Skip to main content
SpringerLink
Log in

Investigation of the effect of machining parameters on the surface quality of machined brass (60/40) in CNC end milling—ANFIS modeling

  • ORIGINAL ARTICLE
  • Published:
The International Journal of Advanced Manufacturing Technology Aims and scope Submit manuscript

Abstract

Brass and brass alloys are widely employed industrial materials because of their excellent characteristics such as high corrosion resistance, non-magnetism, and good machinability. Surface quality plays a very important role in the performance of milled products, as good surface quality can significantly improve fatigue strength, corrosion resistance, or creep life. Surface roughness (Ra) is one of the most important factors for evaluating surface quality during the finishing process. The quality of surface affects the functional characteristics of the workpiece, including fatigue, corrosion, fracture resistance, and surface friction. Furthermore, surface roughness is among the most critical constraints in cutting parameter selection in manufacturing process planning. In this paper, the adaptive neuro-fuzzy inference system (ANFIS) was used to predict the surface roughness in computer numerical control (CNC) end milling. Spindle speed, feed rate, and depth of cut were the predictor variables. Experimental validation runs were conducted to validate the ANFIS model. The predicted surface roughness was compared with measured data, and the maximum prediction error for surface roughness was 6.25 %, while the average prediction error was 2.75 %.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Zronik J (2005) Metals shaping our world. Crabtree, Canada

    Google Scholar 

  2. Cheremisinoff NP (1996) Materials selection deskbook. Noyes, New Jersey

    Google Scholar 

  3. Benardos PG, Vosniakos GC (2002) Prediction of surface roughness in CNC face milling using neural networks and Taguchi’s design of experiments. Robot Comput Integr Manuf 18(5–6):343–354. doi:10.1016/S0736-5845(02)00005-4

    Article  Google Scholar 

  4. Suresh PVS, Venkateswara Rao P, Deshmukh SG (2002) A genetic algorithmic approach for optimization of surface roughness prediction model. Int J Mach Tools Manuf 42(6):675–680. doi:10.1016/S0890-6955(02)00005-6

    Article  Google Scholar 

  5. Wang X, Feng C (2002) Development of empirical models for surface roughness prediction in finish turning. Int J Adv Manuf Technol 20(5):348–356

    Article  Google Scholar 

  6. Sayuti M, Sarhan AAD, Tanaka T, Hamdi M, Saito Y (2012) Cutting force reduction and surface quality improvement in machining of aerospace duralumin AL-2017-T4 using carbon onion nanolubrication system. Int J Adv Manuf Technol 65(9-12):1493--1500

  7. Chen J, Huang B (2003) An in-process neural network-based surface roughness prediction (INN-SRP) system using a dynamometer in end milling operations. Int J Adv Manuf Technol 21(5):339–347

    Article  Google Scholar 

  8. Chang H-K, Kim J-H, Kim IH, Jang DY, Han DC (2007) In-process surface roughness prediction using displacement signals from spindle motion. Int J Mach Tools Manuf 47(6):1021–1026. doi:10.1016/j.ijmachtools.2006.07.004

    Article  Google Scholar 

  9. Singh D, Rao PV (2006) A surface roughness prediction model for hard turning process. Int J Adv Manuf Technol 32(11–12):1115–1124. doi:10.1007/s00170-006-0429-2

    Google Scholar 

  10. Asiltürk İ, Çunkaş M (2011) Modeling and prediction of surface roughness in turning operations using artificial neural network and multiple regression method. Expert Syst Appl 38(5):5826–5832. doi:10.1016/j.eswa.2010.11.041

    Article  Google Scholar 

  11. Asiltürk İ (2012) Predicting surface roughness of hardened AISI 1040 based on cutting parameters using neural networks and multiple regression. Int J Adv Manuf Technol 63(1–4):249–257. doi:10.1007/s00170-012-3903-z

    Article  Google Scholar 

  12. Sayuti M, Sarhan AAD, Fadzil M, Hamdi M (2012) Enhancement and verification of a machined surface quality for glass milling operation using CBN grinding tool—Taguchi approach. Int J Adv Manuf Technol 60(9–12):939–950. doi:10.1007/s00170-011-3657-z

    Article  Google Scholar 

  13. Bagci E, Aykut Ş (2005) A study of Taguchi optimization method for identifying optimum surface roughness in CNC face milling of cobalt-based alloy (stellite 6). Int J Adv Manuf Technol 29(9–10):940–947. doi:10.1007/s00170-005-2616-y

    Google Scholar 

  14. Zalnezhad E, Sarhan AAD, Hamdi M (2013) A fuzzy logic based model to predict surface hardness of thin film TiN coating on aerospace AL7075-T6 alloy. Int J Adv Manuf Technol 68(1–4):415–423. doi:10.1007/s00170-013-4738-y

    Article  Google Scholar 

  15. Kohli A, Dixit US (2004) A neural-network-based methodology for the prediction of surface roughness in a turning process. Int J Adv Manuf Technol 25(1–2):118–129. doi:10.1007/s00170-003-1810-z

    Google Scholar 

  16. Abdel Badie S (2011) Prediction of surface roughness in end milling process using intelligent systems: a comparative study. Appl Comput Intel Soft Comput. doi:10.1155/2011/183764

    Google Scholar 

  17. Asiltürk İ, Akkuş H (2011) Determining the effect of cutting parameters on surface roughness in hard turning using the Taguchi method. Measurement. doi:10.1016/j.measurement.2011.07.003

