Online tool wear prediction system in the turning process using an adaptive neuro-fuzzy inference system

Authors:
Muhammad Rizal;Jaharah A. Ghani;Mohd Zaki Nuawi;Che Hassan Che Haron
Affiliations:
Department of Mechanical and Materials Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, 43600 Bangi, Malaysia and Department of Mechanical Engineering, Fa ...;Department of Mechanical and Materials Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, 43600 Bangi, Malaysia;Department of Mechanical and Materials Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, 43600 Bangi, Malaysia;Department of Mechanical and Materials Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, 43600 Bangi, Malaysia
Venue:
Applied Soft Computing
Year:
2013

Citing 9
Cited 0

Multi-sensor integration for on-line tool wear estimation through radial basis function networks and fuzzy neural network

Neural Networks
Comparison of wavelet families for texture classification by using wavelet packet entropy adaptive network based fuzzy inference system

Applied Soft Computing
A novel approach for ANFIS modelling based on full factorial design

Applied Soft Computing
Transductive-Weighted Neuro-Fuzzy Inference System for Tool Wear Prediction in a Turning Process

HAIS '09 Proceedings of the 4th International Conference on Hybrid Artificial Intelligence Systems
On the Need of Kurtosis-Based Technique to Evaluate the Fatigue Life of a Coil Spring

ICSPS '09 Proceedings of the 2009 International Conference on Signal Processing Systems
On-line monitoring of boring tools for control of boring operations

Robotics and Computer-Integrated Manufacturing
Adaptive neuro-fuzzy inference system modeling of cryogenically treated AISI M2 HSS turning tool for estimation of flank wear

Expert Systems with Applications: An International Journal
Tool wear monitoring using neuro-fuzzy techniques: a comparative study in a turning process

Journal of Intelligent Manufacturing
Self-learning fuzzy controllers based on temporal backpropagation

IEEE Transactions on Neural Networks

Quantified Score

Hi-index	0.00

Visualization

Abstract

Tool wear is a detrimental factor that affects the quality and tolerance of machined parts. Having an accurate prediction of tool wear is important for machining industries to maintain the machined surface quality and can consequently reduce inspection costs and increase productivity. Online and real-time tool wear prediction is possible due to developments in sensor technology. Recently, various sensors and methods have been proposed for the development of tool wear monitoring systems. In this study, an online tool wear monitoring system was proposed using a strain gauge-type sensor due to its simplicity and low cost. A model, based on the adaptive network-based fuzzy inference system (ANFIS), and a new statistical signal analysis method, the I-kaz method, were used to predict tool wear during a turning process. In order to develop the ANFIS model, the cutting speed, depth of cut, feed rate and I-kaz coefficient from the signals of each turning process were taken as inputs, and the flank wear value for the cutting edge was an output of the model. It was found that the prediction usually accurate if the correlation of coefficients and the average errors were in the range of 0.989-0.995 and 2.30-5.08% respectively for the developed model. The proposed model is efficient and low-cost which can be used in the machining industry for online prediction of the cutting tool wear progression, but the accuracy of the model depends upon the training and testing data.