Modelling of Flank wear, Surface roughness and Cutting Temperature in Sustainable Hard Turning of AISI D2 Steel

Modelling of Flank wear, Surface roughness and Cutting Temperature in Sustainable Hard Turning of AISI D2 Steel

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Procedia Manufacturing 20 (2018) 406–413 Procedia Manufacturing 00 (2017) 000–000 www.elsevier.com/locate/procedia

2nd International Conference on Materials Manufacturing and Design Engineering 2nd International Conference on Materials Manufacturing and Design Engineering

Modelling of Flank wear, Surface roughness and Cutting Modelling of Flank wear, Surface roughness and Cutting Temperature in Sustainable HardConference Turning2017, of MESIC AISI D2 Manufacturing Engineering Society International 2017,Steel 28-30 June Temperature in Sustainable Hard Turning of AISI D2 Steel 2017, Vigo (Pontevedra), Spain

Ramanuj Kumar*, Ashok Kumar Sahoo, Rabin Kumar Das, Amlana Panda, Purna Chandra Ramanuj Kumar*, Ashok Kumar Sahoo,Mishra Rabin Kumar Das, Amlana Panda, Purna Chandra Costing models for capacity optimization in Industry 4.0: Trade-off Mishra

between used capacity and operational efficiency

School of Mechanical Engineering, Kalinga Institute of Industrial Technology, Deemed to be University, Bhubaneswar-751024, Odisha, India School of Mechanical Engineering, Kalinga Institute of Industrial Technology, Deemed to be University, Bhubaneswar-751024, Odisha, India

A. Santanaa, P. Afonsoa,*, A. Zaninb, R. Wernkeb a

University of Minho, 4800-058 Guimarães, Portugal

Abstract b Unochapecó, 89809-000 Chapecó, SC, Brazil Abstract Productivity and quality of products are major concern for industries aspects. However present paper focused on the investigation Productivity and qualityroughness of products concern for industries aspects. However paper turning focused of onheat-treated the investigation of flank wear, average of are themajor surface and chip-tool interface temperature inpresent the machine AISI of flank roughness of multi-layer the surface coated and chip-tool temperature in the machine of heat-treated AISI D2 gradewear, tool average steel using indexable carbideinterface inserts. Abrasion, diffusion, chippingturning and catastrophic breakage Abstract D2 major grade tool failure steel using indexable multi-layer coatedsurface carbidemethodology inserts. Abrasion, chipping catastrophic breakage are mechanisms involved. Response (RSM)diffusion, based models and and Artificial-Neural-Network are major tool failure mechanisms involved. Response surface methodology (RSM) basedtomodels and Artificial-Neural-Network (ANN) models are implemented for forecasting the responses in hard-turning. Comparative assessment between actual and Under the concept of "Industry 4.0", production processes will be pushed be increasingly interconnected, (ANN) models are for forecasting the responses in hard-turning. Comparative assessment between actual and predicted results hasimplemented been carried. ANN model flank wear much generated more accurate compare to RSM Model whereas information based on a real time basis and,for necessarily, more efficient. Inresults this context, capacity optimization predicted results has been carried. ANN model for flank wear generated more accurate results compare to RSM Model whereas for surface finish and chip-tool interface temperature, the accuracy of RSM based prediction is more precise compared to ANN. goes beyond the traditional aim of capacity maximization, contributing also for organization’s profitability and value. for surface finish and chip-tool interface temperature, the accuracy of RSM based prediction is more precise compared to ANN. Indeed, lean management and continuous improvement approaches suggest capacity optimization instead of © 2017 The Authors. Published by Elsevier B.V. maximization. The Published study of capacity optimization © 2018 The by by Elsevier B.V.B.V. and costing models is an important research topic that deserves © 2017 TheAuthors. Authors. Published Peer-review under responsibility ofElsevier the scientific committee of the 2nd International Conference on Materials Peer-review under responsibility of the scientific committee of the 2nd International Conference on Materials Manufacturing and contributions from both the practical and theoretical perspectives. paper presents and discusses a mathematical Peer-review under of the scientific committee of the This 2nd International Conference on Materials Manufacturing andresponsibility Design Engineering. Design Engineering. model for capacity management based on different costing models (ABC and TDABC). A generic model has been