    Google Scholar 

  18. Hasçalık A, Çaydaş U (2007) Optimization of turning parameters for surface roughness and tool life based on the Taguchi method. Int J Adv Manuf Technol 38(9–10):896–903. doi:10.1007/s00170-007-1147-0

    Google Scholar 

  19. Hamdan A, Sarhan AAD, Hamdi M (2012) An optimization method of the machining parameters in high-speed machining of stainless steel using coated carbide tool for best surface finish. Int J Adv Manuf Technol 58(1–4):81–91. doi:10.1007/s00170-011-3392-5

    Article  Google Scholar 

  20. Dweiri F, Al-Jarrah M, Al-Wedyan H (2003) Fuzzy surface roughness modeling of CNC down milling of Alumic-79. J Mater Process Tech 133(3):266–275. doi:10.1016/S0924-0136(02)00847-6

    Article  Google Scholar 

  21. Kilickap E, Huseyinoglu M, Yardimeden A (2010) Optimization of drilling parameters on surface roughness in drilling of AISI 1045 using response surface methodology and genetic algorithm. Int J Adv Manuf Technol 52(1–4):79–88. doi:10.1007/s00170-010-2710-7

    Google Scholar 

  22. Kumar BS, Baskar N (2012) Integration of fuzzy logic with response surface methodology for thrust force and surface roughness modeling of drilling on titanium alloy. Int J Adv Manuf Technol 65(9–12):1501–1514. doi:10.1007/s00170-012-4275-0

    Google Scholar 

  23. Dong M, Wang N (2011) Adaptive network-based fuzzy inference system with leave-one-out cross-validation approach for prediction of surface roughness. Applied Mathematical Modelling 35(3):1024–1035. doi:10.1016/j.apm.2010.07.048

    Article  MATH  Google Scholar 

  24. Zalnezhad E, Sarhan AD, Hamdi M (2013) Investigating the effects of hard anodizing parameters on surface hardness of hard anodized aerospace AL7075-T6 alloy using fuzzy logic approach for fretting fatigue application. Int J Adv Manuf Technol 68(1–4):453–464. doi:10.1007/s00170-013-4743-1

    Article  Google Scholar 

  25. Lo S-P (2003) An adaptive-network based fuzzy inference system for prediction of workpiece surface roughness in end milling. J Mater Process Tech 142(3):665–675. doi:10.1016/S0924-0136(03)00687-3

    Article  Google Scholar 

  26. Kumanan S, Jesuthanam CP, Ashok Kumar R (2008) Application of multiple regression and adaptive neuro fuzzy inference system for the prediction of surface roughness. Int J Adv Manuf Technol 35(7–8):778–788. doi:10.1007/s00170-006-0755-4

    Article  Google Scholar 

  27. Ho S-Y, Lee K-C, Chen S-S, Ho S-J (2002) Accurate modeling and prediction of surface roughness by computer vision in turning operations using an adaptive neuro-fuzzy inference system. Int J Mach Tools Manuf 42(13):1441–1446. doi:10.1016/S0890-6955(02)00078-0

    Article  Google Scholar 

  28. Lee K-C, Ho S-J, Ho S-Y (2005) Accurate estimation of surface roughness from texture features of the surface image using an adaptive neuro-fuzzy inference system. Precision Eng 29(1):95–100. doi:10.1016/j.precisioneng.2004.05.002

    Article  Google Scholar 

  29. Jang J-SR, Sun C-T, Mizutani E (1997) Neuro-fuzzy and soft computing : a computational approach to learning and machine intelligence. MATLAB curriculum series. Prentice Hall, Inc, U.S.A.

  30. Kasabov NK (1997) Foundations of neural networks, fuzzy systems, and knowledge engineering, vol. 33. MIT, Cambridge. doi:10.1016/S0898-1221(97)84600-7, Computers & Mathematics with Applications, vol 7

    Google Scholar 

  31. Zalnezhad E, Sarhan AAD, Hamdi M (2012) Prediction of tin coating adhesion strength on aerospace AL7075-T6 alloy using fuzzy rule based system. Int J Precis Eng Manuf 13(8):1453--1459

  32. Ghani JA, Choudhury IA, Hassan HH (2004) Application of Taguchi method in the optimization of end milling parameters. J Mater Process Tech 145(1):84–92. doi:10.1016/S0924-0136(03)00865-3

    Article  Google Scholar 

  33. Philip PK (1971) Built-up edge phenomenon in machining steel with carbide. Int J Mach Tools Des Res 11(2):121–132. doi:10.1016/0020-7357(71)90021-7

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Centre of Advanced Manufacturing and Material Processing, Department of Mechanical Engineering, Faculty of Engineering, University of Malaya, 50603, Kuala Lumpur, Malaysia

    Ibrahem Maher & Ahmed A. D. Sarhan

  2. Department of Mechanical Engineering, Faculty of Engineering, Kafrelsheikh University, Kafrelsheikh, 33516, Egypt

    Ibrahem Maher

  3. Department of Mechanical Engineering, Faculty of Engineering, Assiut University, Assiut, 71516, Egypt

    M. E. H. Eltaib, Ahmed A. D. Sarhan & R. M. El-Zahry

Authors
  1. Ibrahem Maher
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. M. E. H. Eltaib
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ahmed A. D. Sarhan
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. R. M. El-Zahry
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ahmed A. D. Sarhan.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Maher, I., Eltaib, M.E.H., Sarhan, A.A.D. et al. 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 (2014). https://doi.org/10.1007/s00170-014-6016-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00170-014-6016-z

Keywords

Search

Navigation