Abstract
In the present study, the predictive model is developed to observe the effect of radial rake angle on the end milling cutting tool by considering the following machining parameters: spindle speed, feed rate, axial depth of cut, and radial depth of cut. By referring to the real machining case study, the second-order mathematical models have been developed using response surface methodology (RSM). A number of machining experiments based on statistical five-level full factorial design of experiments are carried out in order to collect surface roughness values. The direct and interaction effects of the machining parameter with surface roughness are analyzed using Design Expert software. The optimal surface roughness value can be attained within the specified limits by using RSM. The genetic algorithm (GA) model is trained and tested in MATLAB to find the optimum cutting parameters leading to minimum surface roughness. The GA recommends 0.25 μm as the best minimum predicted surface roughness value. The confirmatory test shows the predicted values which were found to be in good agreement with observed values.
Similar content being viewed by others
References
Jang DY, Choi Y-G, Kim H-G, Hsiao A (1996) Study of the correlation between surface roughness and cutting vibrations to develop an on-line roughness measuring technique in hard turning. Int J Mach Tools Manuf 36(4):453–464. doi:10.1016/0890-6955(95)00074-7
Ming-Yung W, Chang H -Y (2004) Experimental study of surface roughness in slot end milling AL2014-T6. Int J Mach Tools Manuf 44:51–57. doi:10.1016/j.ijmachtools.2003.08.011
Arias ER, Mecanico (1983) Analysis of surface roughness for end milling operations. M.S thesis, Texas Tech University, Lubbock, Texas, United States
Palanisamy P, Rajendran I, Shanmugasundaram S (2007) Optimization of machining parameters using genetic algorithm and experimental validation for end-milling operations. Int J Adv Manuf Technol 32:644–655. doi:10.1007/s00170-005-0384-3
Martellotti ME (1941) An analysis of the milling process. Trans ASME 63:667
Quintana G, Ciurana J, Ribatallada J (2010) Surface roughness generation and material removal rate in ball end milling operations. Mater Manuf Process 25(6):386–398. doi:10.1080/15394450902996601
Mansour A, Abdalla H (2002) Surface roughness model for end milling: a semi-free cutting carbon casehardening steel (EN 32) in dry condition. J Mater Process Technol 124(1–2):183–191. doi:10.1016/S0924-0136(02)00135-8
Alauddin M, El Baradie MA, Hashmi MSJ (1997) Prediction of tool life in end milling by response surface methodology. J Mater Process Technol 71(3):456–465. doi:10.1016/S0924-0136(97)00111-8
Chang H, Kim J, 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
Coker SA, Shin YC (1996) In-process control of surface roughness due to tool wear using a new ultrasonic system. Int J Mach Tools Manuf 36(3):411–422. doi:10.1016/0890-6955(95)00057-7
Gologlu C, Sakarya N (2008) The effects of cutter path strategies on surface roughness of pocket milling of 1.2738 steel based on Taguchi method. J Mater Process Technol; 206(1–3):7–15. doi:10.1016/j.jmatprotec.2007.11.300
Dhokia VG, Kumar S, Vichare P, Newman ST (2008) An intelligent approach for the prediction of surface roughness in ball-end machining of polypropylene. Robot Comput Integr Manuf; 24(6):835–842. doi:10.1016/j.rcim.2008.03.019
Lou SJ, Chen JC (1999) In-process surface roughness recognition (ISRR) system in end-milling operations. Int J Adv Manuf Technol 15(3):200–209. doi:10.1007/s001700050057
Tsai Y, Chen JC, Lou S (1999) An in-process surface recognition system based on neural networks in end milling cutting operations. Int J Mach Tools Manuf 39(4):583–605. doi:10.1016/S0890-6955(98)00053-4
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
Brecher C, Quintana G, Rudolf T, Ciurana J (2011) Use of NC kernel data for surface roughness monitoring in milling operations. Inter J Adv Manuf, Technol 53(9–12):953–962
Chen JC, Lou MS (2000) Fuzzy-nets based approach using an accelerometer for in-process surface roughness prediction system in milling operations. J Comput Integr Manuf Syst 13(4):358–368. doi:10.1080/095119200407714
Ali Y, Zhang L (1999) Surface roughness prediction of ground components using a fuzzy logic approach. J Mater Process Technol 89–90:561–568. doi:10.1016/S0924-0136(99)00022–9
Zain AM, Haron H, Sharif S (2008). An overview of GA technique for surface roughness optimization in milling process. In IEEE proc inter symp on infor technol, IT Sim, 4: 1–6 doi: 10.1109/ITSIM.2008.4631925
