Abstract
The aim of this research is to develop a model to predict the cutting forces of a turning operation. This paper focuses on to design a monitoring system that can recognize cutting force on the basis of cutting parameters like spindle speed, feed and depth of cut by using adaptive neuro-fuzzy inference system (ANFIS). Cutting force is one of the important characteristic variables to be watched and controlled in the cutting processes to determine tool life and surface roughness of the work piece. The principal assumption was that the cutting forces increase due to the wearing of the tool. So, ANFIS model is used to express the cutting force signal. In this paper, ANFIS is used to predict the cutting force. The correlation coefficient (R) and average percentage error found in this modeling are 0.9976 and 2.59% respectively. The predicted cutting force values derived from ANFIS were compared with experimental data. The comparison indicates that the ANFIS achieved very satisfactory accuracy. The correlation coefficient (R) and average percentage error found in this modeling are 0.9976 and 2.59% respectively. The prediction accuracy of ANFIS reached is as high as 97%.
Similar content being viewed by others
References
Abouelatta O, Madl J (2001) Surface roughness prediction based on cutting parameters and tool vibrations in turning operations. J Mater Process Technol 118:269–277
Aengchuan P, Phruksaphanrat B (2015) Comparison of fuzzy inference system (FIS), FIS with artificial neural networks (FIS + ANN) and FIS with adaptive neuro-fuzzy inference system (FIS + ANFIS) for inventory control. J Intell Manuf. https://doi.org/10.1007/s10845-015-1146-1
Azouzi R, Guillot M (1997) On-line prediction of surface finish and dimensional deviation in turning using neural network based sensor fusion. Int J Mach Tools Manuf 37:1201–1217
Baseri H (2011) Design of adaptive neuro-fuzzy inference system for estimation of grinding performance. Mater Manuf Processes 26:757–763. https://doi.org/10.1080/10426911003636951
Chaudhary H, Panwar V, Prasad R, Sukavanam N (2016) Adaptive neuro fuzzy based hybrid force/position control for an industrial robot manipulator. J Intell Manuf 27:1299–1308
Childs T (2000) Metal machining: theory and applications. Butterworth-Heinemann, Waltham
Cus F, Balic J (2003) Optimization of cutting process by GA approach. Robot Comput Integr Manuf 19:113–121
Dixit US, Sarma D, Davim JP (2012) Environmentally friendly machining. Springer, New York
Dong M, Wang N (2011) Adaptive network-based fuzzy inference system with leave-one-out cross-validation approach for prediction of surface roughness. Appl Math Model 35:1024–1035. https://doi.org/10.1016/j.apm.2010.07.048
El Baradie M (1991) Computer aided analysis of a surface roughness model for turning. J Mater Process Technol 26:207–216
El Baradie M (1993) Surface roughness model for turning grey cast iron (154 BHN). Proc Inst Mech Eng Part B J Eng Manuf 207:43–54
Fang X, Jawahir I (1994) Predicting total machining performance in finish turning using integrated fuzzy-set models of the machinability parameters. Int J Prod Res 32:833–849
Gajate A, Haber R, Del Toro R, Vega P, Bustillo A (2012) Tool wear monitoring using neuro-fuzzy techniques: a comparative study in a turning process. J Intell Manuf 23:869–882
Ghani A, Choudhury I (2002) Study of tool life, surface roughness and vibration in machining nodular cast iron with ceramic tool. J Mater Process Technol 127:17–22
Gorczyca FE (1987) Application of metal cutting theory. Industrial Press, New York
Ho W-H, Tsai J-T, Lin B-T, Chou J-H (2009) Adaptive network-based fuzzy inference system for prediction of surface roughness in end milling process using hybrid Taguchi-genetic learning algorithm. Expert Syst Appl 36:3216–3222. https://doi.org/10.1016/j.eswa.2008.01.051
Iqbal A, He N, Dar NU, Li L (2007a) Comparison of fuzzy expert system based strategies of offline and online estimation of flank wear in hard milling process. Expert Syst Appl 33:61–66. https://doi.org/10.1016/j.eswa.2006.04.003
Iqbal A, He N, Li L, Dar NU (2007b) A fuzzy expert system for optimizing parameters and predicting performance measures in hard-milling process. Expert Syst Appl 32:1020–1027. https://doi.org/10.1016/j.eswa.2006.02.003
Jain V, Raj T (2013) Ranking of flexibility in flexible manufacturing system by using a combined multiple attribute decision making method. Glob J Flex Syst Manag 14:125–141. https://doi.org/10.1007/s40171-013-0038-5
