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

International Journal of Advanced Manufacturing Technology

Volumn 74, Issue 1-4, 2014, Pages 531-537

  Maher, Ibrahem ^a,b   Eltaib, M E H ^c   Sarhan, Ahmed A D ^a,c   El Zahry, R M ^c  

^a UNIVERSITY OF MALAYA   (Malaysia)

^b University of Kafrelsheikh   (Egypt)

^c Assiut University   (Egypt)

Author keywords

ANFIS; Brass; CNC; End milling; Surface roughness

Indexed keywords

BRASS; COMPUTER CONTROL SYSTEMS; CORROSION RESISTANCE; FORECASTING; MILLING (MACHINING); SURFACE PROPERTIES;

ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM; ANFIS; CNC; COMPUTER NUMERICAL CONTROL; END MILLING; EXPERIMENTAL VALIDATIONS; FUNCTIONAL CHARACTERISTICS; MANUFACTURING PROCESS PLANNING;

SURFACE ROUGHNESS;

EID: 84906305528     PISSN: 02683768     EISSN: 14333015     Source Type: Journal    
DOI: 10.1007/s00170-014-6016-z     Document Type: Article

References (33)

1. Zronik J (2005) Metals shaping our world. Crabtree, Canada
2. Cheremisinoff NP (1996) Materials selection deskbook. Noyes, New Jersey
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
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
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
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
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
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
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
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
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
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
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
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
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
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
18. HasçalIk 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
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
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
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
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
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
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
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
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
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
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
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
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
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