Manufacturing and Design Engineering. developed andD2 it was to analyze capacity and to design strategies towards the maximization of organization’s Keywords: AISI steel; used Hard turning; RSM;idle ANN value. The trade-off capacity maximization vs operational efficiency is highlighted and it is shown that capacity Keywords: AISI D2 steel; Hard turning; RSM; ANN optimization might hide operational inefficiency. © The Authors. Published by Elsevier B.V. 1.2017Introduction Peer-review under responsibility of the scientific committee of the Manufacturing Engineering Society International Conference 1. Introduction 2017.Currently, hard turning technology process makes a mark over traditional grinding operations for remarkable Currently,which hard turning process industry makes a for mark over traditional grindingelucidation. operations for remarkable improvement deservestechnology in manufacturing suitable newer technology Machining of Keywords: Cost Models; ABC; TDABC; Capacity Management; Idle Capacity; Operational Efficiency improvement which in manufacturing industry suitable newer technology elucidation. of heat treated steel is deserves of immense importance for presentfor day industrialized and scientific research.Machining Notably, for heat treatedmachinability steel is of immense forsome present daylike industrialized and scientific research.affect Notably, for successful machiningimportance performance issues heat generation, friction adversely the tool successful machinability issues like frictionfor adversely affect the tool life, tool wear and surfacemachining roughnessperformance that needs tosome be addressed. Onheat this generation, account, demands higher surface quality 1. Introduction life, tool wear and surface roughness that needs to be addressed. On this account, demands for higher surface quality * Corresponding author. Tel.: 06746540805. E-mail address: [email protected] * The Corresponding author. Tel.: 06746540805. cost of idle capacity is a fundamental information for companies and their management of extreme importance E-mail address: [email protected] in modern production systems. In general, it is defined as unused capacity or production potential and can be measured 2351-9789 © 2017 The Authors. Published by Elsevier B.V. in several ways: tons of production, available hours of manufacturing, etc. The management of the idle capacity Peer-review underThe responsibility of theby scientific of the 2nd International Conference on Materials Manufacturing and 2351-9789 © 2017 Authors. Published Elsevier committee B.V. * Paulo Afonso. Tel.: +351 253 510 761; fax: +351 253 604 741 Design Engineering. Peer-review under responsibility of the scientific committee of the 2nd International Conference on Materials Manufacturing and E-mail address: [email protected] Design Engineering. 2351-9789 © 2017 The Authors. Published by Elsevier B.V. Peer-review under of the scientificbycommittee the Manufacturing Engineering Society International Conference 2017. 2351-9789 © 2018responsibility The Authors. Published Elsevier of B.V. Peer-review under responsibility of the scientific committee of the 2nd International Conference on Materials Manufacturing and Design Engineering. 10.1016/j.promfg.2018.02.059

