process modeling for machining inconel 825 using

Metal Powder Report d Volume xxx, Number xx d xxxx 2020 metal-powder.net

Process modeling for machining Inconel

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825 using cryogenically treated carbide
insert SP
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Sumit Kumar a, P. Sudhakar Rao a, Deepam Goyal b,⇑, Shankar Sehgal c

a Department of Mechanical Engineering, National Institute of Technical Tea
chers Training and Research, Chandigarh, Indiab Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab, IndiacMechanical Engineering, UIET, Panjab University, Chandigarh, India
The ubiquity of artificial intelligence in the manufacturing domain draws inspiration for the presentarticle. The implementation of a neural network technique is still a difficult and time-consuming effortfor the industry. Prediction of machining variables is a considerable issue that needs to be explored forpreventive maintenance of the machine structure and to optimize the surface quality. This work aimsat predicting response parameters of the dry turning process for Inconel 825 alloy using deep-cryogenictreated tungsten-carbide insert through artificial neural network technique. Process parametersconsidered in this work were cutting speed, feed and depth of cut, whereas, surface-roughness, tool-wear, and material-removal-rate were taken as the three response parameters.14 types of trainingfunctions were compared based upon their error indices searching for the training function which bestsuits this work. Artificial Neural Network (ANN) model was developed by taking Bayesian regulariza-tion back propagation based training function. The response values predicted by the ANN were in veryclose approximation to the actual experimental value with the mean square error of only 0.0011 lm2,39.0882 lm2 and 0.0520 cm6/min2in the prediction of surface-roughness, tool-wear, and material-removal-rate of dry turning process of Inconel 825 using treated carbide tool.

1. IntroductionMicro-structural modifications happen due to the cryogenictreatment of carbide tools lead to decreased tool wear andincreased productivity. Both tool wear and productivity play animportant role in deciding the economics of the machining pro-cess. An inexpensive cutting tool with high tool wear may resultin an uneconomical machining process because of the interrup-tions caused by the downtime of machines while changing thetool. To boost the lifetime of cutting tools, heat treat tool mate-rials have been used as a popular strategy in the past. Althoughcryogenic treatment has been in use for cutting tools it is reallyin its inception stage when compared with heat treatment. How-ever, the cryoprocessing is not a replacement for better heattreatment, as is often mistaken; somewhat, it is an add-onmethod to traditional heat treatment to be performed prior to

⇑ Corresponding author.

E-mail address: Goyal, D. ([email protected])

0026-0657/� 2020 Elsevier Ltd. All rights reserved. https://doi.org/10.1016/j.mprp.2020.06.001

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tempering. Though, some enhancement can be achieved by per-forming the treatment on the finished inserts at the end of thenormal heat treatment cycle.

The cryogenic treatment has been recognized as an efficienttechnique to increase the durability of carbide tools by enhanc-ing different properties such as wear resistance, strength hard-ness, and dimensional stability. Da Silva et al. [1] compared theperformance of cryogenically treated and conventionally heat-treated high speed steel (HSS) tool during machining and slidingabrasion test. No significant increase in hardness was observed.Further, the transformation of 25% of retained austenite inuntreated samples to 0% of retained austenite in cryogenicallytreated steel samples was confirmed by the X-ray diffractometer,which suggests the conversion of retained austenite into marten-site, responsible for better performance of cryo-treated tools. Sig-nificant amelioration in the life of cryo-treated tools up to 44%was observed during machining. A higher rate of tool wear was

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mailto:bkdeep�am�goyal@out�look.�com

https://doi.org/10.1016/j.mprp.2020.06.001


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observed for cryo-treated tools after machining an equal numberof work pieces. Khan and Maity [2] observed growth in concen-trations of carbon & cobalt suggesting the development of g-phase carbides along with the densification of cobalt binder.FESEM images also indicated the appearance of g-phase carbidesover the surface of the cryo-treated tool insert. The chips wereobserved to be more serrated in cryo- treated tools. Significantimprovement in wear resistance and micro hardness along witha decrease in chip compression ratio and friction coefficientwas observed in cryo-treated tools. Chidambaram and Palani[3] modelled and analyzed tool wear of cryogenically treatedinserts using response surface methodology. The cutting insertswere soaked at �196 �C for 48 hrs followed by tempering at200 �C for 2 hrs. The formation of g-phase carbides was con-firmed by the SEM and XRD analysis. The experimental and pre-dicted values of flank wear and crater were compared and themaximum errors recorded were 10.0664% and 21.7005% forflank wear and crater wear respectively.

