Surface Roughness Modeling In High Speed Turning Of Ti-6Al-4V Using Response Surface Methodology

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ScienceDirect Materials Today: Proceedings 5 (2018) 11686–11696

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ICMMM - 2017

Surface Roughness Modeling In High Speed Turning Of Ti-6Al-4V Using Response Surface Methodology Grynal D’Melloa*, Srinivasa Pai P. a

Research Scholar, Department of Mechanical Engineering, NMAM Institute of Technology, Nitte, Karkala Taluk, Udupi District - 574110 b

Professor, Department of Mechanical Engineering, NMAM Institute of Technology, Nitte, Karkala Taluk, Udupi District – 574110, Visveswaraya Technological University, Belagavi.

Abstract Usage of titanium alloys has increased day by day in the field of bio medical, aerospace, marine and automobile industries because of its corrosion-resistance, high strength to weight ratio and fatigue resistance. Titanium and its alloys are considered as difficult to machine materials. In the present work, an attempt has been made to investigate the influence of cutting speed, feed rate, tool wear and cutting tool vibrations on two widely investigated and used surface roughness parameters namely Ra andRt during high speed turning of Ti-6Al-4V. The experiments have been conducted using uncoated carbide inserts. The surface roughness modeling has been done using Response Surface Methodology (RSM). RSM models (both first and second order) have been developed considering different combination of input parameters namely all parameters, ignoring cutting tool vibration and ignoring tool wear. The reason is to understand the influence of these two parameters on the RSM models developed. Second order models are better in modeling both Ra and Rt as they exhibit higher values of correlation coefficient (R2). Both Ra and Rt increase with feed rate and cutting tool vibrations whereas increase in cutting speed and tool wear decreases the same. © 2017 Elsevier Ltd. All rights reserved. Selection and/or Peer-review under responsibility of International Conference on Materials Manufacturing and Modelling (ICMMM - 2017).

Keywords:Ti-6Al-4V; Surface Roughness; Tool Wear; Cutting tool vibration; Response Surface Methodology

1. Introduction Usage of titanium alloys has increased day by day in the field of bio medical, aerospace, marine and automobile industries because of its corrosion-resistance, high strength to weight ratio and fatigue resistance. Titanium and its alloys are considered as difficult to machine materials when compared to aluminium and steel. Ti-6Al-4V is a

* Corresponding author. Tel.: +91 9741736234. E-mail address: [email protected] 2214-7853© 2017 Elsevier Ltd. All rights reserved. Selection and/or Peer-review under responsibility of International Conference on Materials Manufacturing and Modelling (ICMMM - 2017).

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Nomenclature A B C D

Cutting speed (m/min) Feed rate (mm/rev) Tool Flank wear (mm) Cutting tool vibrations (Vy) (g)

