Thin part identification for CAD model classification

Abstract

is an open access repository that collects the work of Arts et Métiers ParisTech researchers and makes it freely available over the web where possible. Purpose -The purpose of this paper is to focus on the characterization and classification of parts with respect to the meshing issue, and notably the meshing of thin parts difficulty handled automatically and which often requires adaptation steps. The objective is to distinguish the so-called thin parts and parts with thin features from the other parts. Design/methodology/approach -The concepts of thin part and part with thin features are introduced together with the mechanisms and criteria used for their identification in a CAD models database. The criteria are built on top of a set of shape descriptors and notably the distance distribution which is used to characterize the thickness of the object. To speed up the identification process, shape descriptors are computed from tessellated parts. Findings -A complete modular approach has been designed. It computes shape descriptors over parts stored in a directory and it uses criteria to distinguish three categories: thin parts, parts with thin features and other parts. Being the three categories identified, the user can spend more time on the parts that are considered as more difficulty meshable.Research limitations/implications -The approach is limited to the three above mentioned categories. However, it has been designed so that the values corresponding to the shape descriptors and associated meshing qualities can easily be inserted within a machining learning tool later on. Practical implications -The use of the developed tool can be seen as a pre-processing step during the preparation of finite element (FE) simulation models. It is automatic and can be run in batch and in parallel. Originality/value -The approach is modular, it is simple and easy to implement. Categories are built on top of several shape descriptors and not on a unique signature. It is independent of the CAD modeler. This approach is integrated within a FE simulation model preparation framework and help engineers anticipating difficulties when meshing CAD models.

Content-based CAD assembly model retrieval: Survey and future challenges

“…Using a series of shape descriptors, their approach aims at learning multiple CAD classifications and is applied to the classification of prismatic machined parts and parts with finishing features machined after part casting. The classification proposed by Pernot et al [98] also exploits a series of shape descriptors and classifies products in terms of characteristics that can affect the simplification process for the Finite Element Analysis (FEA) of parts. Hence, their categories are: thin parts, parts with thin portions and normal parts.…”

A methodology for part classification with supervised machine learning

“…Additional works address the categorization of parts according to specific design processes. Among them, Pernot et al (2015) tackle the problem of categorizing products in terms of characteristics that might affect the simplification of parts for the Finite Element Analysis (FEA). To this aim, using a combination of shape descriptors, parts are classified in terms of three categories: thin parts, parts with thin portions and normal.…”

Simulation of concurrent process planning (CPP) system for machining two different products

“…Process planning is considered as the bridge between the engineering design and the manufacturing processes [5]. The basic function of process planning is to determine the sequence to be followed in converting the work part from a raw material to a finished part or to a desired shape [6]. The process plans must be developed within the equipment capabilities and factory capacities.…”