Akincio�glu et al. [4] reviewed various works related to cryo-genic treatment reported in the literature. It was observed thatsoaking temperature was the most influential parameter inincreasing the wear resistance. An increase in thermal conductiv-ity as a consequence of cryogenic treatment was also reported. Itwas also observed that the cryo-treatment enhanced the flankwear and chipping of the tool, whereas cutting forces wereobserved to be decreased. Cryo-treatment enables the materialto be machined at higher temperatures.

Arapo�glu et al. [5] suggested a variable selection methodmodel based on artificial neural networks. Forward and stepwiseselection methods were proposed. A statistical hypothesis testwas used as an elimination criterion. It was observed that theaccuracy of regression variable elimination methods reduces toproduce accurate results when the number of process variablesincreases and so an attempt was made to predict the surfaceroughness considering thirteen independent variables. The cut-ting depth was given the maximum weightage followed by tooloverhang length and so forth the weight values were assignedto the process variables by the network. Karayel [6] developedan ANN for prediction of surface roughness in CNC lathe. ScaledConjugate gradient algorithm was utilized for training the net-work. 3-5-1 network structure was developed for modelling Raand a 3-10-1 network structure was developed for measuring Rz

and Rmax. The predicted values were in close approximation withthe actual values. Chen and Chen [7] proposed and evaluated anANN-based in-process tool wear system. The process variableswere feed rate, depth of cut and the average cutting force inthe y-direction. 100 experimental runs were conducted beforeANN training and the final developed model was evaluatedagainst 9 experimental runs. RMSE was used as the performancevalidation function for the structure developed. Restriction of 2hidden layers and 10 neurons per hidden layer was used. Anaverage error of ±0.037 mm was observed in the tool wear pre-dicted values. Zain et al. [8] developed an ANNmodel for the pre-diction of surface roughness in the machining process.Feedforward backpropagation is selected as the algorithm withtraingdx, learngdx, MSE, logsig as the training, learning, perfor-mance and transfer functions, respectively. Eight different net-

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works were developed and tested for the best performance. Itwas observed that the prediction accuracy can be improved bymodifying the number of hidden layers and the no. of neuronsof the hidden layers. Gupta [9] modelled surface roughness, cut-ting power and tool wear for turning process using RSM, ANNand Support vector regression (SVR). It was observed that the pre-diction capability of ANN and SVR was much better in compar-ison to the RSM and regression analysis. The performance ofANN and SVR is at par with each other. Karkalos et al. [10] com-pared the performance of Multi-layer Perceptron (MLP), theRadial Basis Function Neural Network (RBF-NN), and the Adap-tive Neuro-Fuzzy Inference System (ANFIS) with the regressionmodel. The response parameters considered for the present studywere thrust force and cutting torque during drilling ofAL6082-T6 work piece. It was observed that the Multi-layerPerceptron model outperformed other neural network techniquesand the regression technique. The process parameters consideredwere cutting speed, feed rate and tool diameters. The meansquare error and mean absolute percentage error were consideredas the performance function for choosing the number of hiddenneurons. Manohar et al. [11] modelled cutting forces and surfaceroughness using artificial neural network. Close approximationwas observed between experimental and observed values. Leven-berg–Marquardt algorithm was used for training the model. 67%of data set was used for training the dataset and rest 33% datasetwas used for validation of the model. ANOVA test was used forchecking the adequacy of the model. Mia et al. [12] modelled cut-ting force, surface roughness; feed force during turning of Ti-6Al-4V alloy using RSM and ANN. The adequacies of the model weredetermined by the higher values of regression coefficient andlower values of error. For each response parameter, separate neu-ral networks were developed. The mean absolute percentage errorfor surface error was almost 5–6 times the errors obtained for cut-ting force and feed force owing to the unpredicted contributionof various factors in surface roughness. Senthilkumar andTamizharasan [13] modelled flank wear and surface roughnessusing ANN and multiple regression models. It was observed thatfeed forward-back propagation ANN outperforms the regressionmodels. The process parameters included were cutting speed,fed rate, depth of cut, material hardness cutting inert shape, reliefangle and nose radius. The numbers of hidden neurons wereselected as twice the number of input neurons and one extraneuron.