widely used titanium alloy which contains 6%aluminium and 4% vanadium and comes under alpha-beta category [1].Ezugwu et al. reviewed the machinability of titanium alloys. They focused on machining of titanium and tool wear associated with it. Straight tungsten carbide cutting tools obtained superior results [2]. FarhadNabhani carried out the machining of aerospace titanium alloys. Experiments were carried out on an annealed TA48 titanium alloy using PCBN (Amborite) and Polycrystalline Diamond (PCD) (SYNDITE) cutting tool inserts. It was found that SYNDITE gave a better surface finish and longer tool life. Also a quick stop technique was used where in a layer was formed between the rake face and underside of the emerging chip which had effect on cutting speed and tool wear [3]. Rahman et al. reviewed the high speed machining of grade 5 alloys. It was found that advanced tool materials such as PCD and Cubic Boron Nitride (CBN) are capable of machining titanium alloys at high cutting speed [4].Arrazola et al. presented a review on titanium alloys specially Ti-6Al-4V and Ti555.3. An effort was made to study the machinability of Ti555.3 and tool wear mechanisms. Greater difficulty was encountered during machining Ti555.3 when compared to Ti-6Al-4V. It also showed a correlation between the mechanical properties of work material, tool wear and cutting forces [5]. The quality of the surface can be affected by various parameters like cutting speed, feed rate, depth of cut, tool wear, cutting tool vibrations, cutting forces, material hardness etc. Hence, the user finds it difficult to select the appropriate parameters in order to achieve good surface finish. Some scientific methods are needed to validate the process. Several researchers have attempted to use modelling in machining difficult to machine and costly materials.Bernados&Vosniakos made a detailed survey onsurface roughness prediction in machining. They focused on the various methodologies and practices that are being employed for the prediction of surface roughness. Artificial Intelligence (AI) models were found to be precisely more accurate and realistic. Various AI techniques like Artificial Neural Network (ANN), Adaptive Neuro Fuzzy Inference System (ANFIS) and other modeling techniques like RSM (which is a statistical technique) have been studied and implemented by the researchers in the recent years to model surface roughness obtained during machining[6]. Sahin&RizaMotorcu developed a surface roughness model for machining mild steel with coated carbide tools. TiN coated carbide cutting tools were used for experimentation. First and second order mathematical models were developed for surface roughness from experimental data using Response Surface Methodology (RSM). ANOVA for the second-order model showed that the interaction terms and the square terms were statistically insignificant. It was also seen that in a single order model, cutting speed and feed rate were significant, whereas depth of cut was insignificant[7]. Surface roughness varies with increase in tool wear and cutting tool vibrations can occur due to the impact on work piece, machine vibrations, chatter, tool wear and imbalance of the work piece mounting.Abouelatta&Madl developed a surface roughness prediction model based on cutting parameters and tool vibrations in turning operations. An FFT analyser was used to measure the vibration signals in radial and feed directions. Ra, Rt and Rsk surface roughness parameters were used as functions of the cutting parameters and tool vibrations. Rt which is maximum height parameter, mainly depends on cutting speed and diameter of the work piece [8]. As presented by Ghani et al., surface roughness was found constant with the variation of flank wear and for the same flank wear, vibration decreases with increase in cutting speed [9].Dimla developed correlation of vibration signal features with cutting tool wear in a metal turning operation. The results showed the significane of using vibration signals for monitoring tool wear and wear qualification [10]. Risbood et al. carried out the prediction of surface roughness and dimensional deviation by measuring cutting forces and vibrations in turning process. A neural network model was developed and was concluded that an accelerometer alone is enough for providing the acceleration of radial vibration as feedback[11]. Salgado et al. suggested in process surface roughness prediction system using cutting vibrations in turning. Results showed that surface roughness prediction accuracy based on cutting vibrations can be improved by providing information on tool

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geometry and cutting parameters[12].Upadhyay et al. investigated cutting parameters and vibration signals during the prediction of surface roughness for Ti-6Al-4V. Cutting vibration signals were captured online in axial, radial and tangential directions and multi regression models were developed. Pearson correlation coefficient was used and found that feed rate was highly significant followed by acceleration amplitude of vibration in radial direction, depth of cut and acceleration amplitude of vibration in tangential direction. Neural network model was developed by considering feed rate, depth of cut, vibration signals in radial and tangential direction to check the adequacy of developed models and predicted the surface roughness with less error[13]. Hessainia et al. studied the prediction of surface roughness in hard turning based on cutting parameters and tool vibrations. The results indicated that the feed rate was the dominant factor affecting the surface roughness and vibrations in radial and main cutting force directions had a low effect on surface roughness[14]. In machining, statistical methods have been extensively used for predicting the surface roughness and finding out the significance of cutting parameters. Hari Singh & Pradeep Kumar developed mathematical models of tool life and surface roughness for turning operation using RSM. Second order model was found to be more suitable using central composite rotatable design [15].Nikos C. Tsourveloudis investigated the predictive modeling of Ti-6Al-4V alloy surface roughness. Response Surface Methodology (RSM) and Adaptive Neuro Fuzzy Inference System (ANFIS) were the two techniques used and were compared. Results showed that ANFIS predicted surface roughness more accurately [16]. Neseli et al. carried out the optimization of tool geometry parameters for turning operations based on RSM. Results indicated that tool nose radius was a dominant factor influencing surface roughness [17]. Ramesh et al.carried out the measurement and analysis of surface roughness in turning of aerospace titanium alloy (gr5). Influence of cutting parameters on the surface roughness in turning of titanium alloy was investigated and results revealed that feed is the most dominant factor which was affected the surface roughness [18]. SatyanarayanaKosaraju&Venu Gopal Anne suggested the optimal machining conditions for turning Ti-6Al-4V using response surface methodology. Cutting forces were captured during machining and surface roughness was measured offline. It was found that the model fits for Fz and Ra were found to be 0.968 and 0.970 which concluded an effective model [19].Das et al. analyzed the surface roughness of hardened steel AISI 4140 using TiN coated ceramic inserts. A quadratic model was developed for surface roughness parameter Ra by considering the machining parameters. Ra was highly influenced by feed rate and depth of cut had negligible effect but cutting speed has a negative influence [20]. From the literature, it can be summarised that RSM is effective in predicting and optimizing surface roughness while machining various metals and alloys. In the present work, machining parameters like cutting speed and feed rate, tool wear and cutting tool vibrations (signal acquired in the speed direction (Vy)) have been considered for predicting surface roughness using RSM during high speed turning of Ti-6Al-4V. Multiple regression models have been developed for Ra (Average surface roughness) and Rt (Maximum peak to valley height). Models have been developed considering all parameters, neglecting flank wear and cutting tool vibrations. Accordingly three sets of models have been developed. Thus a total of 9 RSM models have been developed considering first and second order cases considering different sets of input parameters for both Ra and Rt. Experiments have been carried out on a CNC lathe under different cutting conditions. 140 experimental data have been considered for developing the RSM models. Response plots have been drawn considering all the parameters, keeping cutting speed fixed in order to understand the effect of the remaining parameters on Ra and Rt, which are widely used in surface roughness studies. 2. Materials and Methods Experimental setup for conducting high speed turning experiments is shown in fig 1. Work material used in this work for high speed turning is Ti-6Al-4V round bar of 50 mm diameter and 200 mm length. CNC turning centre (HMT Stallion 100SU) with a spindle speed range of 100-3500 rpm has been used to carry out the experiments under dry conditions. Cutting tool inserts used are uncoated carbide 883 with MR4 chip breaker (SECO make) and tool holder is PCLNL 2020 K12 (SECO make).

