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Quality characteristics optimization in CNC end milling of A36 K02600 using Taguchi’s approach coupled with artificial neural network and genetic algorithm

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Abstract

This study investigates surface roughness and energy consumption in a CNC end milling of plain low carbon steel (mild steel) A36 K02600 using a carbide end mill cutter. Taguchi’s L9 orthogonal array was adopted for designing the experimental runs considering several process parameters viz. cutting velocity, Feed rate, spindle speed and cutting depth in order to study their influence on the quality characteristics. Optimal control parameter combinations for minimizing surface roughness and energy consumption were evaluated from signal to noise ratio. Analysis of variance revealed the contribution of control factors on the quality characteristics. Numerical predictive models using linear regression and artificial neural network were developed to envisage the responses accurately. Multi-objective Genetic Algorithm optimization was exploited in order to obtain a specific set of control parameter which would optimize both the responses simultaneously. This study concludes that spindle speed (68.24% contribution) and feed rate (92% contribution) are the most responsible variables for surface quality and energy consumption respectively. The outcome of artificial neural network model and genetic algorithm confirm that both quality characteristics can be optimized simultaneously and Taguchi’s robust design approach is a successful tactic for optimizing machining parameters to achieve desired surface quality at low energy consumption. Improvement in surface quality and reduction in energy consumption were found to be 27.79% and 30% respectively. Low carbon steel is extensively accepted by the industries for its wide variety of which make this study physically viable.

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Abbreviations

ANN:

Artificial neural network

GA:

Genetic algorithm

CNC:

Computer numerical control

ANOVA:

Analysis of variance

OA:

Orthogonal array

S/N ratio:

Signal to noise ratio

MSE:

Mean square error

EC:

Energy consumption (kWh)

Ra :

Surface roughness (µm)

SS:

Spindle speed (rpm)

FR:

Feed rate (mm/min)

DOC:

Depth of cut/cutting depth (mm)

Vc :

Cutting velocity (mm/min)

n:

Total number of responses

y i :

Observed data

N:

Number of inputs

w ij :

Associated weights

x i :

Number of inputs in ith neuron

β:

Constant

µ:

Unit of length

η :

S/N ratio

Σ:

Summation

References

  • Ahmed S, Arora R (2017) Optimization of turning parameters of aluminum 6351 T6 using Taguchi decision making technique. Int J Data Netw Sci 1(2):27–38. https://doi.org/10.5267/j.ijdns.2017.1.008

    Article  Google Scholar 

  • Arnaiz-González Á, Fernández-Valdivielso A, Bustillo A, de Lacalle LNL (2016) Using artificial neural networks for the prediction of dimensional error on inclined surfaces manufactured by ball-end milling. Int J Adv Manuf Technol 83(5–8):847–859

    Article  Google Scholar 

  • Arora R, Arora R (2018a) Multiobjective optimization and analytical comparison of single- and 2-stage (series/parallel) thermoelectric heat pumps. Int J Energy Res 42(4):1760–1778

    Article  Google Scholar 

  • Arora R, Arora R (2018b) Multicriteria optimization based comprehensive comparative analyses of single-and two-stage (series/parallel) thermoelectric generators including the influence of thomson effect. J Renew Sustain Energy 10(4):044701

    Article  Google Scholar 

  • Arora R, Kaushik SC, Kumar R (2015a) Multi-objective and multi-parameter optimization of two-stage thermoelectric generator in electrically series and parallel configurations through NSGA-II. Energy 91:242–254

    Article  Google Scholar 

  • Arora R, Kaushik SC, Kumar R (2015b) Multi-objective optimization of solar powered Ericsson cycle using genetic algorithm and fuzzy decision making. In: 2015 International conference on advances in computer engineering and applications (ICACEA), IEEE, pp 553–558. https://doi.org/10.1109/icacea.2015.7164754

  • Arora R, Kaushik SC, Kumar R (2015c) Multi-objective optimization of an irreversible regenerative Brayton cycle using genetic algorithm. In: 2015 international conference on futuristic trends on computational analysis and knowledge management (ABLAZE), IEEE, pp 340–346. https://doi.org/10.1109/ablaze.2015.7155017