The present paper attempts to put forth the prediction capa-bility of ANN for the dry turning of Inconel 825 using deep-cryogenic treatment based tungsten carbide insert. This insertis also observed to possess better surface morphology and hard-ness properties than the traditionally used untreated insert.ANN model developed during this work is seen to predict theresponse parameters of dry-turning process very accurately withvery low error indices and hence can be used in further controlof the advanced process. In the past researches, ANNs were devel-oped to predict response parameters but most of them were con-fined to predict single response parameter generally surfaceroughness. The present developed network serves as an attemptto predict multiple response parameters for corresponding inputprocess parameters.

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FIGURE 1

Experimental setup.

TABLE 1

Composition of Inconel 825 Alloy (Spectrum 1).

Line Type Weight %

Cr K series 21.77Fe K series 31.31Ni K series 37.78Ti K series 01.23O K series 03.54Mo L series 04.37

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2. Materials and methodsAs shown in Figure 1, experiments were conducted on Okumamake standard conventional lathe machine to dry-turn theInconel 825 specimens with the help of deep-cryogenic-treatedtungsten carbide inserts. Each specimen weighed approximately1.2 kg with a diameter of 39 mm and length 115 mm. In order toensure the authenticity of the material of the specimens, theirchemical compositions were cross-checked with the help of Elec-tron Discharge Spectroscopy setup. Figure 2 shows the spectrumobtained for one cylindrical specimen through EDS. The corre-sponding detailed composition of a specimen is provided inTable 1. During experiments, Tungsten Carbide TNMG 160404-THM cutting inserts were used having a grade, geometry,included angle, cutting edge length, corner radius, thickness,material, and inner circle diameter respectively as THM, triangu-lar, 60�, 16.50 mm, 0.4 mm, 4.76 mm, and 3.91 mm.

FIGURE 2

EDS Spectrum of Inconel 825 specimen.


Microstructural changes were observed after the deep-cryogenic treatment of the cutting inserts.

Surface morphology of the treated insert is shown in Figure 3.Three different regions were developed over the surface, darkgrey, black, and light grey. The dark grey region (Spectrum 4and 5) was in contiguity throughout the matrix whereas the lightgrey (Spectrum 2) and black region (Spectrum 3) were present in

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TABLE 4

EDS report of the dark grey (a phase) region (Spectrum 4).

Element Line Type Weight %

W M-series 61.85C K-series 35.39Ti K-series 01.44Co K-series 01.32

FIGURE 3

Surface morphology of treated insert.

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interrupted zones. To determine their elemental compositiontheir EDS was done. The EDS results are summarized in Tables2–4.

From the EDS reports mentioned above, it is evident that inthe black region the amount of carbon precipitation is muchhigher. Gill et al. [14] studied the metallurgical and mechanicalcharacteristics of untreated, shallow cryo-treated and deep cryo-treated tungsten carbide-cobalt specimens. The decrease in theconcentration and size of the cobalt phase binder was observed.Shallow cryo –treated WC-Co specimen was observed to beharder than the deep cryo-treated specimen. The refinement ofthe a-phase particles and their crystallographic alignment as aresult of spheroidization resulted in refined and stress-free con-figuration. Further, the increase in hardness can be attributedto the simultaneous decrease in soft cobalt phase binder. Resis-tance to wear was observed to be higher in the case of deep

TABLE 2

EDS report of the light grey region (Spectrum 2).


W M-series 78.51C K-series 18.11O K-series 02.36Co K-series 01.02

TABLE 3

EDS report of the black (g phase) region (Spectrum 3).


C K-series 57.34W M-series 38.16O K-series 03.71Co K-series 00.79

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cryo-treated inserts as compared to shallow cryo-treated inserts.Increased thermal conductivity helps in improving the heat dis-sipation effects and hence an ameliorated tool life is achieved.Deshpande and Venugopal [15] cryogenically treated uncoatedtungsten carbide inserts followed by comparing their perfor-mance after machining along with their characterization forevaluating the microstructural changes that occurred owing tothe treatment. It was observed that the segregation of carbideparticles was better in treated samples along with an increasedcolony of g-phase carbides. The tool life was observed to beincreased for the insert which was cryo-treated and tempered at300�C followed by furnace cooling. Kalsi et al. [16] studied theeffects of tempering on WC inserts. Four lots of samples werecryo-treated and all lots were subjected to a different numberof tempering cycles ranging one to four; naming them as CT1–CT4, based upon the number of tempering cycles they were sub-jected. Cobalt binder was observed to be shrunken after treat-ment (CT1, CT2, CT3). The microstructure was observed to bebecoming identical with untreated inserts after the third temper-ing cycle. Wear resistance of the inserts was observed to behigher after the 2nd and 3rd tempering cycle. Sivalingam et al.[17] observed a reduction in flank wear up to 29% and 15% for48 hrs and 24 hrs cryogenically soaked inserts. The direct relationof micro hardness with a soaking period in cryogenic treatmentwas observed in the present study. The decrease in cutting forceand vibration acceleration signal was observed in the cryo-

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FIGURE 4

Surface morphology of (a) untreated insert; (b) treated insert.