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Fig. 1 Experimental Setup

Experiments have been carried out with cutting speeds of 150, 175 and 200 m/min, feed rates of 0.15, 0.2 and 0.25 mm/rev and depth of cut of 0.8 mm which has been kept constant. Each experiment is carried out to a length of 48 mm and flank wear is measured. Experiments are repeated till the tool flank wear reached a value of 0.4 mm. The 1993 international standard (ISO 3685) stipulates a flank wear width of 0.76 mm width for rough turning and 0.38 mm for finish turning [21]. Flank wear was measured using Mitutoyo Tool Maker’s Microscope (TM 505/510) with a provision for measurement using micrometers in X and Y direction with a least count of 0.005 mm. It has a magnification of 15X. The cutting tool vibrations generated during machining trials have been measured using Model 65-10 Isotron® triaxial accelerometer (Meggitt make). The accelerometer is fixed on the tool holder near the cutting zone using a cellophane tape. Tool overhang has been set to 60 mm. The vibration signals generated during machining are sensed by the accelerometer in x, y and z directions, i. e. depth of cut, speed and feed directions respectively. The signals generated during machining are sent to a DNA-PPCx, Power DNA cube (UEI make) at a sampling frequency of 10 kHz. The recorded signals are sent to a PC/Laptop with LABVIEW based display software for storage and display. The raw vibration data is saved in a notepad file for further processing and analysis. Vibration signals along speed direction (Vy)has been found to be more sensitive when compared to other two directions. Hence this has been considered for modeling studies. Measurement of surface roughness is done offline, after machining using a stylus type instrument namely Taylor Hobson Taly Surf 50. Ra (Arithmetic average surface roughness) and Rt (Maximum peak to valley height) are the two surface roughness parameters measured using a sampling length of 2.5 mm. Surface roughness is measured at three different locations 1200 apart on the surface of the work piece and the average value has been considered for modeling. 2.1. Response Surface Methodology (RSM) RSM is a useful mathematical and statistical technique for modeling and analysis of problems in which dependent parameters isaffected by several parametersin order to minimize the dependent parameters. The correlation between the dependent and independent parameters is unknown in most of the cases. Thus the first step in RSM is to find a suitable a useful relationship between dependent parameter ‘y’ and a set of independent parameters {x1, x2, …..,xn}. If the output response is well demonstrated by a linear function of the independent variables, then the first order model which is represented as: =

+

+

+

+

+

(1)

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If there is curvature in the system, then a polynomial of higher degree must be used, such as the second order model which is given as: = +∑ + +∑ ∑ + (2) Where, represents the noise or error observed in the response y such that the expected response is (y - ) and β’s are the regression coefficients [22]. xi represents the coded variables that correspond to the studied machining parameters. 3. Materials and Methods In the present study, 9 experiments have been performed to understand the effect of cutting speed (m/min), feed rate (mm/rev), tool wear (mm) and cutting tool vibrations Vy (g) during the machining of Ti-6Al-4V on surface roughness parameters Ra and Rt. Sample experimental data is shown in Table 1. Table 1 Sample experimental data for Ra and Rt Cutting Tool Feed Vy Speed wear (g) (mm/rev) (m/min) (mm)