Brezocnik M, Kovacic M (2003) Integrated genetic programming and genetic algorithm approach to predict surface roughness. Mater Manuf Process 18(3):475–491. doi:10.1081/AMP-120022023
Oktem H, Erzurumlu T, Erzincanli F (2006) Prediction of minimum surface roughness in end milling mold parts using neutral network and genetic algorithm. J Mater Des 27(9):735–744. doi:10.1016/j.matdes.2005.01.010
Tansel IN, Ozcelik B, Bao WY, Chen P, Rincon D, Yang SY (2006) Selection of optimal cutting conditions by using GONNS. Int J Mach Tools Manuf 46(1):26–35. doi:10.1016/j.ijmachtools.2005.04.012
Suresh PVS, VenkateswaraRao 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
Brezonick M, Kovavic M, Ficko M (2004) Prediction of surface roughness with genetic programming. J Mater Process Technol 157–158:28–36. doi:10.1016/j.jmatprotec.2004.09.004
Colak O, Kurbanoglu C, Kayacan MC (2007) Milling surface roughness prediction using evolutionary programming methods. J Mater Des 28(2):657–666. doi:10.1016/j.matdes.2005.07.004
Oktem H, Erzurumlu T, Kurtaran H (2005) Application of response surface methodology in the optimization of cutting conditions for surface roughness. J Mater Process Technol 170(1–2):11–16. doi:10.1016/j.jmatprotec.2005.04.096
Bissey S, Poulachon G, Lapujoulade F (2007) Modelling of tool geometry in prediction of cutting force during milling of hard materials. Mach Sci Technol Int J 9(1):101–115. doi:10.1081/MST-200051376
Zain AM, Haron H, Sharif S (2010) Application of GA to optimize cutting conditions for minimizing surface roughness in end milling machining process. Expert Syst Appl 37(6):4650–4659. doi:10.1016/j.eswa.2009.12.043
AzlanMohdZain HH, Sharif S (2011) Integration of simulated annealing and genetic algorithm to estimate optimal solutions for minimizing surface roughness in end milling Ti-6AL-4 V. Int J Comput Int Manuf 24(6):574–592. doi:10.1080/0951192X.2011.566629
Kaneeda (1991) TCFRP cutting mechanism. Trans N Am Manuf Res Inst SME 19:216–221
Kim Y-H, Ko S-L (2002) Development of design and manufacturing technology for end mill in machining hardened steel. J Mater Process Technol 130–131:653–661. doi:10.1016/S0924-0136(02)00728-8
Suresh Kumar Reddy N, Venkateswara Rao P (2005) Selection of optimum tool geometry and cutting conditions using a surface roughness prediction model for end milling. Int J Adv Manuf Technol 26(11–12):1202–1210. doi:10.1007/s00170-004-2110-y
Suresh Kumar Reddy N, Venkateswara Rao P (2006) Experimental investigation to study the effect of solid lubricants on cutting forces and surface quality in end milling. Int J Mach Tools Manuf 46:189–198. doi:10.1016/j.ijmachtools.2005.04.008
Suresh Kumar Reddy N, Venkateswara Rao P (2007) A genetic algorithmic approach for optimization of surface roughness prediction model in dry milling. Mach Sci Technol Int J 9(1):63–84. doi:10.1081/MST-200051263
Yesilyurt I (2006) End mill breakage detection using mean frequency analysis of scalogram. Int J Mach Tools Manuf 46(3–4):450–458. doi:10.1016/j.ijmachtools.2005.03.014
Goldberg DE (1989) Genetic algorithms in search, optimization, and machine learning. Addison-Wesley Longman Publishing Co, Inc, Boston
Bernardos PG, Vosniakos GC (2003) Predicting surface roughness in machining: a review. Int J Mach Tools Manuf 43(8):833–844. doi:10.1016/S0890-6955(03)00059-2
Montgomery DC (2001) Design and analysis of experiments. Wiley, New York
Cochran WG, Cox GM (1957) Experimental designs. Wiley, New York
Sivasakthivel PS, VelMurugan V, Sudhakaran R (2011) Prediction of vibration amplitude from machining parameters by response surface methodology in end milling. Int J Adv Manuf Technol 53(5–8):453–461. doi:10.1007/s00170-010-2872-3
Sivasakthivel PS, Sudhakaran R (2013) Optimization of machining parameters on temperature rise in end milling of Al 6063 using response surface methodology and genetic algorithm. Int J Adv Manuf Technol 67(9–12):2313–2323. doi:10.1007/s00170-012-4652-8
Hindustan Machine Tools (HMT) (2001) Production technology, Tata McGraw-Hill Education
Kannan T, Murugan N (2006) Prediction of ferrite number of duplex stainless steel clad metals using RSM. Weld J (AWS) 85(5):91–100
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Mahesh, G., Muthu, S. & Devadasan, S.R. Prediction of surface roughness of end milling operation using genetic algorithm. Int J Adv Manuf Technol 77, 369–381 (2015). https://doi.org/10.1007/s00170-014-6425-z
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00170-014-6425-z