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Soft Computing

, Volume 23, Issue 13, pp 5213–5231 | Cite as

Optimization of different non-traditional turning processes using soft computing methods

  • Mehmet Alper SofuoğluEmail author
  • Fatih Hayati Çakır
  • Melih Cemal Kuşhan
  • Sezan Orak
Methodologies and Application
  • 180 Downloads

Abstract

In this study, different non-traditional turning operations were investigated using various soft computing methods. In these operations, cutting speed, machining method, material type and tool overhang lengths were used as machining inputs. Surface roughness, stable cutting depths and maximum cutting tool temperatures were considered as machining outputs. In the first stage, artificial neural network, classification and regression tree (CART) and support vector machine models were developed to predict these outputs. In the second stage, an optimization study (regression analysis) was conducted. CART model produced better prediction results compared to the other methods. In CART models; 0.991, 0.998 and 0.959 values of correlation coefficients were calculated for the prediction of surface roughness, stable cutting depth and maximum cutting tool temperatures, respectively. In the optimization study, ultrasonic assisted/hot ultrasonic assisted turning methods, a tool overhang length of 60 mm and a cutting speed of 10 m/min provide optimum conditions. The proposed soft computing models will help to understand the effect of various parameters in non-traditional machining methods. These models will give a preliminary idea before the experiments. These models can be used as an alternative instead of 2D finite element machining simulations. Less analysis time is required compared to the finite element simulations.

Keywords

Ultrasonic assisted turning Hot machining Surface roughness Chatter stability Soft computing Hastelloy-X Ti6Al4V Optimization ANN 

Notes

Compliance with ethical standards

Conflict of interest

All the authors declare that they have no conflict of interest.

Human and animal rights

This article does not contain any studies with human participants or animals performed by any of the authors.

