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A Comprehensive GRNN Model for the Prediction of Cutting Force, Surface Roughness and Tool Wear During Turning of CP-Ti Grade 2

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Abstract

Titanium alloys are most widely used in aerospace, military, marine, medical and bio-medical industries because of their unique properties such as high corrosion resistance, biocompatibility, superior strength-to-weight ratio and ability to withstand at elevated temperatures. In spite of the above mentioned capabilities these alloys are classified as “Difficult-to-machine” type materials. This is due to lower thermal conductivity and high chemical affinity of these materials. Poor thermal conductivity and increased chemical reactivity during titanium machining causes rapid tool wear. This might be contributed to the high heat generated at primary deformation zone. This situation necessitates proper selection of the machining parameter as well as cutting tool material before machining the workpiece. The present paper suggests a novel approximation tool for predicting some of the vital machinability aspects during the turning of commercially pure titanium grade 2. Experiments were conducted at five different cutting speeds (30, 60, 90, 120 and 150 m/min) with a constant feed rate of 0.12 mm/rev and depth of cut of 0.5 mm. The variations attained in the performance measures viz. Surface roughness (Ra), cutting force (Fc) and flank wear (VBc) were graphically analyzed. A generalized regression neural network (GRNN) model was developed for the prediction of aforesaid output responses. The neural network was trained, validated and tested with the experimentally measured data sets. The grid search method was employed to find the optimal sigma – value (kernel bandwidth) with minimum cross-validation error. The obtained results were validated and tested in order to demonstrate the adequacy of the model. The results indicated that the GRNN model is successfully capable of approximating turning quality characteristics within ± 5% error.

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Abbreviations

R a :

Surface roughness

F x :

Feed force

F y :

Thrust force

F z :

Tangential cutting force

F c :

Resultant cutting force

VB c :

Average flank wear

v :

Cutting speed

f :

Feed rate

d :

Depth of cut

GRNN:

General regression neural network

CP-Ti:

Commercially pure titanium

MRR:

Material removal rate

ANN:

Artificial neural network

BPNN:

Back-propagation neural network

FL:

Fuzzy logic

RSM:

Response surface methodology

RBF:

Radial basis function

MVRA:

Multi-variable regression analysis

CNC:

Computer numerical control

CBN:

Cubic boron nitride

SEM:

Scanning electron microscope

BE:

Built-up edge

References

  1. Budinski KG (1991) Tribological properties of titanium alloys. Wear 151(2):203–217

    Article  CAS  Google Scholar 

  2. Palraj S, Venkatachari G (2008) Effect of biofouling on corrosion behaviour of grade 2 titanium in Mandapam seawaters. Desalination 230(1):92–99

    Article  CAS  Google Scholar 

  3. Aziz-Kerrzo M et al (2001) Electrochemical studies on the stability and corrosion resistance of titanium-based implant materials. Biomaterials 22(12):1531–1539

    Article  CAS  PubMed  Google Scholar 

  4. Lautenschlager EP, Monaghan P (1993) Titanium and titanium alloys as dental materials. Int Dent J 43 (3):245–253

    CAS  PubMed  Google Scholar 

  5. Zitter H, Plenk H (1987) The electrochemical behavior of metallic implant materials as an indicator of their biocompatibility. J Biomed Mater Res 21(7):881–896

    Article  CAS  PubMed  Google Scholar 

  6. Nalbant M, Gökkaya H, Sur G (2007) Application of Taguchi method in the optimization of cutting parameters for surface roughness in turning. Mater Des 28(4):1379–1385

    Article  CAS  Google Scholar 

  7. Chandrasekaran M et al (2010) Application of soft computing techniques in machining performance prediction and optimization: a literature review. Int J Adv Manuf Technol 46 (5-8):445–464

    Article  Google Scholar 

  8. Abburi N, Dixit U (2006) A knowledge-based system for the prediction of surface roughness in turning process. Robot Comput Integr Manuf 22(4):363–372

    Article  Google Scholar 

  9. Zain AM, Haron H, Sharif S (2009) Artificial neural network for predicting machining performance of uncoated carbide (WC-co) in milling machining operation. In: International conference on computer technology and development, 2009. ICCTD’09. IEEE

  10. 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

    Article  Google Scholar 

  11. 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

    Article  Google Scholar 

  12. Chandrasekaran M, Devarasiddappa D (2014) Artificial neural network modeling for surface roughness prediction in cylindrical grinding of Al-SiCp metal matrix composites and ANOVA analysis. 9(2):59–70. https://doi.org/10.14743/apem2014.2.176

  13. Kumar S et al (2014) A hybrid Taguchi-artificial neural network approach to predict surface roughness during electric discharge machining of titanium alloys. J Mech Sci Technol 28(7):2831–2844

    Article  Google Scholar 

  14. Basheer AC et al (2008) Modeling of surface roughness in precision machining of metal matrix composites using ANN. J Mater Process Technol 197(1):439–444