TABLE 5

Hardness of cutting inserts prior to and after deep-cryogenic treatment.

S.No Type of Insert Micro Hardness (HV1)

Run 1 Run 2 Run 3 Average

1 Untreated WC Insert 2372 2388 2348 2369.332 Deep Cryogenically

Treated WC Insert2453 2484 2575 2504

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treated insert. In the present study, it is observed that a decreasein soft phase cobalt binder along with the simultaneous occur-rence of g phase is attributed to the increase in hardness andthermal conductivity of tungsten carbide cutting insert. Table 4represents the elemental composition of the a-phase of tungstencarbide insert.

Spectrum 4 and spectrum 5 share a similar elementalcomposition and hence it can be summarized that the a phaseis distributed throughout the matrix of tungsten carbide. Thea phase is also known as the ceramic skeleton matrix. Theg phase carbides are complex carbides (Co3W3C and CoW6C6).Cryogenic treatment relieves cobalt from its internal stressesdeveloped during the sintering process. On further highermagnifications, it was observed that the average grain size ofcryogenically treated carbide inserts decreases thereby resultingin a stress-free configuration. Figure 4 shows a variation of grainsizes before and after the deep-cryogenic treatment at the samemagnification level (x2500).

As it clear from Figure 4(a) and (b) that the average grain sizeof particles in the deepcryogenic-treated insert is lesser than itsuntreated counterpart. Such grain-refinement results in stress-free configuration of the insert. The presence of g phase andrefinement of the a phase result in a 5.68% increase in averagehardness of the treated insert relative to an untreated insert asshown in Table 5. Each hardness test was conducted by applyinga load for 10 seconds.

ANNs were generated in the MATLAB wherein several trainingfunctions are available to be used for training the neural net-works. Each training function has some mathematical rulesand logic to train a neural network. The mathematics lyingbehind these training functions involves methods of adjustingweights and bias to predict the response and hence train the net-work. Before proceeding towards the selection of any trainingfunction one must check the performance of all these training


functions as their results vary according to the complexity andlinearity behaviour of the input data sets.

A random selection of training functions would be againstthe ethics of research since each training function will gener-ate a neural network, but which one is best cannot be stated.Hence in this work 14 training functions, as summarized inTable 6 were compared for finding out the best possible train-ing function that best suits the present research problem andgives minimum mean square error between the input and pre-dicted values. For this purpose, neural networks were devel-oped for each training function keeping 10, 20 and 30neurons in the hidden layer. Sharma and Venugopalan [18]compared the neural training function for minimum meansquare error. It was observed that trainbfg, trainlm and train-scg were providing minimum errors between actual and pre-dicted values. For comparison to remaining neutral, basicelements of neural network architecture were required to bekept fixed and so, max_epochs, shows, performance goal,time, min_gradient and max_fail were kept constant at1000, 5, 0, infinite, 0.0000001 and 6 respectively. All trainingfunctions were compared keeping 10, 20 and 30 neurons inthe hidden layer. The performance report of each trainingfunction, at 10, 20 and 30 neurons in the hidden layer, isshown in Table 7.

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TABLE 6

Various training functions compared during this work.

Training Function Number Algorithm Training Function Basis

1 Gradient-Descent Gradient descent back-propagation2 -do- Gradient descent with momentum back propagation3 -do- Gradient descent with adaptive learning rate backpropagation4 -do- Resilient backpropagation5 -do- Variable learning rate back propagation6 Conjugate-Gradient Scaled conjugate gradient7 -do- Fletcher-Powell conjugate gradient8 -do- Polak-Ribiére conjugate gradient9 -do- Conjugate gradient with Powell/Beale restarts10 Quasi-Newton BFGS quasi-Newton11 -do- One step secant12 -do- Levenberg-Marquardt13 -do- Bayesian regularization back propagation14 Random Random order incremental training with learning functions

TABLE 7

Performance report of different training functions.