  • Arora R, Kaushik SC, Kumar R (2016a) Thermodynamic modelling and multi-objective optimization of two-stage thermoelectric generator in electrically series and parallel configurations. Appl Therm Eng 25(103):1312–1323

    Article  Google Scholar 

  • Arora R, Kaushik SC, Kumar R (2016b) Multi-objective thermodynamic optimization of solar parabolic dish stirling heat engine with regenerative losses using nsga-ii and decision making. Appl Sol Energy 52(4):295–304

    Article  Google Scholar 

  • Arora R, Kaushik SC, Kumar R, Arora R (2016c) Multi-objective thermo-economic optimization of solar parabolic dish stirling heat engine with regenerative losses using NSGA-II and decision making. Int J Elect Power Energy Syst 74:25–35

    Article  Google Scholar 

  • Arora R, Kaushik SC, Kumar R, Arora R (2016d) Soft computing based multi-objective optimization of Brayton cycle power plant with isothermal heat addition using evolutionary algorithm and decision making. Appl Soft Comput 46:267–283

    Article  Google Scholar 

  • Arora R, Kaushik SC, Kumar R (2017) Multi-objective thermodynamic optimisation of solar parabolic dish stirling heat engine using NSGA-II and decision making. Int J Renew Energy Technol 8(1):64–92

    Article  Google Scholar 

  • Chien WT, Tsai CS (2003) The investigation on the prediction of tool wear and the determination of optimum cutting conditions in machining 17-4PH stainless steel. J Mater Process Technol 140(1–3):340–345. https://doi.org/10.1016/S0924-0136(03)00753-2

    Article  Google Scholar 

  • Ding L, Yue Y, Ahmet K, Jackson M, Parkin R (2005) Global optimization of a feature-based process sequence using GA and ANN techniques. Int J Prod Res 43(15):3247–3272. https://doi.org/10.1080/00207540500137282

    Article  MATH  Google Scholar 

  • Garg A, Tai K (2012) Review of genetic programming in modeling of machining processes. In: Proceedings of international conference on modelling, identification and control (ICMIC), 2012, pp. 653–658, IEEE

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

    Article  Google Scholar 

  • Gopalsamy BM, Mondal B, Ghosh S (2009) Taguchi method and ANOVA: an approach for process parameters optimization of hard machining while machining hardened steel. J Sci Ind Res 66(8):686–695. https://doi.org/10.1007/s00170-015-7543-y

    Article  Google Scholar 

  • Huang HX, Li JC, Xiao CL (2015) A proposed iteration optimization approach integrating backpropagation neural network with genetic algorithm. Exp Syst Appl 42(1):146–155. https://doi.org/10.1016/j.eswa.2014.07.039

    Article  Google Scholar 

  • Kant G, Sangwan KS (2015) Predictive modelling and optimization of machining parameters to minimize surface roughness using artificial neural network coupled with genetic algorithm. Procedia CIRP 31:453–458. https://doi.org/10.1016/j.procir.2015.03.043

    Article  Google Scholar 

  • Kaushik SC, Kumar R, Arora R (2016) Thermo-economic optimization and parametric study of an irreversible Brayton heat engine cycle. J Therm Eng 2(4):861–870

    Google Scholar 

  • Khorasani A, Yazdi MRS (2017) Development of a dynamic surface roughness monitoring system based on artificial neural networks (ANN) in milling operation. Int J Adv Manuf Technol 93(1–4):141–151. https://doi.org/10.1007/s00170-015-7922-4

    Article  Google Scholar 

  • Kıvak T (2014) Optimization of surface roughness and flank wear using the Taguchi method in milling of hadfield steel with PVD and CVD coated inserts. Measurement 50:19–28. https://doi.org/10.1016/j.measurement.2013.12.017

    Article  Google Scholar 

  • Krishnan SA, Samuel GL (2013) Multi-objective optimization of material removal rate and surface roughness in wire electrical discharge turning Int. J Adv Manuf Technol 67(9–12):2021–2032. https://doi.org/10.1007/s00170-012-4628-8

