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

  • Research Article - Mechanical Engineering
  • Published:
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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.

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Abbreviations

ANFIS:

Adaptive neuro-fuzzy inference system

GP:

Genetic programming

ANN:

Artificial neural network

RSM:

Response surface methodology

MAPE:

Mean absolute percentage error

x, y :

Inputs of the ANFIS

f :

Output of the ANFIS

α 1, β 1, Ω 1, α 2, β 2, Ω 2 :

Linear parameters

A 1, B 1, A 2, B 2 :

Non linear parameters

Q 1, i :

Output of the ith node

Q 2, j :

Output of the jth node

μ A i , μ B i :

Membership function of the inputs

Q2, i = w i :

Weight function of w 1 and w 2 of product layer

Q3, i = W i :

Normalised weight function of normalised layer

Q4, i :

Output of de-fuzzy layer

Q5, i :

Output of the output layer

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Authors and Affiliations

  1. Department of Mechanical and Materials Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, 43600, Bangi, Selangor, Malaysia

    Salah Al-Zubaidi, Jaharah Abdul Ghani & Che Hassan Che Haron

Authors
  1. Salah Al-Zubaidi
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  2. Jaharah Abdul Ghani
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  3. Che Hassan Che Haron
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Correspondence to Jaharah Abdul Ghani.

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Al-Zubaidi, S., Ghani, J.A. & Che Haron, C.H. Prediction of Tool Life when End Milling of Ti6Al4V Alloy Using Hybrid Learning System. Arab J Sci Eng 39, 5095–5111 (2014). https://doi.org/10.1007/s13369-014-0975-0

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  • DOI: https://doi.org/10.1007/s13369-014-0975-0

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