Additively Manufactured Component Characterization by Machine Learning from Resonance Inspection Techniques

Keyphrases

• Material Properties 100%
• Machine Learning 100%
• Inspection Technique 100%
• Resonant Ultrasound Spectroscopy 100%
• Additive Manufactured Parts 100%
• Component Characterization 100%
• Additive Manufacturing 66%
• Non-destructive 33%
• Genetic Algorithm 33%
• Porosity 33%
• Machine Learning Techniques 33%
• Material Parameters 33%
• Young's Modulus 33%
• Low-cost Method 33%
• Dimensionality Reduction 33%
• Modulus Ratio 33%
• Poisson's Ratio 33%
• Three-parameter 33%
• Random Forest Regression 33%
• Finite Element Analysis Simulation 33%
• Resonance Peak 33%
• Mode Identification 33%
• Aerospace 33%
• Non-destructive Method 33%
• Resonance Spectra 33%
• Laser Powder Bed Fusion (L-PBF) 33%
• National Interests 33%
• Part Qualification 33%
• Print Quality 33%

Engineering

• Genetic Algorithm 100%
• Learning System 100%
• Young's Modulus 50%
• Material Parameter 50%
• Poisson Ratio 50%
• Potential Application 50%
• Machine Learning Technique 50%
• Random Forest 50%
• Powder Bed Fusion 50%
• Finite Element Analysis 50%
• Porosity 50%

Material Science

• Materials Property 100%
• Density 33%
• Finite Element Method 33%
• Young's Modulus 33%
• Poisson Ratio 33%
• Laser Powder Bed Fusion 33%