Factors Symbol

A

B

C

D

150 150 150 175 175 175 200 200 200

0.15 0.2 0.25 0.15 0.2 0.25 0.15 0.2 0.25

0.31 0.42 0.445 0.39 0.38 0.425 0.41 0.27 0.435

27.03 33.86 29.16 29.73 46.34 36.68 28.09 52.59 33.07

Ra (µm)

Rt (µm)

1.5017 1.4987 1.3372 0.8704 1.0824 1.5119 0.7399 0.44 0.7311

5.8423 6.008 5.5717 5.051 4.6803 6.4739 4.3104 2.5570 4.2985

Modelling efforts have been done considering all parameters and considering tool wear and cutting vibrations only along with cutting speed and feed. This has been done to understand the influence of tool wear and cutting tool vibrations on surface roughness parameters. Both first and second order models have been developed in order to find the coefficient of regression (R2). Table 2 shows the maximum and minimum levels of variables considered for modelling. The effect of the factors on the responses has been studied on the basis of statistical significance which has been determined by the F-test. The responses have been explained using both first order and second order polynomials, by considering different combinations of the input parameters as described in Table 3. Accordingly 9 models have been developed for both Ra and Rt. Table 2Maximum and minimum levels of variables Levels Factors -1 0 1 Cutting Speed (m/min) 150 175 200 Feed (mm/rev) 0.15 0.2 0.25 Table 3 List of first order and second order models designed in this study Model No

Order

Input parameters

Response

1 2 3 4 5 6 7 8 9

First First First Second Second Second Second Second Second

A, B, C, D A, B, C A, B, D A, B, C, D A, B, C A, B, D A, B, C, D A, B, C A, B, D

Ra Ra Ra Ra Ra Ra Rt Rt Rt

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The determination of coefficients of the first order and second order regression models and Analysis of variance (ANOVA) have been carried out using MINITAB 17 statistical software [23]. Based on the different models obtained, the effect of parameters and their interactions have been studied. 3.1. Development of first order polynomial models for Ra (Model 1, 2, 3) Analysis of variance (ANOVA) is commonly used to test the significance of the regression model, significance of individual model coefficients and lack of fit of the model. Model 1, 2, 3 are the first order models developed for Ra. Tables 4 – 6 shows the corresponding ANOVA results. This analysis has been performed for 95% confidence level. The p value less than 0.05 indicate that the surface roughness model is significant. The terms in the models shown in table have a significant effect on surface roughness parameter Ra. The final models have been developed by backward elimination and all the models have been found to be significant. Backward elimination is a method which removes the least significant variable. It restricts, when all variables in the model have p-values less than or equal to the specified Alpha-to-Remove value (0.05). The ANOVA results of model 1 given in table 4 shows that C has been eliminated as it has been found to be insignificant. But A, B and D are significant in influencing Ra value. Model 2 given in table 5 show that A and B has been found to be significant whereas C is eliminated in the reduced first order model. The ANOVA results for model 3 given in table 6 shows that only A, B and D has been found to be highly significant. This it is clear that from all the three models evaluated, tool wear has been found to be an insignificant factor influencing Ra. On the basis of the regression coefficients obtained from the ANOVA table, the first order models for Ra are represented by Eq. 3 – 5. The R2 values for model 1, 2 and 3 have been found to be 46%, 37.31% and 46% respectively. = 1.469 − 0.00820 A + 4.705 B + 0.003380 D (3) = 1.343 − 0.00630 A + 4.499 B (4) = 1.469 − 0.00820 A + 4.705 B + 0.003380 D (5) Source Model Linear A B D Error Total

Table 4. ANOVA table for reduced first order model of Ra - Model 1 DF Adj SS Adj MS F-Value p-Value 3 7.120 2.37339 38.62 0.000 3 7.120 2.37339 38.62 0.000 1 3.064 3.06442 49.86 0.000 1 5.368 5.36767 87.34 0.000 1 1.344 1.34448 21.88 0.000 136 8.358 0.06146 139 15.479

