Advanced Displacement Magnitude Prediction in Multi-Material Architected Lattice Structure Beams Using Physics Informed Neural Network Architecture
This addresses deformation prediction for multi-material lattice beams in engineering applications, but it is incremental as it combines existing PINN and finite element analysis.
The paper tackled predicting deformation in architected lattice structures by proposing a Physics-Informed Neural Network (PINN) method, which achieved higher accuracy than linear regression with an R-square of 0.7923 versus 0.5686 and lower MSE of 0.00017417 versus 0.00036187.
This paper proposes an innovative method for predicting deformation in architected lattice structures that combines Physics-Informed Neural Networks (PINNs) with finite element analysis. A thorough study was carried out on FCC-based lattice beams utilizing five different materials (Structural Steel, AA6061, AA7075, Ti6Al4V, and Inconel 718) under varied edge loads (1000-10000 N). The PINN model blends data-driven learning with physics-based limitations via a proprietary loss function, resulting in much higher prediction accuracy than linear regression. PINN outperforms linear regression, achieving greater R-square (0.7923 vs 0.5686) and lower error metrics (MSE: 0.00017417 vs 0.00036187). Among the materials examined, AA6061 had the highest displacement sensitivity (0.1014 mm at maximum load), while Inconel718 had better structural stability.