Recognising 2.5D manufacturing feature using neural network

Authors:
Xiaojun Liu;Zhonghua Ni;Xiaoli Qiu;Tingyu Liu
Affiliations:
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
Venue:
International Journal of Computer Applications in Technology
Year:
2010

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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.