Source Model Linear A B Error Lack-of-Fit Pure Error Total

Table 5. ANOVA table for reduced first order model of Ra - Model 2 DF Adj SS Adj MS F-Value p-Value 2 5.7757 2.88784 40.77 0.000 2 5.7757 2.88784 40.77 0.000 1 2.0600 2.06004 29.09 0.000 1 4.9450 4.94495 69.82 0.000 137 9.7029 0.07082 105 9.1259 0.08691 4.82 0.000 32 0.5769 0.01803 139 15.4785

Source Model Linear A B D Error Total

Remarks Significant

Remarks Significant

Table 6. ANOVA table for reduced first order model of Ra - Model 3 DF Adj SS Adj MS F-Value p-Value Remarks 3 7.120 2.37339 38.62 0.000 Significant 3 7.120 2.37339 38.62 0.000 1 3.064 3.06442 49.86 0.000 1 5.368 5.36767 87.34 0.000 1 1.344 1.34448 21.88 0.000 136 8.358 0.06146 139 15.479

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3.2. Development of second order polynomial models for Ra (Model 4, 5, 6) To further understand the effect of interactions, second order polynomial models have been developed. Model 4, 5, 6 are the second order polynomial models developed for Ra considering different combination of parameters as discussed previously. Tables 7 – 9 show the ANOVA results for Ra. All the fitted models are found to be significant. The ANOVA results for model 4 given in table 7 shows that A, B, D, AC and BC are the significant model terms whereas C individually has no significance on Ra, as p value is more than 0.05. The ANOVA results for model 5 from table 8 shows that A, B, AC and BC are found to be significant but for C, p value is 0.689. This implies that tool wear is insignificant in model 5. The ANOVA results for model 6 given in table 9 shows that A, B, D and AB have p values less than 0.05 which are found to be significant parameters. The square terms have been completely eliminated as it had no significant effect on Ra. The R2 values for Ra obtained from model 4, 5 and 6 are 73.55%, 66.6% and 50.21% respectively. Source Model Linear A B C D 2-Way Interaction AC BC Error Total

Table 7. ANOVA table for reduced second order model of Ra - Model 4 DF Adj SS Adj MS F-Value p-Value 6 11.3839 1.89731 61.63 0.000 4 6.1801 1.54502 50.18 0.000 1 4.4872 4.48718 145.75 0.000 1 2.2986 2.29856 74.66 0.000 1 0.0098 0.00977 0.32 0.574 1 1.0751 1.07506 34.92 0.000 2 4.1270 2.06352 67.03 0.000 1 2.0943 2.09433 68.03 0.000 1 1.6599 1.65990 53.92 0.000 133 4.0947 0.03079 139 15.4785

Remarks Significant

The final regression models for Ra are represented in Eq. 6 – 8. = −2.394 + 0.00769 A + 10.697 B + 17.90 C + 0.003028 D − 0.07023 AC − 28.57 BC (6) = −2.609 + 0.00977 A + 10.67 B + 18.40 C − 0.07207 AC − 29.38 BC (7) = −1.224 + 0.00818 A + 18.22 B + 0.002911 D − 0.0811 AB (8) Source Model Linear A B C 2-Way Interaction AC BC Error Lack-of-Fit Pure Error Total Source Model Linear A B D 2-Way Interaction AB Error Total

Table 8. ANOVA table for reduced second order model of Ra - Model 5 DF Adj SS Adj MS F-Value p-Value 5 10.3088 2.06176 53.44 0.000 3 5.1050 1.70168 44.11 0.000 1 3.5613 3.56126 92.31 0.000 1 2.0084 2.00839 52.06 0.000 1 0.0062 0.00621 0.16 0.689 2 4.3669 2.18345 56.60 0.000 1 2.2084 2.20835 57.24 0.000 1 1.7579 1.75786 45.56 0.000 134 5.1697 0.03858 102 4.5928 0.04503 2.50 0.002 32 0.5769 0.01803 139 15.4785 Table 9. ANOVA table for reduced second order model of Ra - Model 6 DF Adj SS Adj MS F-Value p-Value 4 7.7724 1.94311 34.04 0.000 3 5.1047 1.70157 29.81 0.000 1 2.9341 2.93410 51.40 0.000 1 3.3739 3.37386 59.11 0.000 1 0.9591 0.95909 16.80 0.000 1 0.6523 0.65227 11.43 0.001 1 0.6523 0.65227 11.43 0.001 135 7.7061 0.05708 139 15.4785