Jain V, Raj T (2014) Modelling and analysis of FMS productivity variables by ISM, SEM and GTMA approach. Front Mech Eng 9:218–232. https://doi.org/10.1007/s11465-014-0309-7
Jain V, Raj T (2015) Modeling and analysis of FMS flexibility factors by TISM and fuzzy MICMAC. Int J System Assur Eng Manag 6:350–371
Jain V, Raj T (2016) Modeling and analysis of FMS performance variables by ISM, SEM and GTMA approach. Int J Prod Econ 171:84–96. https://doi.org/10.1016/j.ijpe.2015.10.024
Jain V, Raj T (2017) Tool life management of unmanned production system based on surface roughness by ANFIS. Int J Syst Assur Eng Manag 8:458–467. https://doi.org/10.1007/s13198-016-0450-2
Jang JS (1993) ANFIS: adaptive-network-based fuzzy inference system. IEEE Trans Syst Man Cybern 23:665–685. https://doi.org/10.1109/21.256541
Kalpakjian S (2001) Manufacturing engineering and technology. Pearson Education, India
Kumanan S, Jesuthanam C, Kumar RA (2008) Application of multiple regression and adaptive neuro fuzzy inference system for the prediction of surface roughness. Int J Adv Manuf Technol 35:778–788
Lin W, Lee B, Wu C (2001) Modeling the surface roughness and cutting force for turning. J Mater Process Technol 108:286–293
Lo S-P (2002) The application of an ANFIS and grey system method in turning tool-failure detection. Int J Adv Manuf Technol 19:564–572. https://doi.org/10.1007/s001700200061
Maher I, Eltaib M, El-Zahry R (2006) Surface roughness prediction in end milling using multiple regression and adaptive neuro-fuzzy inference system. Paper presented at the International conference on mechanical engineering advanced technology for industrial production, Assiut University, Egypt
Maher I, Eltaib M, Sarhan AA, El-Zahry R (2014) Investigation of the effect of machining parameters on the surface quality of machined brass (60/40) in CNC end milling—ANFIS modeling. Int J Adv Manuf Technol 74:531–537
Maher I, Eltaib M, Sarhan AA, El-Zahry R (2015) Cutting force-based adaptive neuro-fuzzy approach for accurate surface roughness prediction in end milling operation for intelligent machining. Int J Adv Manuf Technol 76:1459–1467
Mital A, Mehta M (1988) Surface finish prediction models for fine turning. Int J Prod Res 26:1861–1876
Ojha D, Dixit U (2005) An economic and reliable tool life estimation procedure for turning. Int J Adv Manuf Technol 26:726–732. https://doi.org/10.1007/s00170-003-2049-4
Rizal M, Ghani JA, Nuawi MZ, Haron CHC (2013) Online tool wear prediction system in the turning process using an adaptive neuro-fuzzy inference system. Appl Soft Comput 13:1960–1968
Roy SS (2015) An application of ANFIS-based intelligence technique for predicting tool wear in milling. In: Mandal D. Kar R, Das S, Panigrahi B (eds) Advances in intelligent systems and computing 2015. Springer, New Delhi, pp 299–306. http://dx.doi.org/10.1007/978-81-322-2268-2_32
Samanta B (2009) Surface roughness prediction in machining using soft computing. Int J Comput Integr Manuf 22:257–266. https://doi.org/10.1080/09511920802287138
Sarkheyli A, Zain AM, Sharif S (2015) A multi-performance prediction model based on ANFIS and new modified-GA for machining processes. J Intell Manuf 26:703–716
Shafaei R, Rabiee M, Mirzaeyan M (2011) An adaptive neuro fuzzy inference system for makespan estimation in multiprocessor no-wait two stage flow shop. Int J Comput Integr Manuf 24:888–899
Sharma VS, Sharma S, Sharma AK (2008) Cutting tool wear estimation for turning. J Intell Manuf 19:99–108
Svalina I, Simunovic G, Simunovic K (2013) Machined surface roughness prediction using adaptive neurofuzzy inference system. Appl Artif Intell 27:803–817. https://doi.org/10.1080/08839514.2013.835233
Vajpayee S (1981) Analytical study of surface roughness in turning. Wear 70:165–175
Waters TF (2002) Fundamentals of manufacturing for engineers. CRC Press, London
Zhang JZ, Chen JC, Kirby ED (2007) The development of an in-process surface roughness adaptive control system in turning operations. J Intell Manuf 18:301–311. https://doi.org/10.1007/s10845-007-0024-x
Acknowledgements
We would like to thank everyone that participated in this research work. We express our gratitude all the anonymous reviewers of this paper for their valuable suggestions, who have helped to improve the quality of this paper.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Jain, V., Raj, T. Prediction of cutting force by using ANFIS. Int J Syst Assur Eng Manag 9, 1137–1146 (2018). https://doi.org/10.1007/s13198-018-0717-x
Received:
Revised:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s13198-018-0717-x