Training function number Hidden neurons Iterations Best validation performance(Minimum Mean Square Error) at corresponding iteration number

1 10 30 1466.7887 at iteration number 3020 7 1313.772 at iteration number 130 43 765.5299 at iteration number 37






7 10 16 1457 at iteration number 1020 7 779.4747 at iteration number 130 6 3073.1223 at iteration number 0








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FIGURE 5

Performance Validation for Bayesian Regularization Back propagation Based Training Function Number 13.

FIGURE 6

3-10-20-3Architect of the final neural network developed.

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As per Table 7, training function number 13, which was basedon the Bayesian regularization back propagation method,attained minimum square error for the present data; hence itwas selected as the training function for further work relatedto ANN development. Figure 5(a–c) shows the validationperformance of Bayesian regularization back propagation basedtraining function at 10, 20 and 30 neurons in a single hiddenlayer.

2.1. Neural network developmentLarge numbers of neural networks were developed and testedfor better prediction of response parameters. The effectivenessof each model was indicated by their regression plots and bestvalidation performance. The approach was to achieve highvalues of correlation coefficient, approaching unity. It wasobserved during the training of neural networks that predic-tion of more than one response parameter against a smalldataset of 27 values was a tedious task for the network. A


common observation was that the network was gettingtrained very well but upon the testing stage, the results werea bit on the far side. Finally, a network comprising of twohidden layers was developed having structure 3-10-20-3, asshown in Figure 6. The values of the regression coefficientfor training, testing and overall were 1, 0.98529 and0.99768 respectively.

3. Results and discussionTable 8 shows the actual values of response parameters and theircorresponding predicted values by ANN.

It is evident that the values of tool wear are magnitudinallylarger than the corresponding values of surface roughness andmaterial removal rate and so is the case with its error so it seemslarge. The average % deviation in case of tool wear is 1.609935whereas in case of surface roughness it is 1.36033. So we cansay that the range of error is quite well within limits. Further,the network was trained on 27 datasets only to check its com-patibility with limited dataset; better predictions can beobtained if we train the network with large dataset. Figure 7(a–c) represents the comparison between the experimentaland predicted values.

It is evident from the graphs (Figure 7(a–c)) that the neuralnetwork predicted values as obtained for Surface roughness, Toolwear and Material removal rate are in close confirmations to theexperimental values. However, slight variation has been alsoobserved at dataset 7 and 19, which confirms that the data havenot been learnt by the developed network. Further it is alsoobserved that the neural network is trained well to handle thevariability aspect of the experimental dataset. The network wastrained on 27 datasets only to check its compatibility with lim-ited dataset, better predictions can be obtained if we train thenetwork with large dataset. However, we cannot predict optimalsettings using ANN. We can predict response parameters corre-sponding to input parameters. Further, the range of processparameters needs to lie between their extreme values for whichthe network is trained.

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TABLE 8

Comparison of experimental versus ANN predicted response parameters.

S.No.

Process Parameters Response Parameters

Cutting speed (m/min)

Feedrate(mm/rev)

Depth of cut(mm)

SR (lm) TW (lm) MRR (cm3/min)

