Abstract
To ensure the quality of machined products at minimum cost and maximum effectiveness, it is crucial that selection of optimum machining parameters should be done when computer numerically controlled (CNC) machine tools technology is employed. Traditionally, experience of the operator plays a major role in the selection of efficient parameter values; however, attaining optimum ones each time by even skilled end users, is extremely difficult. This paper takes advantage of the possibilities of current computer-aided design (CAD)/computer-aided manufacturing (CAM) technology and implements a genetic algorithm for optimising CNC machining operations mainly for sculptured surfaces. The algorithm has been developed as a hosted application to a cutting-edge CAD/CAM system. Collaboration among applications has been achieved through programming for software automation by utilising the application programme interface of the system. The approach was implemented to a group of test sculptured models with different properties whilst one of them has been actually machined using typical resources. Results obtained after the implementation indicated that the methodology is capable of providing optimum values for process parameters on its way to maintain both productivity and high quality.
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References
Baptista R, Simoes JFA (2000) Three and five axes milling of sculptured surfaces. Int J Mater Process Technol 103:398–403
Fountas NA, Vaxevanidis NM, Stergiou CI, Benhadj-Djilali R (2014) Evaluation of 3- and 5-axis sculptured surface machining in CAM environment through design of experiments. Int J Comp Int Manuf http://dx.doi.org/10.1080/0951192X.2013.875627
Yusoff AR, Hassan MF, Mansor MH (2012) Multiobjective optimization of milling tool geometry for chatter suppression and productivity improvement. Adv Mater Res 445(21):21–26
Lin T, Lee J-W, Bohez ELJ (2009) A new accurate curvature matching and optimal tool based five-axis machining algorithm. J Mech Sci Technol 23:2624–2634
Radzevich SP (2002) Conditions of proper sculptured surface machining. Comput-Aided Des 34:727–740
Li H, Dong Z, Vickers GW (1994) Optimal tool path pattern identification for single island, sculptured part rough machining using fuzzy pattern analysis. Comput-Aided Des 26(11):787–795
Zhang X, Zhu L, Zhang D, Ding H, Xiong Y (2012) Numerical robust optimization of spindle speed for milling process with uncertainties. Int J Mach Tools Manuf 61:9–19
Corso LL, Zeilmann RP, Nicola GL, Missell FP, Gomes HM (2013) Using optimization procedures to minimize machining time while maintaining surface quality. Int J Adv Manuf Technol 65(9–12):1659–1667
DeLacalle LN, Lamikiz A, Sanchez JA, Salgado MA (2007) Tool path selection based on the minimum deflection cutting forces in the programming of complex surfaces milling. Int J Mach Tools Manuf 47:388–400
Lazoglu I, Manav C, Murtezaoglu Y (2009) Tool path optimization for free form surface machining CIRP. Annals Manuf Technol 58:101–104
Wojciechowski S, Twardowski P (2012) Tool life and process dynamics in high speed ball end milling of hardened steel. 5th CIRP Conf On High Performance Cutting – Proc CIRP 1:289–294
Milfelner M, Kopac J, Cus F, Zuperl U (2006) Intelligent system for machining and optimization of 3D sculptured surfaces with ball-end milling. J Achiev Mater Manuf Eng 14(1–2):171–177
Gajate A, Bustillo A, Haber RE (2012) Transductive neurofuzzy-based torque control of a milling process: results of a case study. Int J Innov Comput Inf Control 8(5b):3495–3510
Ozkelik B, Oktem H, Kurtaran H (2005) Optimum surface roughness in end milling Inconel 718 by coupling neural network model and genetic algorithm. Int J Adv Manuf Technol 27:234–241
Tandon V, El-Mounayri H, Kishawy H (2002) NC end milling optimization using evolutionary computation. Int J Mach Tools Manuf 42:595–605
Arokiadass R, Palaniradja K, Alagumoorthi N (2012) Tool flank wear model and parametric optimization in end milling of metal matrix composite using carbide tool: response surface methodology approach. Int J Ind Eng Comput 3:511–518
