Overview

When parametric optimization is performed on a complex model with many CAD parameters, a geometry collapse is likely to occur if the CAD parameters are set too wide, resulting in a low rate of effective geometry generation. Narrowing the range also narrows the design space, and a globally optimal solution may not be obtained.
JMAG has a function that uses a GA (Genetic Algorithm: Generation Algorithm) to search for an appropriate range of CAD parameters, making it possible to create valid geometry in a short time. A tool is also provided to evaluate whether the model is valid for this function before performing the optimization.
This document explains how to use this useful function to search for parameter ranges that reduce geometry collapse during optimization calculations.

Keywords

Geometry collapse, Optimization, CAD parameter, GA, Geometry, Dimension, Preinstalled script, Python, Range finder, Geometry evaluation