ANFIS Building Methodology for the Task of Cutting Tool Condition Diagnosis Using Matlab Software

Abstract:

Machining of materials with cutting still covers a significant part of shapegenerating operations in manufacturing process. One of the most important tasks in research touches upon the area of cutting is the development of a method which can insure: required productivity, high accuracy of machining, optimal usage of cutting tool and machine-tool resource, automation of manufacturing process, reduction of machinetool down time and cutting tool costs. In this connection cutting tool condition diagnosis (CTCD) becomes an important requirement for the realization of computer-aided manufacturing. The real-time CTCD allows improving the efficiency of machining with the opportune cutting tool replacement and the prior prevention of its catastrophic wear or breakdown. The main goal of this investigation is the verification of the ANFIS working capacity for the description of the relation between the flank wear of cutting tool and the power of vibration signal received during CTCD in turning by means of Matlab software. Consequently, the methodology of building the adaptive neuro-fuzzy inference system (ANFISnetwork) for the task of cutting tool condition diagnosis is worked out.

• Periodical: Key Engineering Materials (Volume 581)
• Pages: 466-471
• DOI: https://doi.org/10.4028/www.scientific.net/KEM.581.466
• Online since: October 2013
• Authors: V.O. Zaloha, Ruslan M. Zinchenko, Anna V. Honshchyk
• Keywords: ANFIS-Network, Artificial Intelligence (AI), Diagnosis, Membership Function, Tool Wear

References

[1] C.С. Tsao. Tool wear and surface roughness prediction using an artificial neural network (ANN) in turning steel under minimum quantity lubrication (MQL), Engineering and Technology, 2010, No. 62, p.830 – 839.

[2] S.M. Ali. Modeling of tool wear and surface roughness under MQL condition-a neuralapproach, Canadian Journal on Artificial Intelligence, Machine Learning & Pattern Recognition, 2010, Vol. 1, No. 2, p.7 – 25.

[3] F. Basсiftсi. On-line prediction of tool wears by using methods ofartificial neural networks and fuzzy logic, Scientific Research and Essays, 2010, Vol. 5, No. 19, p.2883 – 2888.

[4] V.S. Sharma, S.K. Sharma, A.K. Sharma. Cutting tool wear estimation for turning, Journal of Intelligent Manufacturing, 2008, Vol. 19, p.99 – 108.

DOI: 10.1007/s10845-007-0048-2

[5] D. Dinakaran, S. Sampathkumar, J.S. Mary. Real Time Prediction of Flank Wear by Neuro Fuzzy Technique in Turning, Jordan Journal of Mechanical and Industrial Engineering, 2010, Vol. 4, No. 6, p.725 – 732.

[6] A. Gajate, R. Haber, R. del Toro, P. Vega, A. Bustillo. Tool wear monitoring using neuro-fuzzy techniques: a comparative study in a turning process, Journal of Intelligent Manufacturing, 2012, Vol. 23, No. 3, p.869 – 882.

DOI: 10.1007/s10845-010-0443-y

[7] I. Asilturk. On-line surface roughness recognition system by vibration monitoring in CNC turning using adaptive neuro-fuzzy inference system (ANFIS), International Journal of the Physical Sciences, 2011, Vol. 6 (22), p.5353 – 5360.

[8] S. Palani, U. Natarajan, M. Chellamalai. On-line prediction of micro-turning multi-response variables by machine vision system using adaptive neuro-fuzzy inference system (ANFIS), Machine Vision and Applications, (2011).

DOI: 10.1007/s00138-011-0378-0

[9] U. Zuperl, F. Cus, J. Balic. Intelligent cutting tool condition monitoring in milling. Journal of Achievements in Materials and Manufacturing Engineering, 2011, Vol. 49, No. 2, p.477 – 486.

[10] Information on http: /www. grima. ufsc. br/cobef4/files/011122400. pdf.

[11] S.S. Gill, R. Singh, J. Singh, H. Singh. Adaptive neuro-fuzzy inference system modeling of cryogenically treated AISI M2 HSS turning tool for estimation of flank wear, Expert Systems with Applications, 2012, Vol. 39, No. 4, p.4171 – 4180.

DOI: 10.1016/j.eswa.2011.09.117

[12] P. Kulandaivelu, P.S. Kumar. Investigate the Crater Wear Monitoring of Single Point Cutting Tool Using Adaptive Neuro Fuzzy Inference System, Journal of Applied Science and Engineering, 2012, Vol. 15, No. 3, p.265 – 274.

[13] A. Gajate, R.E. Haber, J.R. Alique, P.I. Vega. Transductive-Weighted Neuro-Fuzzy Inference System for Tool Wear Prediction in a Turning Process, 2009, HAIS 2009, LNAI 5572, p.113–120.

DOI: 10.1007/978-3-642-02319-4_14

[14] V.A. Zaloga, R.N. Zinchenko, A.V. Gonshchik. The new structure of system for cutting tool condition diagnosis based on utilization of fuzzy logic rules and artificial intelligence systems, Memoirs of the Crimean Engineering and Pedagogical University. Issueк 27. Technical sciences, Simferopol, 2011, p.46.