    Article  Google Scholar 

  • Kumar R, Kaushik SC, Kumar R, Hans R (2016) Multi-objective thermodynamic optimization of irreversible regenerative brayton cycle using evolutionary algorithm and decision making. Ain Shams Eng J 7(2):741–753

    Article  Google Scholar 

  • Oda Y, Kawamura Y, Fujishima M (2012) Energy consumption reduction by machining process improvement. Procedia CIRP 4:120–124. https://doi.org/10.1016/j.procir.2012.10.022

    Article  Google Scholar 

  • Öktem H (2009) An integrated study of surface roughness for modelling and optimization of cutting parameters during end milling operation. Int J Adv Manuf Technol 43(9–10):852–861. https://doi.org/10.1007/s00170-008-1763-3

    Article  Google Scholar 

  • Ozcelik 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(3–4):234–241. https://doi.org/10.1007/s00170-004-2175-7

    Article  Google Scholar 

  • Pontes FJ, Ferreira JR, Silva MB, Paiva AP, Balestrassi PP (2010) Artificial neural networks for machining processes surface roughness modeling. Int J Adv Manuf Technol 49(9–12):879–902. https://doi.org/10.1007/s00170-009-2456-2

    Article  Google Scholar 

  • Rangajanardhaa G, Rao S (2009) Development of hybrid model and optimization of surface roughness in electric discharge machining using artificial neural networks and genetic algorithm. J Mater Process Technol 209(3):1512–1520. https://doi.org/10.1016/j.jmatprotec.2008.04.003

    Article  Google Scholar 

  • Rao RV, Kalyankar VD (2014) Optimization of modern machining processes using advanced optimization techniques: a review. Int J Adv Manuf Technol 73(5–8):1159–1188. https://doi.org/10.1007/s00170-014-5894-4

    Article  Google Scholar 

  • Sahin Y, Motorcu AR (2005) Surface roughness model for machining mild steel with coated carbide tool. Mater Des 26(4):321–326. https://doi.org/10.1016/j.matdes.2004.06.015

    Article  Google Scholar 

  • Sahu NK, Andhare AB (2017) Modelling and multiobjective optimization for productivity improvement in high speed milling of Ti–6Al–4 V using RSM and GA. J Braz Soc Mech Sci Eng 39(12):5069–5085. https://doi.org/10.1007/s40430-017-0804-y

    Article  Google Scholar 

  • Sangwan KS, Saxena S, Kant G (2015) Optimization of machining parameters to minimize surface roughness using integrated ANN-GA approach. Procedia CIRP 29:305–310. https://doi.org/10.1016/j.procir.2015.02.002

    Article  Google Scholar 

  • Sivasakthivel PS, VelMurugan V, Sudhakaran R (2012) Experimental evaluation of surface roughness for end milling of Al 6063: response surface and neural network model. Int J Manuf Res 7(1):9–25. https://doi.org/10.1504/IJMR.2012.045241

    Article  Google Scholar 

  • Tzeng CJ, Chen RY (2013) Optimization of electric discharge machining process using the response surface methodology and genetic algorithm approach. Int J Precis Eng Manuf 14(5):709–717. https://doi.org/10.1007/s12541-013-0095-x

    Article  Google Scholar 

  • Zain AM, Haron H, Sharif S (2008) An overview of GA technique for surface roughness optimization in milling process. In: International symposium on information technology, ITSim 2008. vol 4, pp 1–6. IEEE. https://doi.org/10.1109/ITSIM.2008.4631925

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Authors and Affiliations

  1. Department of Mechanical Engineering, Amity University Haryana, Gurgaon, 122413, India

    Shofique U. Ahmed & Rajesh Arora

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  1. Shofique U. Ahmed
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  2. Rajesh Arora
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Correspondence to Rajesh Arora.

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Ahmed, S.U., Arora, R. Quality characteristics optimization in CNC end milling of A36 K02600 using Taguchi’s approach coupled with artificial neural network and genetic algorithm. Int J Syst Assur Eng Manag 10, 676–695 (2019). https://doi.org/10.1007/s13198-019-00796-8

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  • DOI: https://doi.org/10.1007/s13198-019-00796-8

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