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components based on hardened steel are increasing with the rapidly changing with the manufacturing industry. The hardened steel has more utilization in the gears used in automotive, bearing tool and die production. The most noteworthy aspects regarding wear at the flank surface are cutting speed and the depth of cut and regarding surface finish cutting speed followed by federate respectively. The reduction in heat generation and of tool long life can be enhanced by used of cutting fluid. To get more accuracy and machined surface quality, hard turning gives better results to conventional grinding process. Tool life span of a cutting tool depends on composition of tool material, cutting environment and geometry of cutting tool. Tool wear considerably effects on dimensional, form and surface roughness errors. [1-2]. Finish hard turning process is apt concerned with machining components having hardness more than 45 HRC value. This technology gives better surface roughness, dimensional and shape tolerance. The technological benefits of hard turning include high flexibility, capability to machine composite geometries by means of a single set up. Hardened steel has several applications on the field of automotive gear, ball bearing, transmission shafts, tool and die industry [3]. Mia and Dhar [4] established the analytical models of average machining temperature between tool-workpiece relating to feed, cutting-speed, and material hardness during turning hardened AISI 1060 steels utilizing coated carbide inserts in dry environment and coolant jet with high pressure adopting RSM and ANN techniques. However, both these developed models are adequate enough for prediction capability, though the ANN model established an elevated accuracy as minimum prediction error. Panda et al. [5] developed the model and parametric optimisation of the surface finish in course of finish hard-turning process of heat treated AISI 4340 steel by means of PVD-TiN coated ceramic insert. It was concluded that. RSM, DOE and PSO were used for improvement of cutting process and economic feasibility. It was found that RSM pooled along PSO method leads to minimum roughness on the turned surface. PSO method performed good amalgamation of process parameters assisting in turning since proportion of error is less as compared to RSM. Sankar and Rao [6] used Taguchi L27 orthogonal array and analysis to scrutinize the effect of cutting restrictions. From the conclusion it was found that the influencing parameters are in the order of nose radius of insert, depth of cut, speed and feed. Keblouti et al. [7] developed mathematical model on the basis of RSM. ANOVA approach was applied to enumerate the effects of parameter related to turning on the surface roughness and MRR. Results establish that feed incorporates prominent effect on the surface roughness. Johanssona et al. [8] used Taylor tool life model and coding model for different combinations of work piece materials and tool grades during turning with cemented carbide insert. This investigation also addresses the models capability to replicate data required for cutting in finishing process. Zhang and Guo [9] proposed tool wear model on the basis of cutting force and energy consumption during turning process. The energy consumption in turning can be estimated on the basis on the cutting force prediction. Özel et al. [10] implemented linear regression and ANN models to predict the quality of finish on surface of parent work piece and wear at the flank surface in finish machine turning of D2 grade tool steel by means of ceramic wiper cutting insert. It was concluded that the accuracy of ANN model was more favourable compared to regression model. Sahoo and Sahoo [11] implemented RSM model to generate the fitted value for responses and suggested to implement the RSM technique for hard turning applications. Nouioua et al. [12] introduced RSM and ANN methods to find out optimal prediction of uncontrollable parameters. The ANN method gives more precise results and suggested for usefulness in relating to correlation coefficients, Mean prediction errors and root mean square errors correlate towards those acquired by RSM method. Form the literature studied, inadequate amount of work reported on implementation of both RSM and ANN techniques to modelled flank wear, surface roughness and chip-tool interface techniques in high speed turning of heat treated D2 steel using indexable multi-layer coated inserts. However there is worth needed to compare the ability of both techniques for modelling the hard turning responses. 2.

Machining details and procedure

AISI D2 steel bar with hardness 55 ± 1 HRC has been chosen for turning process due to its wide utilization in press tool making industries, mould and die making industries, knife industries, automotive industries etc. For this experimentation work, the workpiece diameter and machining length have been fixed as 48 mm and 200 mm respectively. CVD applied coated carbide insert (TiN/TiCN/Al2O3/TiN) of ISO designated CNMG120408 was utilized due to its greatly wear resistant capability. The rhombus shaped indexable insert is clamped rigidly in ISO designated PCLNR2525M12 tool holder. The experimentation was carried on medium duty HMT made NH22 lathe

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having 40 to 2040 rpm spindle speed capacity shown in Fig. 1a. The measurement of flank wear (VBc) is carried on tool tip with help of Olympus STM6 optical microscope (Fig. 1b) with 50x magnification. The surface quality is measured offline in term of average surface roughness (Ra) with help of Taylor Hobson (Surtronic 25) roughness instrument (Fig. 1c). Chip-insert interface temperature (T) is taken during machining with use of infra-red Fluke Ti32 thermal imager. Continuous measurement of temperature has been carried and maximum value has been taken as a response data. After experimentation, each response value is gathered and empirical relations between response and inputs are developed using RSM (response surface methodology). ANN modelling is also carried and the experimental results have been compared with predicted results obtained through RSM and ANN. Minitab16 software has been used for modelling (RSM), model ANOVA, normality plot and surface plot whereas matlab2013 is used for ANN modelling.

a

Turning set-up

Optical Microscope

b

Infra-red camera

c

Surface roughness tester

Machined D2 steel rod

Fig. 1. (a) Turning set-up with thermal camera (b) Flank wear width measurement (c) Surface roughness measurement

3.

