Title: Recognising 2.5D manufacturing feature using neural network

Authors: Xiaojun Liu, Zhonghua Ni, Xiaoli Qiu, Tingyu Liu

Addresses: School of Mechanical Engineering, Southeast University, Nanjing 210096, China. ' School of Mechanical Engineering, Southeast University, Nanjing 210096, China. ' School of Mechanical Engineering, Southeast University, Nanjing 210096, China. ' School of Mechanical Engineering, Southeast University, Nanjing 210096, China

Abstract: This study develops a neural network based methodology for recognising Manufacturing Feature (MF) using the Boundary representation (B-rep) information. The methodology is capable of recognising both basic MF (including standard MF and non-standard MF) and interacting MF. Firstly, both the edges| convex concave attribute and the edges| position (whether an edge belongs to the inner edge loop or outer edge loop), which reflects the edges| characteristics and the relationship between the bottom profile and its adjacent faces, were analysed to define the input vector for the neural network. Based on this, a BP neural network with a single hidden layer which contains ten neurons was obtained. The basic MF is divided into four groups, and can be recognised easily using the neural network. For the interacting MFs, a basic MF is recognised firstly, the bottom profile for the interactive MF is updated, and the interactive MF can be recognised.

Keywords: manufacturing features; feature recognition; neural networks; CAD-CAPP-CAM; CADCAM; CAPP; boundary representation; B-rep; process planning.

DOI: 10.1504/IJCAT.2010.034725

International Journal of Computer Applications in Technology, 2010 Vol.39 No.1/2/3, pp.19 - 26

Published online: 18 Aug 2010 *

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