Remarks Significant

Remarks Significant

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Model 4 considering all the parameters has higher R2 value compared to other models and also this value is much higher than first order model discussed in previous section. Further tool wear again shows the least significance on Ra. Thus is clear that second order models give a relationship better than first order models. Therefore, further RSM analysis will be based on the second order models. 3.3. Development of second order polynomial models for Rt (Model 7, 8,9) Three second-order polynomial models have been designed for Rt by taking into consideration the same combination of parameters discussed previously for first order models of Rt. For model 7given in table 10, the ANOVA results show that C is found to be insignificant, whereas other main factor and interaction effects of AC and BC have been highly significant as p value is less than 0.05. The ANOVA results for model 8 given in table 11 shows that A and B are highly significant followed by the interaction effects of AC and BC but C has been found to be insignificant. For model 9 from table 12, ANOVA results showed that only main factors have a significant effect, and square terms and interaction terms had no effect and hence have been eliminated resulting in a model similar to model 6. The R2 values obtained for model 7, 8 and 9 are 61.19%, 35.03% and 52.10% respectively. The regression equations obtained for the response factors using multiple regression are represented in Eq. 9 – 11. = −2.31 + 0.0027 A + 29.46 B + 36.90 C + 0.02319 D − 0.1305 AC − 63.5 BC(9) = −3.95 + 0.0186 A + 29.27 B + 40.77 C − 0.1446 AC − 69.7 BC (10) = 5.167 − 0.02478 A + 16.75 B + 0.02405 D (11) From the results obtained from RSM modeling, it can be seen that second order polynomial models have been found to be better with higher R2 value than first order polynomial models. The models developed for Ra have higher R2 value than for Rt. When compared to other results from the literature [24], it is found that R2 obtained during the modeling of surface roughness parameters (Ra and Rt) in machining Ti-6Al-4V has been found to be less in this study. RSM modeling is best suited for planned experiments with fixed levels.But since the tool wear and cutting tool vibrations have been considered as the independent variables in this study andexperimental data has been acquired based on unplanned experiments, the desired level of accuracy is not achieved[22]. 3.4. Response Surface Plots The three dimensional response surface plots are shown in fig. 2 – 4 to understand the influence of machining parameters, flank wear and cutting tool vibrations on Ra and Rt. The surface plots created were considering cutting speed as the constant parameter fixed on one axis and varying other parameters namely feed, tool wear and cutting tool vibrations in accordance with the second order model fitted. These combinations were selected due to the reason that cutting speed is found to be a dominant parameter followed by feed in influencing surface roughness parameters Source Model Linear A B C D 2-Way Interaction AC BC Error Total

Table 10. ANOVA table for reduced second order model of Rt - Model 7 DF Adj SS Adj MS F-Value p-Value 6 147.501 24.5835 34.94 0.000 4 105.256 26.3139 37.40 0.000 1 39.746 39.7461 56.49 0.000 1 35.408 35.4081 50.33 0.000 1 1.585 1.5851 2.25 0.136 1 63.055 63.0549 89.62 0.000 2 16.974 8.4869 12.06 0.000 1 7.234 7.2345 10.28 0.002 1 8.200 8.1999 11.66 0.001 133 93.572 0.7035 139 241.073

Remarks Significant

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Ra and Rt. Tool wear and cutting tool vibration are found to be insignificant at times. Fig. 2 shows the response surface plots considering cutting speed and feed on Ra and Rt, tool wear and Vyare kept at the middle level. It is seen from fig 2A that Ra decreases with increase in cutting speed and increases with increase in feed. Influence ofcutting speed and feed on Rt is shown in figure 2B which shows similar results as that of Ra. Increase in feed causes the thickness of chip to increase, thereby forming larger uncut ridge which results in poor surface finish and low Table 11. ANOVA table for reduced second order model of Rt - Model 8 Source DF Adj SS Adj MS F-Value p-Value Model 5 84.446 16.889 14.45 0.000 Linear 3 42.201 14.067 12.03 0.000 A 1 15.971 15.971 13.66 0.000 B 1 26.655 26.655 22.80 0.000 C 1 1.995 1.995 1.71 0.194 2-Way Interaction 2 20.696 10.348 8.85 0.000 AC 1 8.891 8.891 7.61 0.007 BC 1 9.898 9.898 8.47 0.004 Error 134 156.627 1.169 Lack-of-Fit 102 121.107 1.187 1.07 0.428 Pure Error 32 35.519 1.110 Total 139 241.073

