Recognition of features in sheet metal parts manufactured using progressive dies. (May 2021)

• Record Type: Journal Article
• Authors: Yang, Yang; Hinduja, Srichand; Owodunni, Oladele O.; Heinemann, Robert
• Is Part Of: Computer aided design. Volume 134(2021)
• Publication Date: 2021-05
• Subjects: Sheet metal parts -- Progressive dies -- Feature recognition -- Thickness faces -- Feature taxonomy; Computer-aided design -- Periodicals; Engineering design -- Data processing -- Periodicals; Computer graphics -- Periodicals; Conception technique -- Informatique -- Périodiques; Infographie -- Périodiques; Computer graphics; Engineering design -- Data processing; Periodicals; Electronic journals; 620.00420285
• DOI: 10.1016/j.cad.2021.102991
• Languages: English
• ISSNs: 0010-4485

Abstract

Abstract: Feature recognition is an important stage in digital manufacturing as it supports activities such as automated process planning and part re-design. This paper describes a two-stage algorithm for recognising features in sheet metal parts which are manufactured using progressive dies. Representation of the part in neutral format (STEP AP203) facilitates the first stage of this algorithm in which the thickness faces are extracted in the form of chains. In the second stage, an extracted chain of faces is classified by, first, determining its topological and geometric characteristics and then comparing its characteristics with pre-set attributes for each feature. The system was developed in C++ and has been successfully tested on representative parts containing features ranging from simple bends to composite features, such as a cut-out with lance. Highlights: Methodology for extracting features mainly based on thickness face chains. Geometric and topological characteristics of the features are captured in matrices. Characteristics matrices are used to classify sheet metal features.