Prediction of Tool Life when End Milling of Ti6Al4V Alloy Using Hybrid Learning System

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

Tool life significantly affects the machining cost and productivity. A wide number of techniques have been applied to modelling metal cutting processes. Techniques of artificial intelligence are new soft computing methods which suit solutions of nonlinear and complex problems such as metal cutting processes. The current study is concerned with the application of an adaptive neuro-fuzzy inference system (ANFIS). This ANFIS model is developed to predict tool life when end milling of Ti6Al4V alloy with coated (PVD) and uncoated cutting tools are under dry cutting conditions. By carrying out training and testing the ANFIS models, the current study employed real experimental results, and based on such results, a selection of the best model was conducted based on the mean absolute percentage error (%). For the modelling process, the study adopted a generalised bell shape membership function, and there was a change in its number from 2 to 5. The findings revealed that ANFIS is capable of modelling tool life in end milling process, and that there was good matching obtained between experimental and predicted results.

Original languageEnglish
Pages (from-to)5095-5111
Number of pages17
JournalArabian Journal for Science and Engineering
Volume39
Issue number6
DOIs
Publication statusPublished - 2014

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Fuzzy inference
Learning systems
Metal cutting
Soft computing
Physical vapor deposition
Cutting tools
Membership functions
Artificial intelligence
Machining
Productivity
Testing
Costs

Keywords

  • ANFIS
  • Cutting tools
  • Dry conditions
  • Titanium alloys
  • Tool life

ASJC Scopus subject areas

  • General

Cite this

Prediction of Tool Life when End Milling of Ti6Al4V Alloy Using Hybrid Learning System. / Al-Zubaidi, Salah; A Ghani, Jaharah; Che Haron, Che Hassan.

In: Arabian Journal for Science and Engineering, Vol. 39, No. 6, 2014, p. 5095-5111.

Research output: Contribution to journalArticle

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