Automated 2D and 3D Finite Element Overclosure Adjustment and Mesh Morphing Using Generalized Regression Neural Networks

TL;DR

This paper presents novel algorithms using Generalized Regression Neural Networks for mesh morphing and overclosure adjustment in 3D geometries, improving performance over existing RBF-based methods, with implementations available publicly.

Contribution

Introduction of two new GRNN-based algorithms for mesh morphing and overclosure correction, surpassing RBF-based techniques in efficiency and accuracy.

Findings

Validated algorithms against existing methods with improved results

Demonstrated performance gains in test cases

Provided open-source MATLAB implementations

Abstract

Computer representations of three-dimensional (3D) geometries are crucial for simulating systems and processes in engineering and science. In medicine, and more specifically, biomechanics and orthopaedics, obtaining and using 3D geometries is critical to many workflows. However, while many tools exist to obtain 3D geometries of organic structures, little has been done to make them usable for their intended medical purposes. Furthermore, many of the proposed tools are proprietary, limiting their use. This work introduces two novel algorithms based on Generalized Regression Neural Networks (GRNN) and 4 processes to perform mesh morphing and overclosure adjustment. These algorithms were implemented, and test cases were used to validate them against existing algorithms to demonstrate improved performance. The resulting algorithms demonstrate improvements to existing techniques based on Radial Basis Function (RBF) networks by converting to GRNN-based implementations. Implementations in MATLAB of these algorithms and the source code are publicly available at the following locations: https://github.com/thor-andreassen/femors https://simtk.org/projects/femors-rbf https://www.mathworks.com/matlabcentral/fileexchange/120353-finite-element-morphing-overclosure-reduction-and-slicing