References

  1. Acayaba GMA, Escalona PM (2015) Prediction of surface roughness in low speed turning of AISI316 austenitic stainless steel. CIRP J Manuf Sci Technol 11:62–67CrossRefGoogle Scholar
  2. Al Hazza MHF, Adesta EYT, Hasan MH, Shaffiar N (2014) Surface roughness modeling in high speed hard turning using regression analysis. Int Rev Mech Eng 8(2):431–436Google Scholar
  3. Amini S, Teimouri R (2017) Parametric study and multicharacteristic optimization of rotary turning process assisted by longitudinal ultrasonic vibration. Proc Inst Mech Eng Part E J Process Mech Eng 231(5):1–14CrossRefGoogle Scholar
  4. Amini S, Hosseinabadi HN, Sajjady SA (2016) Experimental study on effect of micro textured surfaces generated by ultrasonic vibration assisted face turning on friction and wear performance. Appl Surf Sci 390:633–648CrossRefGoogle Scholar
  5. Arsecularatne JA, Zhang LC, Montross C, Mathew P (2006) On machining of hardened AISI D2 steel with PCBN tools. J Mater Process Technol 171(2):244–252CrossRefGoogle Scholar
  6. Babitsky V, Kalashnikov A, Meadows A, Wijesundara AAH (2003) Ultrasonically assisted turning of aviation materials. J Mater Process Technol 132(1–3):157–167CrossRefGoogle Scholar
  7. Babitsky V, Mitrofanov A, Silberschmidt V (2004) Ultrasonically assisted turning of aviation materials: simulations and experimental study. Ultrasonics 42(1–9):81–86CrossRefGoogle Scholar
  8. Bai W, Sun R, Leopold J (2016) Numerical modelling of microstructure evolution in Ti6Al4V alloy by ultrasonic assisted cutting. Procedia CIRP 46:428–431CrossRefGoogle Scholar
  9. Bartarya G, Choudhur SK (2012) Effect of cutting parameters on cutting force and surface roughness during finish hard turning AISI52100 grade steel. Procedia CIRP 1:651–656CrossRefGoogle Scholar
  10. Benga GC, Abrao AM (2003) Turning of hardened 100Cr6 bearing steel with ceramic and PCBN cutting tools. J Mater Process Technol 143:237–241CrossRefGoogle Scholar
  11. Brehl DE, Dow TA (2008) Review of vibration-assisted machining. Precis Eng 32(3):153–172CrossRefGoogle Scholar
  12. Breiman L, Friedman JH, Olshen RA, Stone CJ (1984) Classification and Regression Trees. Wadsworth Inc, WadsworthzbMATHGoogle Scholar
  13. Çelik YH, Kılıçkap E, Güney M (2016) Investigation of cutting parameters affecting on tool wear and surface roughness in dry turning of Ti–6Al–4V using CVD and PVD coated tools. J Braz Soc Mech Sci Eng 39(6):2085–2093CrossRefGoogle Scholar
  14. Chen W (2000) Cutting forces and surface finish when machining medium hardness steel using CBN tools. Int J Mach Tools Manuf 40(3):455–466CrossRefGoogle Scholar
  15. Cheung CF, Lee WB (2000) Modelling and simulation of surface topography in ultra-precision diamond turning. Proc Inst Mech Eng Part B J Eng Manuf 214(6):463–480CrossRefGoogle Scholar
  16. Davim JP (2003) Design of optimisation of cutting parameters for turning metal matrix composites based on the orthogonal arrays. J Mater Process Technol 132(1–3):340–344CrossRefGoogle Scholar
  17. Davim JP (ed) (2010) Surface integrity in machining. Springer, LondonGoogle Scholar
  18. Deng W, Chen R, He B, Liu Y (2012a) A novel two-stage hybrid swarm intelligence optimization algorithm and application. Soft Comput 16:1707–1722CrossRefGoogle Scholar
  19. Deng W, Chen R, Gao J, Song Y, Xu J (2012b) A novel parallel hybrid intelligence optimization algorithm for a function approximation problem. Comput Math with Appl 63(1):325–336MathSciNetCrossRefzbMATHGoogle Scholar
  20. Deng W, Yang X, Zou L, Wang M, Liu Y, Li Y (2013) Chemometrics and intelligent laboratory systems an improved self-adaptive differential evolution algorithm and its application. Chemom Intell Lab Syst 128:66–76CrossRefGoogle Scholar
  21. Deng W, Zhao H, Liu J, Yan X, Li Y, Yin L, Ding C (2015) An improved CACO algorithm based on adaptive method and multi-variant strategies. Soft Comput 19:701–713CrossRefGoogle Scholar
  22. Deng W, Zhao H, Zou L (2017a) A novel collaborative optimization algorithm in solving complex optimization problems. Soft Comput 21(15):4387–4398CrossRefGoogle Scholar
  23. Deng W, Zhao H, Yang X, Xiong J, Sun M, Li B (2017b) Study on an improved adaptive PSO algorithm for solving multi-objective gate assignment. Appl Soft Comput 59:288–302CrossRefGoogle Scholar
  24. Deng W, Yao R, Zhao H, Yang X, Li G (2017c) A novel intelligent diagnosis method using optimal LS-SVM with improved PSO algorithm. Soft Comput.  https://doi.org/10.1007/s00500-017-2940-9 Google Scholar
