Tool wear prediction using evolutionary Dynamic Fuzzy Neural (EDFNN) Network

Mahardhika Pratama*, Meng Joo Er, Xiang Li, Oon Peen Gan, Richad J. Oentaryo, San Linn, Lianyin Zhai, Imam Arifin

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

6 Citations (Scopus)

Abstract

In development of self-organizing fuzzy neural network, selection of optimal parameters is one of the key issues. This is especially so for a system with more than 10 parameters whereby it will be challenging for expert users to determine the optimal parameters. This paper presents a hybrid Dynamic Fuzzy Neural Network (DFNN), and Genetic Algorithm (GA) termed Evolutionary Dynamic Fuzzy Neural Network (EDFNN) for the prediction of tool wear of ball nose end milling process. GA, well known for its powerful search method, is implemented to obtain optimal parameters of DFNN, so as to circumvent the complex time varying property without prior knowledge or exhaustive trials. Degradation of machine tools in ball nose end milling process is highly non-linear and time varying. Benchmarked again original DFNN in the experimental study, EDFNN demonstrates the effectiveness and versatility of proposed algorithm which not only produces higher prediction accuracy, and faster training time, but also serves to more compact and parsimonious network structure.

Original languageEnglish
Title of host publicationProceedings
Subtitle of host publicationIECON 2011 - 37th Annual Conference of the IEEE Industrial Electronics Society
Pages4739-4744
Number of pages6
DOIs
Publication statusPublished - 2011
Event37th Annual Conference of the IEEE Industrial Electronics Society, IECON 2011 - Melbourne, VIC, Australia
Duration: 7 Nov 201110 Nov 2011

Publication series

NameIECON Proceedings (Industrial Electronics Conference)

Conference

Conference37th Annual Conference of the IEEE Industrial Electronics Society, IECON 2011
Country/TerritoryAustralia
CityMelbourne, VIC
Period7/11/1110/11/11

Keywords

  • DFNN
  • Genetic Algorithm
  • ball nose end milling process
  • tool wear prediction

Fingerprint

Dive into the research topics of 'Tool wear prediction using evolutionary Dynamic Fuzzy Neural (EDFNN) Network'. Together they form a unique fingerprint.

Cite this