Accelerated process parameter selection of polymer-based selective laser sintering via hybrid physics-informed neural network and finite element surrogate modelling

Keyphrases

• Finite Element 100%
• Polymer-based 100%
• Physics-informed Neural Networks 100%
• Selective Laser Sintering 100%
• Surrogate Model 100%
• Network Elements 100%
• Process Parameter Selection 100%
• Finite Element Method 66%
• Computation Time 66%
• Hybrid Model 66%
• Thermo-fluid Dynamics 66%
• Fluid Dynamics Simulation 66%
• Cooling Rate 33%
• Cooling Phase 33%
• High Viscosity 33%
• Finite Element Model 33%
• Heat Conduction Model 33%
• Parametric Variation 33%
• Thermal Gradient 33%
• Simulation Model 33%
• Slow Evolution 33%
• Product Quality 33%
• Manufacturing Process 33%
• Temperature Field 33%
• Material Degradation 33%
• Liquid Flow 33%
• Sintering Process 33%
• Neural Network Model 33%
• Sintering Temperature 33%
• Melt Pool Geometry 33%
• Transient Evolution 33%
• Time-varying Demand 33%
• Melt Pool Size 33%
• Lack-of-fusion 33%
• Low Conductivity 33%
• Multi-track 33%
• Partial Differential Equation Solvers 33%
• Tracking Simulation 33%
• Region Formation 33%

Engineering

• Finite Element Method 100%
• Process Parameter 100%
• Selective Laser Sintering 100%
• Network Element 100%
• Computer Simulation 66%
• Computational Time 66%
• Fluid Dynamics 66%
• Thermal Fluid 66%
• Hybrid Model 66%
• Melt Pool 66%
• Partial Differential Equation 33%
• Transients 33%
• Temperature Field 33%
• Finite Element Modeling 33%
• Thermal Gradient 33%
• Manufacturing Process 33%
• Product Quality 33%
• Liquid Flow 33%
• Materials Degradation 33%
• Artificial Intelligence 33%
• Pool Size 33%
• Sintering 33%
• Sintering Process 33%
• Cooling Rate 33%
• Sintering Temperature 33%

Material Science

• Finite Element Method 100%
• Surrogate Modeling 100%
• Selective Laser Sintering 100%
• Sintering 66%
• Fluid Dynamics 66%
• Sintering Temperature 33%
• Finite Element Modeling 33%
• Materials Degradation 33%