  25. Deng W, Li B, Zhao H (2017d) Study on an airport gate reassignment method. Symmetry 9(258):1–18Google Scholar
  26. Es HA, Kalender FY, Harzemcebi C (2014) Forecasting the net energy demand of turkey by artificial neural networks. J Fac Eng Arch Gazi Univ 29(3):495–504Google Scholar
  27. Farahnakian M, Razfar MR (2014) Experimental study on hybrid ultrasonic and plasma aided turning of hardened steel AISI 4140. Mater Manuf Process 29(5):550–556CrossRefGoogle Scholar
  28. Ferreira R, Řehoř J, Lauro CH, Carou D, Davim JP (2016) Analysis of the hard turning of AISI H13 steel with ceramic tools based on tool geometry: surface roughness, tool wear and their relation. J Braz Soc Mech Sci Eng 38(8):2413–2420CrossRefGoogle Scholar
  29. Gaitonde VN, Karnik S, Figueira L, Davim JP (2011) Performance comparison of conventional and wiper ceramic inserts in hard turning through artificial neural network modeling. Int J Adv Manuf Technol 52(1–4):101–114CrossRefGoogle Scholar
  30. Guo P, Ehmann KF (2013) Development of a tertiary motion generator for elliptical vibration texturing. Precis Eng 37(2):364–371CrossRefGoogle Scholar
  31. Gürgen S, Çakır, FH, Sofuoğlu, MA, Orak, S, Kuşhan, MC (2019) An experimental study of hot ultrasonic assisted machining for Ti6Al4V alloy. Measurement (Unpublished)Google Scholar
  32. Hamzaçebi C (2011) Yapay Sinir Ağları: Tahmin Amaçlı Kullanımı Matlab ve Neurosolution Uygulamalı. Ekin Publishing, BursaGoogle Scholar
  33. Jiao F, Niu Y, Liu X (2015) Effect of ultrasonic vibration on surface white layer in ultrasonic aided turning of hardened GCr15 bearing steel. Mater Res Innov 19(8):S8-938-S8-942Google Scholar
  34. Karabulut S (2015) Optimization of surface roughness and cutting force during AA7039/Al2O3 metal matrix composites milling using neural networks and Taguchi method. Measurement 66:139–149CrossRefGoogle Scholar
  35. Kim D-S, Chang I-C, Kim S-W (2002) Microscopic topographical analysis of tool vibration effects on diamond turned optical surfaces. Precis Eng 26(2):168–174CrossRefGoogle Scholar
  36. Kumar R, Chauhan S (2015) Study on surface roughness measurement for turning of Al 7075/10/SiCp and Al 7075 hybrid composites by using response surface methodology (RSM) and artificial neural networking (ANN). Measurement 65:166–180CrossRefGoogle Scholar
  37. Madić M, Radovanović M (2013) Modeling and analysis of correlations between cutting parameters and cutting force components in turning AISI 1043 steel using ANN. J Braz Soc Mech Sci Eng 35(2):111–121CrossRefGoogle Scholar
  38. Mahdavinejad RA, Khani N, Fakhrabadi MMS (2012) Optimization of milling parameters using artificial neural network and artificial immune system. J Mech Sci Technol 26(12):4097–4104CrossRefGoogle Scholar
  39. Mitrofanov AV, Babitsky VI, Silberschmidt VV (2003) Finite element simulations of ultrasonically assisted turning. Comput Mater Sci 28(3–4):645–653CrossRefGoogle Scholar
  40. Morgan ve JN, Sonquist JA (1963) Problems in the analysis of survey data, and a proposal. J Am Stat Assoc 58:415–435CrossRefzbMATHGoogle Scholar
  41. Muhammad R, Maurotto A, Roy A, Silberschmidt VV (2011) Analysis of forces in vibro-impact and hot vibro-impact turning of advanced alloys. Appl Mech Mater 70:315–320CrossRefGoogle Scholar
  42. Muhammad R, Maurotto A, Roy A, Silberschmidt VV (2012) Hot ultrasonically assisted turning of β-ti alloy. Procedia CIRP 1:336–341CrossRefGoogle Scholar
  43. Muhammad R, Roy A, Silberschmidt VV (2013) Finite element modelling of conventional and hybrid oblique turning processes of titanium alloy. Procedia CIRP 8:510–515CrossRefGoogle Scholar
  44. Muhammad R, Hussain MS, Maurotto A, Siemers C, Roy A, Silberschmidt VV (2014) Analysis of a free machining α+β titanium alloy using conventional and ultrasonically assisted turning. J Mater Process Technol 214(4):906–915CrossRefGoogle Scholar
  45. Muller KR, Smola A, Ratch G, Scholkopf B, Kohlmorgen J, Vapnik V (2000) Using support vector support machines for time series prediction. Image Processing Services Research Lab, AT&T Labs, Florham ParkGoogle Scholar
  46. Nath C, Rahman M (2008) Effect of machining parameters in ultrasonic vibration cutting. Int J Mach Tools Manuf 48(9):965–974CrossRefGoogle Scholar
  47. Nath C, Rahman M, Andrew SSK (2007) A study on ultrasonic vibration cutting of low alloy steel. J Mater Process Technol 192–193(1):159–165CrossRefGoogle Scholar
  48. Niknam SA, Khettabi R, Songmene V (2014) Machinability and machining of titanium alloys: a review. In: Davim JP (ed) machining of titanium alloys. Springer, Berlin, pp 1–30Google Scholar
