In this engineering study, YantriX investigated machine learning surrogate models for accelerating mechanical design optimization—evaluating regression networks on finite element datasets for interactive full-field stress prediction during early concept sizing.
Overview
Engineering research study evaluating deep learning surrogate models to predict structural stress fields in tens of milliseconds—accelerating iterative CAD parameter sweeps.
Discipline: Simulation & Validation
Project Type: Simulation & AI (Machine Learning Surrogate for FEA Study)
Application: Advanced Simulation & Engineering Computation
YantriX Scope Ownership
Key specifications
Objectives
Challenge
Full finite element solves take minutes or hours per iteration, creating a major bottleneck when exploring hundreds of geometric permutations during early concept sizing. This research study explored whether neural network surrogates can approximate stress fields with sufficient accuracy to provide instantaneous design feedback during CAD modeling.
Approach
Developed Python automation scripts driving parametric geometry variation and mesh generation in ANSYS.
Trained a convolutional graph neural network mapping geometric point clouds and boundary conditions to nodal stresses.
Evaluated model predictions on unseen test geometries, tracking mean absolute percentage error.
Evaluated inference latency on workstation GPU hardware versus traditional numerical solvers.
Outcomes
Deliverables
Tools used
Impact
Conclusion
Surrogate models do not replace rigorous finite element solvers—they augment them by making early design exploration fast and interactive, leaving full solvers for final qualification.
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Tagged
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