Minimization of turning time for high-strength steel with a given surface roughness using the Edgeworth–Pareto optimization method

  • A. T. Abbas
  • D. Yu. Pimenov
  • I. N. Erdakov
  • T. Mikolajczyk
  • E. A. El Danaf
  • M. A. Taha
Open Access


High-strength steels are used in various civilian and military products. The initial cost of the raw materials for these products is very high. The surface roughness of these products is extremely important during the finishing pass to be accepted during the final inspection. The surface roughness should conform to the required values stated on the design drawing. The paper presents the results of experiments in turning of high-strength steel featuring three factors—cutting speed V, feed rate f, and depth of cut t—on five levels (125 specimens). These were divided into 25 groups. Each of the five groups was subjected to one common machining speed. Each group was machined using five levels of cutting depth. Each depth was processed using five levels of feed rate. Tessa was used for examination of surface roughness. There is little modern research on machining high-strength steel. The high cost of this material compels us to look for the optimum turning conditions to provide for the specified roughness of surface Ra and the minimum machining time of unit volume T m . As a result of our study, an artificial neural network was designed in Matlab on the basis of the MLP 3-10-1 multilayer perceptron that allows us to predict Ra of the workpiece with ±2.14% accuracy within the range of the experimental cutting speed, depth of cut, and feed rate values. For the first time, a Pareto frontier was obtained for Ra and T m of the finished workpiece from high-strength steel using the artificial neural network model that was later used to determine the optimum cutting conditions. It is possible to integrate the suggested optimization algorithms into computer-aided manufacturing using Matlab.


Artificial neural network High-strength steel Turning operation Optimization Edgeworth–Pareto method Surface roughness Data mining 