Mustafa A (2011) Determination and optimization of the machining parameters of the welded areas in moulds. Sci Res Essays 6(2):485–492
Erturk A, Ozguven H, Budak E (2006) Analytical modeling of spindle—tool dynamics on machine tools using Timoshenko beam model and receptance coupling for the prediction of tool point FRF. Int J Mach Tools Manuf 46(15):1901–1912
Lin CA, Liu HT (1998) Automatic generation of NC cutter path from massive data points. Comput-Aided Des 30(1):77–90
Tunc LT, Budak E (2009) Extraction of 5-axis milling conditions from CAM data for process simulation. Int J Adv Manuf Technol 43:538–550
Gong H, Cao L-X, Liu J (2008) Second order approximation of tool envelope surface for 5-axis machining with single point contact. Comput-Aided Des 40:604–615
Manav C, Bank HS, Lazoglu I (2013) Intelligent tool path selection via multi-criteria optimization in complex sculptured surface milling. J Intell Manuf 24:349–355
Wu PH, Li YW, Chu CH (2008) Tool path planning for 5-axis flank milling based on dynamic programming techniques. Advances in Geometric Modeling and Processing-Lecture Notes in Comput Sci 4975:570–577
Agrawal RK, Pratihar DK, Choudhury AR (2006) Optimization of CNC iso-scallop free form surface machining using a genetic algorithm. Int J Mach Tools Manuf 46:811–819
Afifi AA, Hayhurst DR, Khan WA (2011) Non-productive tool path optimisation for four-axis milling using the simulated annealing algorithm. Int J Prod Res 49(17):5277–5302
Hsieh H-T, Chu C-H (2009) Improving optimization of tool path planning in 5-axis flank milling using advanced PSO algorithms. Robot Comput Integr Manuf 29:3–11
Li L, Liu F, Chen B, Li CB (2013) Multi-objective optimization of cutting parameters in sculptured parts machining based on neural network. J Intell Manuf. doi:10.1007/s10845-013-0809-z
Vosniakos GC, Benardos PG, Krimpenis AA (2012) Intelligent optimisation of 3-axis sculptured surface machining on existing CAM systems. In: Davim JP (ed) Machining of complex sculptured surfaces. Springer, London, pp 157–190
Zeroudi N, Fontaine M, Necib K (2012) Prediction of cutting forces in 3-axes milling of sculptured surfaces directly from CAM tool path. J Intell Manuf 23(5):1573–1587
Del Prete A, Mazzotta D, Anglani A (2010) Design optimization application in accordance with product and process requirements. Adv En Soft 41(3):427–432
Kersting P, Zabel A (2009) Optimizing NC-tool paths for simultaneous five-axis milling based on multi-population multi-objective evolutionary algorithms. Adv En Soft 40:452–463
Antoniadis A, Savakis C, Bilalis N, Balouktsis A (2003) Prediction of surface topomorphy and roughness in ball-end milling. Int J Adv Manuf Technol 21:965–971
Fountas NA, Kimpenis AA, Vaxevanidis NM (2012) Computational techniques in statistical analysis and exploitation of CNC machining experimental data. In: Davim JP (ed) Computational methods for optimizing manufacturing technology: models and techniques. IGI-Global, Hershey, pp 111–143
Fountas NA, Kimpenis AA, Vaxevanidis NM, Davim JP (2012) Single and multi-objective optimization methodologies in CNC machining. In: Davim JP (ed) Statistical and computational methods in manufacturing. Springer, Heidelberg, pp 187–218
Kim IY, De Weck O (2004) Adaptive weighted sum method for multiobjective optimization. 10th AIAA/ISSMO multidisciplinary analysis and optimization conference 30 August–1 September 2004. American Institute of Aeronautics and Astronautics, Inc, Albany, New York
Fonseca CM, Correia MB (2005) Developing redundant binary representations for genetic search. Proc. IEEE CEC. Edinburgh, New York, 372–379
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Fountas, N.A., Vaxevanidis, N.M., Stergiou, C.I. et al. Development of a software-automated intelligent sculptured surface machining optimization environment. Int J Adv Manuf Technol 75, 909–931 (2014). https://doi.org/10.1007/s00170-014-6136-5
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DOI: https://doi.org/10.1007/s00170-014-6136-5