Results morphology

Present study emphasis on surface finish, flank wear and chip-insert boundary temperature analysis in finish machine turning of heat treated AISI D2 steel. Abrasion, diffusion, chipping in addition to catastrophic breakage is major tool failure mechanisms involved. At lower speed ranges abrasion is more predominant whereas at higher speed diffusion are more dominant which is cause of tool tip breakage or catastrophic failure of tool as shown in Fig. 2a. Cutting speed is observed as only affecting process parameter as far as flank wear is concerned while feed and depth of cut do not indicate any significance. Similar trends have been reported in previous work [10, 11].

a

b

Catastrophic failure of tool nose

V = 182 m/min, f = 0.12 mm/rev d = 0.2 mm

Fig. 2. (a) SEM micrograph of tool nose after machining (b) Infra-red temperature image

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Surface roughness is noticed to be below the standard limit of 1.6 µm which is as good as to cylindrical grinding operation and also comparable with previous researchers [1, 2, 11]. Feed rate succeeded by depth of cut are the most influencing controllable term for Ra whereas speed is not significant. From the results, it is clearly noticed that the coated tools are unable to withstand at elevated temperature at higher speed (Fig. 2b) values however rapid tool wear noticed in terms of chipping and premature tip breakage as displayed in Fig. 2a. Only speed is noticed to be significant variables for temperature, whereas feed and depth are not produced any considerable influence. 4.

Multi response modelling

4.1 Response surface methodology Response surface modelling is world wise popular techniques to predict the responses in hard turning problems as reported by various researchers [7, 11]. Regression models for responses are developed with the help of response surface methodology (uncoded unit) at 95% confidence level taking into consideration orthogonal array intend data set [11, 12]. For adequacy of models, determination coefficients (R2) were enumerated and approval was established on large to very large coefficients of correlation (R2) which indicates better fitting of the model. Further, if the pvalue in ANOVA table is lower than 0.05, then the predicted model, its factors, the interaction of those factors and curvature are said to be noteworthy. VBc = 0.21597 + 0.24401d + 3.12045f-0.00822v ˗ 5.32500d2˗ 1.71875f 0.00362fv

R2 = 98.15%

2

+ 0.00003v2 + 1.40428df + 0.02053dv˗

R2 (adj) = 95.37%

(1)

Ra = 0.7228 ˗ 0.3034d ˗ 1.2200f ˗ 0.0017v + 3.6812d + 23.5547f + 0.0000v + 2.4918df ˗ 0.0096dv + 0.0096fv 2

R2 = 99.99%

2

2

R2 (adj) = 99.98%

(2)

T = 123.68 + 198.60d + 919.80 f + 0.97v - 290.62d ˗ 5113.28f + 0.00 v + 1461.33df ˗1.94dv ˗ 2.83fv 2

R2 = 99.60%

a

2

R2 (adj) = 99.00 %

(3)

b

Normal Probability Plot (response is VBc)

Versus Order (response is VBc) 0.050

95 90 80 70 60 50 40 30 20 10 5

0.025 Residual

Percent

99

1 -0.10

2

0.000 -0.025 -0.050 -0.075

-0.05

0.00 Residual

0.05

0.10

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 Observation Order

Fig. 3. (a) Normal probability graph for VBc (b) Residual vs number of observations graph for VBc

The developed models (Eqs.1-3) through quadratic regression for VBc, Ra and T have higher value of determination coefficients (R2 = 0.09815; 0.9999; and 0.9960 respectively) showing significance of model as it approaches to one. The R2 and adj R2 values are also very close to each other. Normal Probability graph (Fig. 3a) for

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flank wear ensures the fairly close distribution to a straight line which revealed that the residuals have dispersed in normality, confirming that the terms related with the models are significant. It also ensures perfect correlations between experimental and predicted values. From residual vs observations graph (Fig. 3b) the distribution pattern of residuals are arbitrary and don’t follow any cyclic pattern with number of observations. Flank wear seems to be improve with rise of all cutting factors but largest impact due to speed-feed (Fig. 4a) and speed-depth of cut ( Fig. 4c) have been noticed from 3D surface plots (Fig. 4) . Model ANOVA (Table 1) for VBc indicates statistically significance because of P-value is lesser in amount than 0.05 at a confidence level of 95%. Similarly, surface finish and temperature models are also significant. a

b

c

f

e

d

Fig. 4. (a-f) 3D surface plot for VBc Table 1. ANOVA for VBc model Source Regression Linear Square Interaction Residual Error Total