  1. 1.
    Risbood KA, Dixit US, Sahasrabudhe AD (2003) Prediction of surface roughness and dimensional deviation by measuring cutting forces and vibrations in turning process. J Mater Process Technol 132(1–3):203–214. doi: 10.1016/S0924-0136(02)00920-2 CrossRefGoogle Scholar
  2. 2.
    Bajić D, Lela B, Cukor G (2008) Examination and modelling of the influence of cutting parameters on the cutting force and the surface roughness in longitudinal turning. Strojniski Vestn / J Mech Eng 54(5):322–333Google Scholar
  3. 3.
    Muthukrishnan N, Davim JP (2009) Optimization of machining parameters of al/SiC-MMC with ANOVA and ANN analysis. J Mater Process Technol 209(1):225–232. doi: 10.1016/j.jmatprotec.2008.01.041 CrossRefGoogle Scholar
  4. 4.
    Ali SM, Dhar NR (2010) Tool wear and surface roughness prediction using an artificial neural network (ANN) in turning steel under minimum quantity lubrication (MQL). World Acad Sci Eng Technol 62:830–839Google Scholar
  5. 5.
    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. doi: 10.1007/s00170-009-2456-2 CrossRefGoogle Scholar
  6. 6.
    Natarajan C, Muthu S, Karuppuswamy P (2011) Prediction and analysis of surface roughness characteristics of a non-ferrous material using ANN in CNC turning. Int J Adv Manuf Technol 57(9–12):1043–1051. doi: 10.1007/s00170-011-3343-1 CrossRefGoogle Scholar
  7. 7.
    Svalina I, Sabo K, Šimunović G (2011) Machined surface quality prediction models based on moving least squares and moving least absolute deviations methods. Int J Adv Manuf Technol 57(9–12):1099–1106. doi: 10.1007/s00170-011-3353-z CrossRefGoogle Scholar
  8. 8.
    Abdullah AA, Naeem UJ, Xiong C (2012) Estimation and optimization cutting conditions of surface roughness in hard turning using Taguchi approach and artificial neural network. Adv Mater Res 463–464:662–668. doi: 10.4028/ CrossRefGoogle Scholar
  9. 9.
    Pontes FJ, Paiva APD, Balestrassi PP, Ferreira JR, Silva MBD (2012) Optimization of radial basis function neural network employed for prediction of surface roughness in hard turning process using Taguchi’s orthogonal arrays. Expert Syst Appl 39(9):7776–7787. doi: 10.1016/j.eswa.2012.01.058 CrossRefGoogle Scholar
  10. 10.
    Asiltürk I (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 CrossRefGoogle Scholar
  11. 11.
    Upadhyay V, Jain PK, Mehta NK (2013) In-process prediction of surface roughness in turning of Ti-6Al-4V alloy using cutting parameters and vibration signals. Measurement 46(1):154–160. doi: 10.1016/j.measurement.2012.06.002 CrossRefGoogle Scholar
  12. 12.
    Ahilan C, Kumanan S, Sivakumaran N, Edwin Raja Dhas J (2013) Modeling and prediction of machining quality in CNC turning process using intelligent hybrid decision making tools. Appl Soft Comput 13(3):1543–1551. doi: 10.1016/j.asoc.2012.03.071 CrossRefGoogle Scholar
  13. 13.
    Azam M, Jahanzaib M, Wasim A, Hussain S (2015) Surface roughness modeling using RSM for HSLA steel by coated carbide tools. Int J Adv Manuf Technol 78(5-8):1031–1041. doi: 10.1007/s00170-014-6707-5 CrossRefGoogle Scholar
  14. 14.
    Acayaba GMA, Escalona PMD (2015) Prediction of surface roughness in low speed turning of AISI316 austenitic stainless steel. CIRP J Manuf Sci Technol 11:62–67. doi: 10.1016/j.cirpj.2015.08.004 CrossRefGoogle Scholar
  15. 15.
    Al Bahkali EA, Ragab AE, El Danaf EA, Abbas AT (2016) An investigation of optimum cutting conditions in turning nodular cast iron using carbide inserts with different nose radius. Proc Inst Mech Eng B J Eng Manuf 230(9):1584–1591 CrossRefGoogle Scholar
  16. 16.
    Mia M, Dhar NR (2016) Prediction of surface roughness in hard turning under high pressure coolant using artificial neural network. Measurement 92:464–474. doi: 10.1016/j.measurement.2016.06.048 CrossRefGoogle Scholar
  17. 17.
    Jurkovic Z, Cukor G, Brezocnik M, Brajkovic T. (2016) A comparison of machine learning methods for cutting parameters prediction in high speed turning process. J Intell Manuf Article in press:1-11. doi: 10.1007/s10845-016-1206-1
  18. 18.
    Tootooni MS, Liu C, Roberson D, Donovan R, Rao PK, Kong ZJ, Bukkapatnam STS (2016) Online non-contact surface finish measurement in machining using graph theory-based image analysis. J Manuf Syst 41:266–276. doi: 10.1016/j.jmsy.2016.09.007 CrossRefGoogle Scholar
  19. 19.
    Abbas AT (2016) Influence of process parameters on the surface roughness during turning operation of high strength steel. J Mater Sci Res 5(2):1927–0593. doi: 10.5539/jmsr.v5n2p100 Google Scholar
  20. 20.
    Zuperl U, Cus F (2003) Optimization of cutting conditions during cutting by using neural networks. Robot Comput Integr Manuf 19(1–2):189–199. doi: 10.1016/S0736-5845(02)00079-0 CrossRefMATHGoogle Scholar
  21. 21.
    Senthilkumaar JS, Selvarani P, Arunachalam RM (2012) Intelligent optimization and selection of machining parameters in finish turning and facing of Inconel 718. Int J Adv Manuf Technol 58(9–12):885–894. doi: 10.1007/s00170-011-3455-7 CrossRefGoogle Scholar
  22. 22.
    Zinati RF, Razfar MR (2012) Constrained optimum surface roughness prediction in turning of X20Cr13 by coupling novel modified harmony search-based neural network and modified harmony search algorithm. Int J Adv Manuf Technol 58(1–4):93–107. doi: 10.1007/s00170-011-3393-4 CrossRefGoogle Scholar
  23. 23.
    Jafarian F, Taghipour M, Amirabadi H (2013) Application of artificial neural network and optimization algorithms for optimizing surface roughness, tool life and cutting forces in turning operation. J Mech Sci Technol 27(5):1469–1477. doi: 10.1007/s12206-013-0327-0 CrossRefGoogle Scholar
  24. 24.
    Mokhtari Homami R, Fadaei Tehrani A, Mirzadeh H, Movahedi B, Azimifar F (2014) Optimization of turning process using artificial intelligence technology. Int J Adv Manuf Technol 70(5–8):1205–1217. doi: 10.1007/s00170-013-5361-7 CrossRefGoogle Scholar
