However, the current automated production lines for mechanical parts face great challenges. Mechanical parts are an important part of the machinery industry. Computational costs and required time are at the moment considerable. Accuracy of reconstructed models is comparable/better than state of the art results. Fitting process is controlled by a Particle Swarm Optimization algorithm. A feature-based parametric-associative modelling history is retrieved. Highlights A novel CAD reconstruction method fitting a CAD template model to mesh data.

Five reconstruction tests, covering both synthetic and real-scanned mesh data, are presented and discussed in the manuscript the results are finally compared with models generated by state of the art reverse engineering software and key aspects to be addressed in future work are hinted at. The proposed implementation exploits a cooperation between a CAD software package (Siemens NX) and a numerical software environment (MATLAB). As a result, a parametric CAD model that perfectly fulfils the imposed geometric relations is produced and a feature tree, defining an associative modelling history, is available to the reverse engineer. The CAD template is fitted upon the mesh data, optimizing its dimensional parameters and positioning/orientation by means of a particle swarm optimization algorithm.

The reconstruction process is performed relying on a CAD template, whose feature tree and geometric constraints are defined according to the a priori information on the physical object. CAD template) of the object to be reconstructed in order to retrieve a meaningful digital representation, a novel reverse engineering approach for the reconstruction of CAD models starting from 3D mesh data is proposed. Inspired by recent works suggesting the possibility/opportunity of exploiting a parametric description (i.e.

Template-Based reverse engineering approaches represent a relatively poorly explored strategy in the field of CAD reconstruction from polygonal models.

Geomagic design x tutorial pdf