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.
Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.References
Acayaba GMA, Escalona PM (2015) Prediction of surface roughness in low speed turning of AISI316 austenitic stainless steel. CIRP J Manuf Sci Technol 11:62–67
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):431–436
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):1–14
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 390:633–648
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):244–252
Babitsky V, Kalashnikov A, Meadows A, Wijesundara AAH (2003) Ultrasonically assisted turning of aviation materials. J Mater Process Technol 132(1–3):157–167
Babitsky V, Mitrofanov A, Silberschmidt V (2004) Ultrasonically assisted turning of aviation materials: simulations and experimental study. Ultrasonics 42(1–9):81–86
Bai W, Sun R, Leopold J (2016) Numerical modelling of microstructure evolution in Ti6Al4V alloy by ultrasonic assisted cutting. Procedia CIRP 46:428–431
Bartarya G, Choudhur SK (2012) Effect of cutting parameters on cutting force and surface roughness during finish hard turning AISI52100 grade steel. Procedia CIRP 1:651–656
Benga GC, Abrao AM (2003) Turning of hardened 100Cr6 bearing steel with ceramic and PCBN cutting tools. J Mater Process Technol 143:237–241
Brehl DE, Dow TA (2008) Review of vibration-assisted machining. Precis Eng 32(3):153–172
Breiman L, Friedman JH, Olshen RA, Stone CJ (1984) Classification and Regression Trees. Wadsworth Inc, Wadsworth
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):2085–2093
Chen W (2000) Cutting forces and surface finish when machining medium hardness steel using CBN tools. Int J Mach Tools Manuf 40(3):455–466
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):463–480
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):340–344
Davim JP (ed) (2010) Surface integrity in machining. Springer, London
Deng W, Chen R, He B, Liu Y (2012a) A novel two-stage hybrid swarm intelligence optimization algorithm and application. Soft Comput 16:1707–1722
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):325–336
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 128:66–76
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 19:701–713
Deng W, Zhao H, Zou L (2017a) A novel collaborative optimization algorithm in solving complex optimization problems. Soft Comput 21(15):4387–4398
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 59:288–302
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
Deng W, Li B, Zhao H (2017d) Study on an airport gate reassignment method. Symmetry 9(258):1–18
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):495–504
Farahnakian M, Razfar MR (2014) Experimental study on hybrid ultrasonic and plasma aided turning of hardened steel AISI 4140. Mater Manuf Process 29(5):550–556
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):2413–2420
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):101–114
Guo P, Ehmann KF (2013) Development of a tertiary motion generator for elliptical vibration texturing. Precis Eng 37(2):364–371
Gürgen S, Çakır, FH, Sofuoğlu, MA, Orak, S, Kuşhan, MC (2019) An experimental study of hot ultrasonic assisted machining for Ti6Al4V alloy. Measurement (Unpublished)
Hamzaçebi C (2011) Yapay Sinir Ağları: Tahmin Amaçlı Kullanımı Matlab ve Neurosolution Uygulamalı. Ekin Publishing, Bursa
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-S8-942
Karabulut S (2015) Optimization of surface roughness and cutting force during AA7039/Al2O3 metal matrix composites milling using neural networks and Taguchi method. Measurement 66:139–149
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):168–174
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 65:166–180
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):111–121
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):4097–4104
Mitrofanov AV, Babitsky VI, Silberschmidt VV (2003) Finite element simulations of ultrasonically assisted turning. Comput Mater Sci 28(3–4):645–653
Morgan ve JN, Sonquist JA (1963) Problems in the analysis of survey data, and a proposal. J Am Stat Assoc 58:415–435
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 70:315–320
Muhammad R, Maurotto A, Roy A, Silberschmidt VV (2012) Hot ultrasonically assisted turning of β-ti alloy. Procedia CIRP 1:336–341
Muhammad R, Roy A, Silberschmidt VV (2013) Finite element modelling of conventional and hybrid oblique turning processes of titanium alloy. Procedia CIRP 8:510–515
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):906–915
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 Park
Nath C, Rahman M (2008) Effect of machining parameters in ultrasonic vibration cutting. Int J Mach Tools Manuf 48(9):965–974
Nath C, Rahman M, Andrew SSK (2007) A study on ultrasonic vibration cutting of low alloy steel. J Mater Process Technol 192–193(1):159–165
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 1–30
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):262–269
Patil S, Joshi S, Tewari A, Joshi SS (2014) Modelling and simulation of effect of ultrasonic vibrations on machining of Ti6Al4V. Ultrasonics 54(2):694–705
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):827–835
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):132–141
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):229–240
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):311–323
Shamoto E, Moriwaki T (1994) Study on elliptical vibration cutting. CIRP Ann Manuf Technol 43(1):35–38
Shamoto E, Suzuki N, Hino R (2008) Analysis of 3D elliptical vibration cutting with thin shear plane model. CIRP Ann Manuf Technol 57(1):57–60
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):1417–1442
Silberschmidt VV, Mahdy SMA, Gouda MA, Naseer A, Maurotto A, Roy A (2014) Surface-roughness improvement in ultrasonically assisted turning. Procedia CIRP 13:49–54
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 499–507
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):83–97
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):1–12
Vapnik VN (1995) The Nature of Statistical Learning Theory. Springer, New York
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
Wu X, Kumar V (2009) CART: classification and regression trees, top ten algorithms in data mining. Chapman and Hall, London
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):72–81
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):378–387
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):35–42
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):139–152
Zhang C, Guo P, Ehmann KF, Li Y (2016) Effects of ultrasonic vibrations in micro-groove turning. Ultrasonics 67:30–40
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 2015:1–19
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
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.
Additional information
Communicated by V. Loia.
Rights and permissions
About this article
Cite this article
Sofuoğlu, M.A., Çakır, F.H., Kuşhan, M.C. et al. Optimization of different non-traditional turning processes using soft computing methods. Soft Comput 23, 5213–5231 (2019). https://doi.org/10.1007/s00500-018-3471-8
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00500-018-3471-8