Source Model Linear A B D Error Total

Remarks Significant

Table 12. ANOVA table for reduced second order model of Rt - Model 9 DF Adj SS Adj MS F-Value p-Value Remarks 3 125.61 41.8687 49.31 0.000 Significant 3 125.61 41.8687 49.31 0.000 1 27.96 27.9632 32.94 0.000 1 68.02 68.0245 80.12 0.000 1 68.05 68.0522 80.15 0.000 136 115.47 0.8490 139 241.07

Cutting speeds results in the formation of increase in height of uncut ridge which leads to poor surface finish when compared to higher speeds [19]. Fig. 3 shows response surface plots considering cutting speed and tool wear. It can be seen from fig 3A and 3B that Ra and Rt does not show much variation with increase in speed, but as tool wear is increased, Ra and Rt tend to decrease. This is probably due to the deformation of flank face at the tool nose radius [25]. Fig. 4 shows response surface plots considering cutting speed and cutting tool vibrations. Cutting speed and vibrations show a significant effect on Ra and Rt. As there is increase in cutting speed and vibrations, Ra and Rt increase accordingly. This is due to the fact that increase in cutting speed produces more vibrations which even include machine vibrations that can affect surface roughness parameters. Rt variation mainly depends on cutting speed and diameter of the work piece [14], hence Rt alone cannot be considered for surface roughness modeling. Waviness of the surface is caused by vibration in the first cut. The tool cuts into wavy surface which causes variation of thickness in the chip and force that excites the structure, which generates greater vibrations between the cutting tool and the work piece which is called ‘waviness regeneration’ [26]. 4. Conclusions In the present work, a study has been carried out to investigate the effect of machining parameters (namely cutting speed and feed), tool wear and cutting tool vibrations on two surface roughness parameters Ra and Rt. RSM has been implemented and multiple regression models have been developed considering 140 experimental data. The effect of tool wear and cutting tool vibrations on Ra and Rt has been effectively investigated which is the primary goal of this work. The following conclusions can be drawn based on this study: i.

Analysis of variance (ANOVA) shows that cutting speed, feed rate and cutting tool vibrations significantly influence surface roughness parameters Ra and Rt.

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A

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B

Fig. 2. Response surface plots of (A) Ra and (B) Rt, considering speed and feed A

B

Fig. 3. Response surface plots of (A) Ra and (B) Rt, considering speedand tool wear A

B

Fig. 4. Response surface plots of (A) Ra and (B) Rt, considering speedand cutting tool vibration ii.

iii. iv. v.

The developed multiple regression models both first and second order describes the effect of cutting parameters, cutting tool vibrations and tool wear on Ra and Rt. Second order regression models show better coefficient of regression (R2) when compared with first order regression models, thereby establishing its effectiveness in RSM modeling. Surface roughness parameters (RaandRt) increases with increase in feed rate and cutting tool vibrations while an increase in cutting speed and tool wear decreases the surface roughness. Raand Rt are the suggested parameters to completely evaluate the quality of turned surfaces of Ti-6Al-4V. Tool wear and cutting tool vibrationssignificantly affect Rtthan Ra.

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Acknowledgements The authors would like to acknowledge AICTE, New Delhi for sponsoring this research work Ref. No.: 20/AICTE/RIFD/RPS(POLICY-1)/2012-13 under Research Promotion Scheme (RPS). The authors would like to thank Ms. LouellaConceptaGoveas, Assistant Professor, Dept. of Biotechnology, NMAM Institute of Technology, Nitte for helping in the development of the RSM models. References [1]

[2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13] [14] [15] [16] [17] [18] [19] [20] [21] [22] [23] [24] [25] [26]