DF 9 3 3 3 6 15

Seq SS 0.9550 0.7974 0.0980 0.0595 0.0180 0.9730

Adj SS 0.9550 0.0378 0.0982 0.0595 0.0180

Adj MS 0.1061 0.0126 0.0327 0.0198 0.0030

F 35.36 4.2 10.91 6.61

P 0.000 0.064 0.008 0.025

Remarks Significant Insignificant Significant Significant

4.2 Artificial neural network modelling An artificial neural network (ANN) applies mapping method that has facilitated to update the network output. This approach is based on data processing modelling to capture and present input-output relationship. ANN is prospective and apt to learn multi-variable relationship between machining parameters and study their effects on responses. Therefore, it’s an influential modelling tool designed for complex relationship. It has impended to scrutinize complicated relationship linking various process parameters which influence the particular output. The model consists of a multi-layer feed forward, trained by a back propagation algorithmic. The applications of ANNs include forecasting, vision system, pattern recognition etc. [13]. ANN composed of neurons or nodes. Neural computation requires simple processors i.e. a number of neurons to be interconnected collectively into a simulated network structure. Neurons are being placed in layers. Every neuron inside the system generally a uncomplicated processing component that incorporates one or more inputs and generates an output. Input layer receives data and

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output data sends the final information. The input and output layers are out in the open to the environment and hidden layers don’t have any contact with the environment. The neuron merely adds together all the inputs and calculates an output [12, 13].

Fig. 5. 3-7-1 Architecture for neural network

Data Fit Y = T

1.4 1.3 1.2 1.1 1 0.9 0.8 0.7 0.8

1

Validation: R=1 Output ~=1*Target +-0.0012

Output ~=1*Target +-0.03

Training: R=0.99686 1.5

1.2

1.5

Data Fit Y = T

1.4 1.3 1.2 1.1 1 0.9 0.8 0.7

1.4

0.8

Target

Output ~=1*Target +-0.025

Output ~=1*Target +-0.012

1.3 1.2 1.1 1 0.9 0.8 0.7 0.8

1

1.2

Target

1.4

All: R=0.99716

Data Fit Y = T

1.4

1.2

Target

Test: R=1 1.5

1

1.4

1.5

Data Fit Y = T

1.4 1.3 1.2 1.1 1 0.9 0.8 0.7 0.8

1

1.2

1.4

Target

Fig. 6. Interrelationship between actual and predicted values for training, validation and test readings.

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In the present study, MATLAB R2013a neural network toolbox was applied to build artificial neural network. A ‘3-7-1’ (3 input neurons, 7 hidden neurons and 1 output neurons) design structure has been fixed for all responses and the architecture for neural network is displayed in Fig. 5. While performing the modelling for each response, tolerance in error and number of epoch are adopted as 0.001 and 2500000 respectively. Interrelationship between actual and predicted values for training, validation and test readings are shown in Fig. 6. The R2 value for models VBc, Ra and T are noticed to be 0.9943, 0.9955 and 0.9781 respectively. However R2 for all response models are near to unity which indicates the successful prediction of dataset under considered neural parameters. After training the data has been simulated and compared with input data and this process was repeated for fixed number of cycles and similar procedure has been reported in earlier work [14-15]. However, from both modelling techniques, the predicted values for each response are gathered and percentage of error between actual and predicted data has been calculated using Eq. 4.

(4)

Error (%) = [(Practical value – Fitted value) / Practical value] × 100

The Error between practical and forecasted data has been computed for all run and the mean error between practical and forecasted values for both modelling techniques has been listed in Table 2. Table 2. Average error between practical and fitted results Modelling

Mean % error between practical and fitted results

techniques

VBC

Ra

T

RSM

13.297

0.178

0.629

ANN

1.756

1.296

1.17

From the error analysis (Table 2), the Average % error for response VBc is less in ANN however, ANN based flank wear model fitted better than RSM model i.e ANN is more favourable compare to RSM for predicting the tool flank wear. Similarly the average % error for response Ra and T is less in RSM however RSM techniques is more suitable to predict the responses Ra and T compare to ANN. 5.

Conclusions

Present work emphasis on analysis of flank wear, surface roughness and chip-tool interface temperature in finish machine turning of heat-treated AISI D2 grade steel (55±1 HRC) using coated carbide insert. The subsequent conclusions are drawn: • Abrasion and diffusion are major mechanisms responsible for flank wear and cutting speed is the most sensitive term for flank wear. At higher speed ranges catastrophic breakage of tool nose produced. A good quality of surface finish noticed (Ra< 1.6 µm) and feed succeeded by depth of cut are most influencing term is noticed. Temperature (chip-tool interface) rises with speed and it is highly influenced by speed in hard machining • RSM based empirical relations have been made for responses flank wear, surface roughness and chip-tool interface temperature. R2 values for all three responses are very close to unity which ensures a good agreement with actual results. ANN based modelling has been carried and developed correlations shows a strong agreement with actual results. • RSM as well as ANN based prediction results are compared with actual results. For response flank wear, the prediction carried by ANN is more favourable compared to RSM whereas for surface roughness and chip- tool temperature RSM based predictions are more accurate over ANN.