Experimental ANNprediction

Squareerror

Experimental ANNprediction Squareerror

Experimental ANNprediction

Squareerror

1 62.4944 0.09 0.6 0.7900 0.7942 0.0000 121.8960 121.8957 0.0000 2.7900 2.7900 0.00002 62.4944 0.09 0.4 0.8100 0.8048 0.0000 110.1650 110.1651 0.0000 0.5265 0.5306 0.00003 62.4944 0.08 0.6 0.7666 0.7717 0.0000 128.7115 128.7119 0.0000 1.2000 1.1997 0.00004 62.4944 0.09 0.2 0.8033 0.8051 0.0000 106.2714 106.2712 0.0000 2.1060 2.1056 0.00005 62.4944 0.07 0.4 0.7266 0.7287 0.0000 89.9705 89.9706 0.0000 1.0020 1.0027 0.00006 89.4527 0.08 0.2 0.5333 0.5288 0.0000 85.9400 85.9400 0.0000 0.5781 0.5829 0.00007 35.536 0.09 0.2 1.1766 1.0424 0.0180 62.5100 49.6523 165.3204 1.7869 1.7850 0.00008 89.4527 0.07 0.4 0.4133 0.4075 0.0000 124.7090 124.7091 0.0000 1.3163 1.3164 0.00009 35.536 0.09 0.4 1.2666 1.2679 0.0000 66.3170 66.3170 0.0000 0.2919 0.2915 0.000010 35.536 0.08 0.2 1.0060 1.0072 0.0000 48.4060 48.4492 0.0019 0.1260 0.1263 0.000011 89.4527 0.08 0.6 0.6110 0.6084 0.0000 123.0785 123.0790 0.0000 4.0468 4.0466 0.000012 62.4944 0.08 0.4 0.7366 0.7399 0.0000 110.4800 110.4801 0.0000 0.4400 0.4372 0.000013 89.4527 0.09 0.4 0.6360 0.5625 0.0054 140.6600 143.5538 8.3741 1.9920 1.8940 0.009614 35.536 0.07 0.4 0.8966 0.8935 0.0000 60.0660 60.0660 0.0000 0.6142 0.6126 0.000015 89.4527 0.09 0.2 0.6300 0.6368 0.0000 120.4711 120.4710 0.0000 2.5798 2.5789 0.000016 62.4944 0.08 0.2 0.7366 0.7330 0.0000 99.0921 99.0922 0.0000 1.1517 1.1504 0.000017 35.536 0.08 0.6 1.4230 1.4206 0.0000 68.8148 68.8148 0.0000 0.1270 0.1654 0.001518 89.4527 0.08 0.4 0.5766 0.5194 0.0033 107.1940 101.7092 30.0830 2.1490 2.1564 0.000119 35.536 0.08 0.4 0.8733 0.8646 0.0001 82.1500 52.9678 851.6008 1.3546 0.1753 1.390720 62.4944 0.07 0.6 0.7300 0.7255 0.0000 91.6919 91.6920 0.0000 1.9390 1.9388 0.000021 89.4527 0.07 0.2 0.3133 0.3433 0.0009 132.6780 132.6780 0.0000 2.0234 2.0238 0.000022 62.4944 0.07 0.2 0.7166 0.7133 0.0000 50.2808 50.2821 0.0000 1.2116 1.2105 0.000023 89.4527 0.07 0.6 0.4600 0.4597 0.0000 230.5515 230.5045 0.0022 4.8190 4.7887 0.000924 89.4527 0.09 0.6 0.6466 0.6470 0.0000 149.0269 149.0271 0.0000 4.2120 4.2122 0.000025 35.536 0.07 0.6 0.8533 0.8598 0.0000 87.6541 87.6541 0.0000 0.5038 0.5033 0.000026 35.536 0.09 0.6 1.7000 1.6491 0.0026 83.6205 83.6204 0.0000 0.7087 0.7071 0.000027 35.536 0.07 0.2 0.8100 0.8127 0.0000 89.9040 89.9040 0.0000 0.1130 0.1437 0.0009Errors Mean Square Error

(MSE)0.0011 Mean Square Error

(MSE)39.0882 Mean Square Error(MSE) 0.0520

Root Mean Square error(RMSE)

0.0335 Root Mean Square error(RMSE)

6.2521 Root Mean Square error(RMSE)

0.2280

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FIGURE 7

Comparison of response parameters using ANN.

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4. ConclusionsIn the present study, the response parameters were predictedusing an artificial neural network for the dry turning of Inconel825 alloy using deep-cryogenically-treated-tungstencarbideinsert. Process parameters considered during this work werecutting-speed, feed and depth-of-cut, whereas, surface rough-ness, tool wear, and material removal rate were taken as the threeresponse parameters. The major findings of the present work areas follows:

� Study of surface morphology of tungsten carbide by scanningelectron microscope followed by spectroscopy test by EDSsetup revealed the formation of g phase carbides. The cryo-genic treatment relieves cobalt from its internal stresses devel-oped during the sintering process. The average grain size ofcryogenically treated carbide inserts decreases thereby whichresulting in their stress-free configuration.

� The hardness of deep-cryogenic-treated tungsten carbideinsert is 5.68% higher than the untreated tungsten carbideinsert due to the presence of the g phase and refinement ofthe a phase.

� A comparison of ANN-based predictions with their experi-mental counterparts shows that the proposed model can accu-rately predict response parameters for the dry turning processconsidered during this work. The proposed ANN can be usedto predict the response values in advance to actual machiningand corrective measures regarding productivity can be taken,in accordance with the response obtained from the ANNmodel.

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process modeling for machining inconel 825 using

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