GMCAD: an original Synthetic Dataset of 2D Designs along their Geometrical and Mechanical Conditions

Abstract

We build an original synthetic dataset of 2D mechanical designs alongside their mechanical and geometric constraints, GMCAD. Such a dataset allows training Deep Learning (DL) models for Design for Additive Manufacturing (DfAM) to incorporate and control Computer-Aided-Design (CAD) features with mechanical performance. Geometric AM constraints are often complex to describe, depending on applications, processes, materials. They often lack explicit mathematical descriptions, belong exclusively to the CAD world, and hardly can be integrated into mechanical design, hampering AM design freedom. DL models have recently emerged as a potential to reconcile both CAD and Computer Aided-Engineering (CAE) worlds. They derive data-driven geometric rules over mechanical designs, allowing fine-grained control over the geometry during the design phase, contrary to the conventional CAD-to-CAE sequential approach. DL models, however, need high-quality labeled data, and merging CAD features to CAE aspects is challenging as they rely on different formats, rules, and tools. GMCAD dataset solves this issue following these building steps. (i) Building a DL-mechanical conditions predictor from a dataset generated by a density-gradient-based Topology Optimization method (TO); an AM-synergetic design generation tool. (ii) Creating a CAD dataset inspired by the TO-based designs. (iii) Predicting the mechanical conditions of the CADs using the DL predictor of mechanical conditions. Last, we evaluate the mechanical performance of GMCAD’s designs and derive statistics over CAD and CAE features. Designs of GMCAD show the significant influence of minor geometric changes, explaining the intricate design task of conforming both with functionality and geometric constraints. Consequently, having GMCAD is advantageous to train DL models to generate designs accounting for all these constraints simultaneously, without the need for time-consuming trial and error techniques. Such models could enhance DfAM and go beyond AM; they can also enhance other challenging fields as CAD automatic reconstruction, reverse engineering, isogeometric design and paves the way to multi-objective controllable design generation.