  49. Özel T, Hsu TK, Zeren E (2005) Effects of cutting edge geometry, workpiece hardness, feed rate and cutting speed on surface roughness and forces in finish turning of hardened AISI H13 steel. Int J Adv Manuf Technol 25(3-4):262–269CrossRefGoogle Scholar
  50. Patil S, Joshi S, Tewari A, Joshi SS (2014) Modelling and simulation of effect of ultrasonic vibrations on machining of Ti6Al4V. Ultrasonics 54(2):694–705CrossRefGoogle Scholar
  51. Razavi H, Mirbagheri M (2016) Design and fabrication of a novel vibrational system for ultrasonic assisted oblique turning process. J Mech Sci Technol 30(2):827–835CrossRefGoogle Scholar
  52. Saglam H, Unsacar F, Yaldiz S (2006) Investigation of the effect of rake angle and approaching angle on main cutting force and tool tip temperature. Int J Mach Tool Manuf 46(2):132–141CrossRefGoogle Scholar
  53. Sahoo A, Rout A, Das D (2015) Response surface and artificial neural network prediction model and optimization for surface roughness in machining. Int J Ind Eng Comput 6(2):229–240Google Scholar
  54. Sajjady SA, Nouri Hossein Abadi H, Amini S, Nosouhi R (2016) Analytical and experimental study of topography of surface texture in ultrasonic vibration assisted turning. Mater Des 93(5):311–323CrossRefGoogle Scholar
  55. Shamoto E, Moriwaki T (1994) Study on elliptical vibration cutting. CIRP Ann Manuf Technol 43(1):35–38CrossRefGoogle Scholar
  56. Shamoto E, Suzuki N, Hino R (2008) Analysis of 3D elliptical vibration cutting with thin shear plane model. CIRP Ann Manuf Technol 57(1):57–60CrossRefGoogle Scholar
  57. Sharma VS, Dogra M, Suri NM (2008) Advances in the turning process for productivity improvement: a review. Proc Inst Mech Eng Part B J Eng Manuf 222(11):1417–1442CrossRefGoogle Scholar
  58. Silberschmidt VV, Mahdy SMA, Gouda MA, Naseer A, Maurotto A, Roy A (2014) Surface-roughness improvement in ultrasonically assisted turning. Procedia CIRP 13:49–54CrossRefGoogle Scholar
  59. Singh P, Pungotra H, Kalsi NS (2016) On the complexities in machining titanium alloys. In: Mandal DK, Syan CS (eds) CAD/CAM, robotics and factories of the future. Springer India, New Delhi, pp 499–507CrossRefGoogle Scholar
  60. Sofuoğlu MA, Çakır FH, Gürgen S, Orak S, Kuşhan MC (2018a) Experimental investigation of machining characteristics and chatter stability for Hastelloy-X with ultrasonic and hot turning. Int J Adv Manuf Technol 95(1-4):83–97CrossRefGoogle Scholar
  61. Sofuoğlu MA, Çakır FH, Gürgen S, Orak S, Kuşhan MC (2018b) Numerical investigation of hot ultrasonic assisted turning of aviation alloys. J Braz Soc Mech Sci Eng 40(122):1–12Google Scholar
  62. Vapnik VN (1995) The Nature of Statistical Learning Theory. Springer, New YorkCrossRefzbMATHGoogle Scholar
  63. Wang X, Feng CX (2002) Development of empirical models for surface roughness prediction in finish turning. Int J Adv Manuf Technol 20(5):348–356CrossRefGoogle Scholar
  64. Wu X, Kumar V (2009) CART: classification and regression trees, top ten algorithms in data mining. Chapman and Hall, LondonCrossRefGoogle Scholar
  65. Yen YC, Jain A, Altan T (2004) A finite element analysis of orthogonal machining using different tool edge geometries. J Mater Process Technol 146(1):72–81CrossRefGoogle Scholar
  66. Zhang X, Senthil Kumar A, Rahman M, Nath C, Liu K (2012) An analytical force model for orthogonal elliptical vibration cutting technique. J Manuf Process 14(3):378–387CrossRefGoogle Scholar
  67. Zhang X, Kumar AS, Rahman M, Liu K (2013) Modeling of the effect of tool edge radius on surface generation in elliptical vibration cutting. Int J Adv Manuf Technol 65(1–4):35–42CrossRefGoogle Scholar
  68. Zhang C, Ehmann K, Li Y (2015) Analysis of cutting forces in the ultrasonic elliptical vibration-assisted micro-groove turning process. Int J Adv Manuf Technol 78(1–4):139–152CrossRefGoogle Scholar
  69. Zhang C, Guo P, Ehmann KF, Li Y (2016) Effects of ultrasonic vibrations in micro-groove turning. Ultrasonics 67:30–40CrossRefGoogle Scholar
  70. Zou P, Xu Y, He Y, Chen M, Wu H (2015) Experimental investigation of ultrasonic vibration assisted turning of 304 austenitic stainless steel. Shock Vib 2015:1–19CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Mehmet Alper Sofuoğlu
    • 1
    Email author
  • Fatih Hayati Çakır
    • 2
  • Melih Cemal Kuşhan
    • 1
  • Sezan Orak
    • 1
  1. 1.Department of Mechanical EngineeringEskişehir Osmangazi UniversityEskisehirTurkey
  2. 2.Vocational SchoolEskişehir Osmangazi UniversityEskisehirTurkey

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