  25. 25.
    Tamang SK, Chandrasekaran M. (2015) Modeling and optimization of parameters for minimizing surface roughness and tool wear in turning Al/SiCp MMC, using conventional and soft computing techniques. Advances in Production Engineering And Management 10(2):59–72. Doi: 10.14743/apem2015.2.192
  26. 26.
    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. doi: 10.1016/j.procir.2015.02.002 CrossRefGoogle Scholar
  27. 27.
    Gupta AK, Guntuku SC, Desu RK, Balu A (2015) Optimisation of turning parameters by integrating genetic algorithm with support vector regression and artificial neural networks. Int J Adv Manuf Technol 77(1–4):331–339. doi: 10.1007/s00170-014-6282-9 CrossRefGoogle Scholar
  28. 28.
    Basak S, Dixit US, Davim JP (2007) Application of radial basis function neural networks in optimization of hard turning of AISI D2 cold-worked tool steel with a ceramic tool. Proc Inst Mech Eng B J Eng Manuf 221(6):987–998. doi: 10.1243/09544054JEM737 CrossRefGoogle Scholar
  29. 29.
    Karpat Y, Özel T (2007) Multi-objective optimization for turning processes using neural network modeling and dynamic-neighborhood particle swarm optimization. Int J Adv Manuf Technol 35(3–4):234–247. doi: 10.1007/s00170-006-0719-8 CrossRefGoogle Scholar
  30. 30.
    Yue C, Wang L, Liu J, Hao S (2016) Multi-objective optimization of machined surface integrity for hard turning process. Int J Smart Home 10(6):71–76. doi: 10.14257/ijsh.2016.10.6.08 CrossRefGoogle Scholar
  31. 31.
    Abbas AT, Hamza K, Aly MF, Al-Bahkali EA (2016) Multiobjective optimization of turning cutting parameters for j-steel material. Mater Sci Eng 6429160:8. doi: 10.1155/2016/6429160 Google Scholar
  32. 32.
    Feng C-XJ YZ-GS, Kingi U, Pervaiz BM (2005) Threefold vs. fivefold cross validation in one-hidden-layer and two-hidden-layer predictive neural network modeling of machining surface roughness data. J Manuf Syst 24(2):93–107. doi: 10.1016/S0278-6125(05)80010-X CrossRefGoogle Scholar
  33. 33.
    Feng C-XJ YZ-GS, Emanuel JT, Li P-G, Shao X-Y, Wang Z-H (2008) Threefold versus fivefold cross-validation and individual versus average data in predictive regression modelling of machining experimental data. Int J Comput Integ Manuf 21(6):702–714. doi: 10.1080/09511920701530943 CrossRefGoogle Scholar
  34. 34.
    Asilturk I, Kahramanli H, El Mounayri H (2012) Prediction of cutting forces and surface roughness using artificial neural network (ANN) and support vector regression (SVR) in turning 4140 steel. Mater Sci Technol 28(8):980–986. doi: 10.1179/1743284712Y.0000000043 CrossRefGoogle Scholar
  35. 35.
    Statnikov RB, Matusov J (1996) Use of Pτ-nets for the approximation of the Edgeworth-Pareto set in multicriteria optimization. J Optim Theory Appl 91(3):543–560MathSciNetCrossRefMATHGoogle Scholar
  36. 36.
    Berezkin VE, Lotov AV (2014) Comparison of two Pareto frontier approximations. Comp Math Math Phys 54(9):1402–1410. doi: 10.1134/S0965542514090048 MathSciNetCrossRefMATHGoogle Scholar
  37. 37.
    Lotov AV, Ryabikov AI, Buber AL (2014) Pareto frontier visualization in the development of release rules for hydro-electrical power stations. Sci Tech Inf Process 41(5):314–324. doi: 10.3103/S0147688214050025 CrossRefGoogle Scholar
  38. 38.
    Carvalho M, Ambrósio J, Eberhard P (2011) Identification of validated multibody vehicle models for crash analysis using a hybrid optimization procedure. Struct Multidisc Optim 44(1):85–97. doi: 10.1007/s00158-010-0590-y CrossRefGoogle Scholar
  39. 39.
    Choudhary AK, Harding JA, Tiwari MK (2009) Data mining in manufacturing: a review based on the kind of knowledge. J Intell Manuf 20(5):501–521. doi: 10.1007/s10845-008-0145-x CrossRefGoogle Scholar
  40. 40.
    Wang X, Feng CX (2002) Development of empirical models for surface roughness prediction in finish turning. Int J Adv Manuf Technol 20(5):348–356. doi: 10.1007/s001700200162 CrossRefGoogle Scholar
  41. 41.
    Al-Ahmari AMA (2007) Predictive machinability models for a selected hard material in turning operations. J Mater Process Technol 190(1–3):305–311. doi: 10.1016/j.jmatprotec.2007.02.031 CrossRefGoogle Scholar
  42. 42.
    Jack Feng C-X, Yu Z-GS, Kingi U, Pervaiz BM (2005) Threefold vs. fivefold cross validation in one-hidden-layer and two-hidden-layer predictive neural network modeling of machining surface roughness data. J Manuf Syst 24(2):93–107. doi: 10.1016/S0278-6125(05)80010-X CrossRefGoogle Scholar
  43. 43.
    Nogin VD. Decision making in multicriteria environment: quantitative approach. M.: FIZMATLIT, 2002, P.144. [in Russian] Google Scholar
  44. 44.
    Kostenetskiy PS, Safonov AY (2016) SUSU supercomputer resources. CEUR Workshop Proceedings 1576:561–573Google Scholar

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© The Author(s) 2017

Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (, which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Authors and Affiliations

  • A. T. Abbas
    • 1
  • D. Yu. Pimenov
    • 2
  • I. N. Erdakov
    • 3
  • T. Mikolajczyk
    • 4
  • E. A. El Danaf
    • 1
  • M. A. Taha
    • 5
  1. 1.Department of Mechanical Engineering, College of EngineeringKing Saud UniversityRiyadhSaudi Arabia
  2. 2.Department of Automated Mechanical EngineeringSouth Ural State UniversityChelyabinskRussia
  3. 3.Department of Pyrometallurgical and Casting TechnologiesSouth Ural State UniversityChelyabinskRussia
  4. 4.Department of Production EngineeringUTP University of Science and TechnologyBydgoszczPoland
  5. 5.Department of Mechanical Design and Production, Faculty of EngineeringZagazig UniversityZagazigEgypt

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