Muhammad, Riaz, Mohammad SajidHussain, AgostinoMaurotto, CarstenSiemers, Anish Roy, and Vadim V. Silberschmidt. "Analysis of a free machining α+ β titanium alloy using conventional and ultrasonically assisted turning".Journal of Materials Processing Technology 214.4 (2014): 906-915. Ezugwu, E. O., and Z. M. Wang. "Titanium alloys and their machinability—a review".Journal of materials processing technology 68.3 (1997): 262-274. Nabhani, Farhad. "Machining of aerospace titanium alloys".Robotics and Computer-Integrated Manufacturing 17.1 (2001): 99-106. Rahman, Mustafizur, Wang Zhi-Gang, and W. O. N. G. Yoke-San "A review on high-speed machining of titanium alloys".JSME International Journal Series C Mechanical Systems, Machine Elements and Manufacturing 49.1 (2006): 11-20. Arrazola, P-J., A. Garay, L-M. Iriarte, M. Armendia, SurendarMarya, and Felix Le Maitre. "Machinability of titanium alloys (Ti6Al4V and Ti555. 3)". Journal of materials processing technology 209.5 (2009): 2223-2230. Benardos, P. G., and G-C. Vosniakos. "Predicting surface roughness in machining: a review".International journal of machine tools and manufacture 43.8 (2003): 833-844. Sahin, Yusuf, and A. RizaMotorcu. "Surface roughness model for machining mild steel with coated carbide tool".Materials & design 26.4 (2005): 321-326. Abouelatta, O. B., and J. Madl. "Surface roughness prediction based on cutting parameters and tool vibrations in turning operations".Journal of materials processing technology 118.1 (2001): 269-277. Ghani, A. K., and I. A. Choudhury. "Study of tool life, surface roughness and vibration in machining nodular cast iron with ceramic tool".Journal of Materials Processing Technology 127. 1 (2002): 17-22. Dimla, Dimla Eric. "The correlation of vibration signal features to cutting tool wear in a metal turning operation".The International Journal of Advanced Manufacturing Technology 19.10 (2002): 705-713. Risbood, K. A., U. S. Dixit, and A. D. Sahasrabudhe. "Prediction of surface roughness and dimensional deviation by measuring cutting forces and vibrations in turning process".Journal of Materials Processing Technology 132.1 (2003): 203-214. Salgado, David Rodríguez, F. J. Alonso, I. Cambero, and A. Marcelo. "In-process surface roughness prediction system using cutting vibrations in turning".The International Journal of Advanced Manufacturing Technology 43.1-2 (2009): 40-51. Upadhyay, Vikas, P. K. Jain, and N. K. Mehta. "In-process prediction of surface roughness in turning of Ti–6Al–4V alloy using cutting parameters and vibration signals".Measurement 46.1 (2013): 154-160. Hessainia, Zahia, Ahmed Belbah, Mohamed AthmaneYallese, TarekMabrouki, and Jean-François Rigal. "On the prediction of surface roughness in the hard turning based on cutting parameters and tool vibrations".Measurement 46.5 (2013): 1671-1681. Singh, H., Kumar, P., “Mathematical models of tool life and surface roughness for turning operation through response surface methodology”. Journal of Scientific and Industrial Research, 66 (2007): 220-226. Tsourveloudis, Nikos C. "Predictive modeling of the Ti6Al4V alloy surface roughness".Journal of Intelligent & Robotic Systems 60.3 (2010): 513-530. Neşeli, Süleyman, SüleymanYaldız, and ErolTürkeş. "Optimization of tool geometry parameters for turning operations based on the response surface methodology".Measurement 44.3 (2011): 580-587. Ramesh, S., L. Karunamoorthy, and K. Palanikumar. "Measurement and analysis of surface roughness in turning of aerospace titanium alloy (gr5)".Measurement 45.5 (2012): 1266-1276. Kosaraju, Satyanarayana, and Venu Gopal Anne. "Optimal machining conditions for turning Ti-6Al-4V using response surface methodology".Advances in Manufacturing 1.4 (2013): 329-339. Das, SudhansuRanjan, Amaresh Kumar, and DebabrataDhupal. "Surface roughness analysis of hardened steel using TiN coated ceramic inserts".International Journal of Machining and Machinability of Materials 17.1 (2015): 22-38. Standard, I. S. O. "3685." Tool-life Testingwith Single Point Turning Tools (1993). Montgomery, Douglas C. Design and analysis of experiments. John Wiley & Sons, (2008). Minitab, Inc. "MINITAB release 17: statistical software for windows." Minitab Inc, USA (2014). Ramesh, S., L. Karunamoorthy, and K. Palanikumar. "Surface roughness analysis in machining of titanium alloy".Materials and Manufacturing Processes 23.2 (2008): 174-181. Hasçalık, Ahmet, and UlaşÇaydaş. "Optimization of turning parameters for surface roughness and tool life based on the Taguchi method".The International Journal of Advanced Manufacturing Technology 38. 9-10 (2008): 896-903. Pramanik, A. "Problems and solutions in machining of titanium alloys".The International Journal of Advanced Manufacturing Technology 70.5-8 (2014): 919-928.