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Acknowledgements Current research is supported by All India Council for Technical Education, New Delhi, India under research promotion scheme (RPS) project vide Ref No: 8-154/RIFD/RPS/POLICY-4/2013-14. The authors express their gratitude and thank AICTE for granting financial support to carry out the research work. References [1] P. J. Liew, A. Shaaronia, N. A.Chesidiki, An overview of current status of cutting fluids and cooling techniques of turning hard steel, International Journal of Heat and Mass Transfer, 114 (2017) 380–394. [2] J. P.Davim, Machining of hard Materials; Springer, London, (2011) 1–32. [3] G. Poulachon, B. P. Bandyopadhya, I .S.Jawahir, S .Pheulpin, E .Seguin, Wear behaviour of CBN tools while turning various hardened steels, Wear, 256 (2004) 302–310. [4] M. Mia, N. R. Dhar, Response surface and neutral network based predictive models of cutting temperature in hard turning, Journal of Advanced Research, 7 (2016) 1035–1044. [5] A. Panda, S. R. Das, D. Dhupal, Surface roughness analysis for economical feasibility study of coated ceramic tool in hard turning operation process, Integr Optim Sustain, https://doi.org/10.1007/s41660-017-0019-9. [6] B. R. Sankar, P. U. Rao, Analysis of forces during hard turning of aisi 52100 steel using taguchi method, 5th International Conference of Materials Processing and Characterization (ICMPC 2016). [7] O. Keblouti, L. Boulanouar, M. W. Azizi, M .A. Yallese, Modeling and multi-objective optimization of surface roughness and productivity in dry turning of AISI 52100 steel using (TiCN-TiN) coating cermet tools, International Journal of Industrial Engineering Computations, 8(1) (2017) 71–84. [8] D. Johanssona, S. Hägglundb, V. Bushlyaa, J. Ståhl, Assessment of commonly used tool life models in metal cutting, Procedia Manufacturing, 11 ( 2017 ) 602–609. [9] G. Zhang, C. Guo, Modeling flank wear progression based on cutting force and energy prediction in turning process, Procedia Manufacturing, 5 (2016) 536–545. [10] T Özel, Y. Karpat, L Figueira, J. P. Davim, Modelling of surface finish and tool flank wear in turning of AISI D2 steel with ceramic wiper inserts, Journal of materials processing technology, 189 (2007) 192–198. [11] A. K. Sahoo, B. Sahoo, Performance studies of multilayer hard surface coatings (TiN/TiCN/Al2O3/TiN) of indexable carbide inserts in hard machining: Part-II (RSM, grey relational and techno economical approach), Measurement, 46 (2013) 2368–2884. [12] M. Nouioua, M. A.Yallese, R. Khettabi, S. Belhadi, M. Lamine, B. F. Girardin, Investigation of the performance of the MQL, dry, and wet turning by response surface methodology (RSM) and artificial neural network (ANN), International Journal of Advance Manufacturing Technology, 93 (2017) 2485–2504. [13] R. J. Babu, A.R.Babu, Correlation among the cutting parameters, surface roughness and cutting forces in turning process by experimental studies, 5th International & 26th All India Manufacturing Technology, Design and Research Conference (AIMTDR 2014) December 12th–14th, 2014, IIT Guwahati, Assam, India [14] B. A. Beatrice, E. Kirubakaran, P. R. J. Thangaiah, K. L. D. Wins, Surface roughness prediction using artificial neural network in hard turning of AISI H13 steel with minimal cutting fluid application, Procedia Engineering, 97 ( 2014 ) 205–211. [15] N. Senthilkumar, T. Tamizharasan, Flank wear and surface roughness prediction in hard turning via artificial neural network and multiple regressions, Australian Journal of Mechanical Engineering, 13(1) (2015) 31–45.