    Article  CAS  Google Scholar 

  15. Risbood K, Dixit U, Sahasrabudhe A (2003) Prediction of surface roughness and dimensional deviation by measuring cutting forces and vibrations in turning process. J Mater Process Technol 132(1):203–214

    Article  CAS  Google Scholar 

  16. Routara B et al (2012) Response surface methodology and genetic algorithm used to optimize the cutting condition for surface roughness parameters in CNC turning. Procedia Engineering 38:1893–1904

    Article  CAS  Google Scholar 

  17. Sonar D, Dixit U, Ojha D (2006) The application of a radial basis function neural network for predicting the surface roughness in a turning process. Int J Adv Manuf Technol 27(7-8):661–666

    Article  Google Scholar 

  18. Tsao C, Hocheng H (2008) Evaluation of thrust force and surface roughness in drilling composite material using Taguchi analysis and neural network. J Mater Process Technol 203 (1):342– 348

    Article  CAS  Google Scholar 

  19. Panda BN, Bahubalendruni MR, Biswal BB (2014) Comparative evaluation of optimization algorithms at training of genetic programming for tensile strength prediction of FDM processed part. Procedia Materials Science 5:2250–2257

    Article  CAS  Google Scholar 

  20. Pradhan MK, Biswas CK (2010) Neuro-fuzzy and neural network-based prediction of various responses in electrical discharge machining of AISI d2 steel. Int J Adv Manuf Technol 50(5-8):591–610

    Article  Google Scholar 

  21. Wang X, Feng C (2002) Development of empirical models for surface roughness prediction in finish turning. Int J Adv Manuf Technol 20(5):348–356

    Article  CAS  Google Scholar 

  22. Choudhury I, El-Baradie M (1997) Surface roughness prediction in the turning of high-strength steel by factorial design of experiments. J Mater Process Technol 67(1):55–61

    Article  Google Scholar 

  23. Kopač J, Bahor M (1999) Interaction of the technological history of a workpiece material and the machining parameters on the desired quality of the surface roughness of a product. J Mater Process Technol 92:381–387

    Article  Google Scholar 

  24. Montgomery DC (2008) Design and analysis of experiments. Wiley, New York

    Google Scholar 

  25. Myers RH, Montgomery DC, Anderson-Cook CM (2016) Response surface methodology: process and product optimization using designed experiments. Wiley, New York

    Google Scholar 

  26. Özel T, Nadgir A (2002) Prediction of flank wear by using back propagation neural network modeling when cutting hardened h-13 steel with chamfered and honed CBN tools. Int J Mach Tools Manuf 42(2):287–297

    Article  Google Scholar 

  27. Sanjay C, Neema M, Chin C (2005) Modeling of tool wear in drilling by statistical analysis and artificial neural network. J Mater Process Technol 170(3):494–500

    Article  CAS  Google Scholar 

  28. Singh A et al (2006) Predicting drill wear using an artificial neural network. Int J Adv Manuf Technol 28 (5-6):456–462

    Article  Google Scholar 

  29. Panda BN, Vendan SA, Garg A (2017) Experimental-and numerical-based studies for magnetically impelled arc butt welding of t11 chromium alloy tubes. Int J Adv Manuf Technol 88(9-12):3499–3506

    Article  Google Scholar 

  30. Garg A et al (2016) Study of effect of nanofluid concentration on response characteristics of machining process for cleaner production. J Clean Prod 135:476–489

    Article  CAS  Google Scholar 

  31. Panda BN, Bahubalendruni MR, Biswal BB (2015) A general regression neural network approach for the evaluation of compressive strength of FDM prototypes. Neural Comput & Applic 26(5):1129–1136

    Article  Google Scholar 

  32. Specht DF, Shapiro PD (1990) Training speed comparison of probabilistic neural networks with back-propagation networks. In: Proceedings of the international neural network conference

  33. Janakiraman VM, Nguyen X, Assanis D (2013) Nonlinear identification of a gasoline HCCI engine using neural networks coupled with principal component analysis. Appl Soft Comput 13(5):2375–2389

    Article  Google Scholar 

  34. Nawi NM, Atomi WH, Rehman M (2013) The effect of data pre-processing on optimized training of artificial neural networks. Procedia Technology 11:32–39

    Article  Google Scholar 

  35. Khidhir BA, Mohamed B (2010) Study of cutting speed on surface roughness and chip formation when machining nickel-based alloy. J Mech Sci Technol 24(5):1053–1059

    Article  Google Scholar 

  36. Gill SS et al (2011) Flank wear and machining performance of cryogenically treated tungsten carbide inserts. Mater Manuf Process 26(11):1430–1441

    Article  CAS  Google Scholar 

  37. Hou J et al (2014) Influence of cutting speed on cutting force, flank temperature, and tool wear in end milling of Ti-6Al-4V alloy. Int J Adv Manuf Technol 70(9-12):1835–1845

