Automated 2D and 3D finite element overclosure adjustment and mesh morphing using generalized regression neural networks

• Novel algorithms for mesh morphing and overclosure adjustment are introduced. • Algorithms operate on 2D and 3D elements with triangular or quadrilateral faces . • Algorithms use a set of four processes and a generalized regression neural network . • Generalized regression neural networks offer better performance than existing tools. • Implementation of the algorithms are publicly available. 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