A Survey on 3D CAD model quality assurance and testing tools

TL;DR

This survey reviews CAD model quality issues, classifies errors, and evaluates testing tools, highlighting gaps in semantic error detection and the potential of procedural models for improved quality assurance.

Contribution

It introduces a taxonomy of CAD quality issues, validates it through classification of testing tools, and discusses the limitations and future prospects of current quality assurance methods.

Findings

Explicit models cover low semantic errors but are costly for SMEs.

Standards like STEP AP 203/214 improve interoperability.

Procedural models automatically prevent many morphologic errors.

Abstract

A new taxonomy of issues related to CAD model quality is presented, which distinguishes between explicit and procedural models. For each type of model, morphologic, syntactic, and semantic errors are characterized. The taxonomy was validated successfully when used to classify quality testing tools, which are aimed at detecting and repairing data errors that may affect the simplification, interoperability, and reusability of CAD models. The study shows that low semantic level errors that hamper simplification are reasonably covered in explicit representations, although many CAD quality testers are still unaffordable for Small and Medium Enterprises, both in terms of cost and training time. Interoperability has been reasonably solved by standards like STEP AP 203 and AP214, but model reusability is not feasible in explicit representations. Procedural representations are promising, as interactive modeling editors automatically prevent most morphologic errors derived from unsuitable modeling strategies. Interoperability problems between procedural representations are expected to decrease dramatically with STEP AP242. Higher semantic aspects of quality such as assurance of design intent, however, are hardly supported by current CAD quality testers.