    Article  Google Scholar 

  38. Jianxin D, Yousheng L, Wenlong S (2008) Diffusion wear in dry cutting of Ti–6Al–4V with WC/co carbide tools. Wear 265(11):1776–1783

    Article  CAS  Google Scholar 

  39. Che-Haron C (2001) Tool life and surface integrity in turning titanium alloy. J Mater Process Technol 118 (1):231–237

    Article  CAS  Google Scholar 

  40. Ginting A, Nouari M (2009) Surface integrity of dry machined titanium alloys. Int J Mach Tools Manuf 49(3):325–332

    Article  Google Scholar 

  41. Shi Q et al (2013) Experimental study in high speed milling of titanium alloy TC21. Int J Adv Manuf Technol 64(1-4):49–54

    Article  Google Scholar 

  42. Hartung PD, Kramer B, Von Turkovich B (1982) Tool wear in titanium machining. CIRP Ann Manuf Technol 31(1):75–80

    Article  CAS  Google Scholar 

  43. Amin AN, Ismail AF, Khairusshima MN (2007) Effectiveness of uncoated WC–co and PCD inserts in end milling of titanium alloy—Ti–6Al–4V. J Mater Process Technol 192:147–158

    Article  CAS  Google Scholar 

  44. Jawaid A, Che-Haron C, Abdullah A (1999) Tool wear characteristics in turning of titanium alloy Ti-6246. J Mater Process Technol 92:329–334

    Article  Google Scholar 

  45. Pervaiz S, Deiab I, Darras B (2013) Power consumption and tool wear assessment when machining titanium alloys. Int J Precis Eng Manuf 14(6):925–936

    Article  Google Scholar 

  46. Bendu H, Deepak B, Murugan S (2016) Application of GRNN for the prediction of performance and exhaust emissions in HCCI engine using ethanol. Energy Convers Manag 122:165–173

    Article  CAS  Google Scholar 

  47. Specht DF (1991) A general regression neural network. IEEE Trans Neural Netw 2(6):568–576

    Article  CAS  PubMed  Google Scholar 

  48. Davim JP, Gaitonde V, Karnik S (2008) Investigations into the effect of cutting conditions on surface roughness in turning of free machining steel by ANN models. J Mater Process Technol 205(1):16–23

    Article  CAS  Google Scholar 

  49. Lu C (2008) Study on prediction of surface quality in machining process. J Mater Process Technol 205 (1):439–450

    Article  Google Scholar 

  50. Nalbant M et al (2009) The experimental investigation of the effects of uncoated, PVD-and CVD-coated cemented carbide inserts and cutting parameters on surface roughness in CNC turning and its prediction using artificial neural networks. Robot Comput Integr Manuf 25(1):211–223

    Article  Google Scholar 

  51. Karayel D (2009) Prediction and control of surface roughness in CNC lathe using artificial neural network. J Mater Process Technol 209(7):3125–3137

    Article  Google Scholar 

  52. Al-Ahmari A (2007) Predictive machinability models for a selected hard material in turning operations. J Mater Process Technol 190(1):305–311

    Article  CAS  Google Scholar 

  53. Cus F, Zuperl U (2006) Approach to optimization of cutting conditions by using artificial neural networks. J Mater Process Technol 173(3):281–290

    Article  CAS  Google Scholar 

  54. Zuperl U et al (2004) A hybrid analytical-neural network approach to the determination of optimal cutting conditions. J Mater Process Technol 157:82–90

    Article  Google Scholar 

  55. Zuperl U, Cus F (2003) Optimization of cutting conditions during cutting by using neural networks. Robot Comput Integr Manuf 19(1):189–199

    Article  Google Scholar 

  56. Kohli A, Dixit U (2005) A neural-network-based methodology for the prediction of surface roughness in a turning process. Int J Adv Manuf Technol 25(1-2):118–129

    Article  Google Scholar 

  57. Ezugwu E et al (2005) Modelling the correlation between cutting and process parameters in high-speed machining of Inconel 718 alloy using an artificial neural network. Int J Mach Tools Manuf 45(12):1375–1385

    Article  Google Scholar 

  58. Grzesik W, Brol S (2003) Hybrid approach to surface roughness evaluation in multistage machining processes. J Mater Process Technol 134(2):265–272

    Article  Google Scholar 

  59. Mia M, Dhar NR (2016) Prediction of surface roughness in hard turning under high pressure coolant using Artificial Neural Network. Measurement 92:464–474

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Mechanical Engineering, National Institute of Technology, Rourkela, India

    Akhtar Khan & Kalipada Maity

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  1. Akhtar Khan
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  2. Kalipada Maity
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Correspondence to Kalipada Maity.

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Khan, A., Maity, K. A Comprehensive GRNN Model for the Prediction of Cutting Force, Surface Roughness and Tool Wear During Turning of CP-Ti Grade 2. Silicon 10, 2181–2191 (2018). https://doi.org/10.1007/s12633-017-9749-0

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  • DOI: https://doi.org/10.1007/s12633-017-9749-0

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