Please use this identifier to cite or link to this item:
Title: Bayesian-inference-based neural networks for tool wear estimation
Authors: Dong, J.
Subrahmanyam, K.V.R.
Wong, Y.S. 
Hong, G.S. 
Mohanty, A.R.
Keywords: ARD
Tool wear
Issue Date: Sep-2006
Citation: Dong, J., Subrahmanyam, K.V.R., Wong, Y.S., Hong, G.S., Mohanty, A.R. (2006-09). Bayesian-inference-based neural networks for tool wear estimation. International Journal of Advanced Manufacturing Technology 30 (9-10) : 797-807. ScholarBank@NUS Repository.
Abstract: This paper introduces the application of neural networks based on Bayesian inference, the automatic relevance determination algorithm for selecting relevant features and designing neural estimators for tool wear estimation in face-milling processes. Two types of neural networks are studied: Bayesian support vector machines for regression (BSVR) and Bayesian multilayer perceptrons (BMLP). Sixteen well-known features derived from pre-processing of the milling force signal are considered. The force signal samples are collected from 20 milling experiments under various machining conditions. The feature extraction and selection procedure is then applied to the sampled force signal. The feature selection results from the two neural networks are found to be quite similar. The average force has been proven to be the most relevant feature for tool wear estimation in both cases, among a set of six other features in each case, with each set differing by only one feature. The comparison among the generalization capabilities of the entire, selected, and rejected features shows that the selected features are relatively more relevant to tool wear processes in both cases. The comparison between the estimation results from the two neural networks using the corresponding relevant feature set shows that the BSVR method is more accurate in estimating flank wear than BMLP, but at the cost of a higher computing load. © Springer-Verlag London Limited 2006.
Source Title: International Journal of Advanced Manufacturing Technology
ISSN: 02683768
DOI: 10.1007/s00170-005-0124-8
Appears in Collections:Staff Publications

Show full item record
Files in This Item:
There are no files associated with this item.


checked on Oct 16, 2018


checked on Oct 8, 2018

Page view(s)

checked on Sep 22, 2018

Google ScholarTM



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.