Case‑based tuning of a metaheuristic algorithm exploiting sensitivity analysis and design of experiments for reverse engineering applications
Article dans une revue avec comité de lecture
Author
Date
2022-03-17Journal
Engineering with ComputerAbstract
Due to its capacity to evolve in a large solution space, the Simulated Annealing (SA) algorithm has shown very promising
results for the Reverse Engineering of editable CAD geometries including parametric 2D sketches, 3D CAD parts and assem blies. However, parameter setting is a key factor for its performance, but it is also awkward work. This paper addresses the
way a SA-based Reverse Engineering technique can be enhanced by identifying its optimal default setting parameters for the
ftting of CAD geometries to point clouds of digitized parts. The method integrates a sensitivity analysis to characterize the
impact of the variations in the parameters of a CAD model on the evolution of the deviation between the CAD model itself
and the point cloud to be ftted. The principles underpinning the adopted ftting algorithm are briefy recalled. A framework
that uses design of experiments (DOEs) is introduced to identify and save in a database the best setting parameter values
for given CAD models. This database is then exploited when considering the ftting of a new CAD model. Using similar ity assessment, it is then possible to reuse the best setting parameter values of the most similar CAD model found in the
database. The applied sensitivity analysis is described together with the comparison of the resulting sensitivity evolution
curves with the changes in the CAD model parameters imposed by the SA algorithm. Possible improvements suggested by
the analysis are implemented to enhance the efciency of SA-based ftting. The overall approach is illustrated on the ftting
of single mechanical parts but it can be directly extended to the ftting of parts’ assemblies. It is particularly interesting in
the context of the Industry 4.0 to update and maintain the coherence of the digital twins with respect to the evolution of the
associated physical products and systems.
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