis 20%
• Welding Direction 20%
• Quality Feed 20%
• AA6082-T6 20%

Engineering

• Surface Quality 100%
• Welds 100%
• Artificial Neural Network 100%
• Friction Stir Welding 100%
• Convolutional Neural Network 60%
• Visual Inspection 40%
• Feedforward 20%
• Optimization Method 20%
• Classification Accuracy 20%
• Force Signal 20%
• Process Variable 20%
• Rotational Speed 20%
• Joining Technology 20%
• Sampling Rate 20%
• Surface Topography 20%
• Recurrent Neural Network 20%
• Surface Defect 20%

Material Science

• Surface Property 100%
• Friction Stir Welding 100%
• Surface (Surface Science) 60%
• Surface Defect 20%
• Surface Topography 20%
• Aluminum Alloy 20%