Accepted Manuscript

Design-of-experiments application in machining titanium alloys for aerospace

structural components

Navneet Khanna, J.P. Davim

PII: S0263-2241(14)00528-4
DOI: http://dx.doi.org/10.1016/j.measurement.2014.10.059
Reference: MEASUR 3118

To appear in: Measurement

Received Date: 18 May 2014

Revised Date: 22 September 2014
Accepted Date: 30 October 2014

Please cite this article as: N. Khanna, J.P. Davim, Design-of-experiments application in machining titanium alloys
for aerospace structural components, Measurement (2014), doi: http://dx.doi.org/10.1016/j.measurement.
2014.10.059

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Design-of-experiments application in machining
titanium alloys for aerospace structural components
Navneet Khanna a*, J. P. Davim b
a
Mechanical Engineering Department, IITRAM, Ahmedabad-380026, Gujarat, India
b
Department of Mechanical Engineering, University of Aveiro, Campus Santiago, 3810-Aveiro, Portugal
a*
E-mail address: navneetkhanna@iitram.ac.in
b
E-mail address: pdavim@ua.pt

ABSTRACT
The present investigation is an attempt to study the effect of control factors i.e. the cutting speed,

the feed rate, and the titanium alloys on response variables i.e. the cutting force, the feed force

and the cutting tool temperature by using the Taguchi techniques. A number of experiments were

conducted using the L18 orthogonal array on a CNC vertical machining center. The machining

center was configured to act as a lathe with the tool oriented to produce an orthogonal cut. A

combined technique using orthogonal array and analysis of variance was employed to investigate

the contribution and effects of cutting speed, feed rate and different aerospace grade titanium

alloys (Ti6Al4V, Ti54M and Ti10.2.3) on two force components and cutting tool temperature. The

results revealed that the feed rate was the most influential factor which affects the cutting and

feed forces, while the cutting speed had most significant effect on the cutting tool temperature.

Thereafter, optimal control factors were obtained for minimum response variables.

Keywords: Design-of-Experiments; Titanium; Temperature; Forces; Optimization

1. Introduction

Recently, titanium alloys have received considerable attention due to their excellent corrosion

resistance, high strength-to-weight ratio, high strength at elevated temperatures, and biological

compatibility. Hence, these alloys are used in a wide range of applications in aerospace,

automotive, chemical, and medical industries. The demand of titanium has been steady

increasing in aerospace industry because of its excellent strength-to-weight ratio and

electrochemically compatibility with the increasingly applied composite materials in aerospace

industry. Titanium alloys have outstanding physical-mechanical properties, but they are difficult-

to-machine materials because of their high chemical reactivity, poor heat conductivity, low

modulus of elasticity, which lead to lower production rates and increased tool wear [1-10].

New aircraft designs that make extensive use of composite materials make extensive use of

titanium at the same time. Compared to aluminium, titanium is more compatible with composites

in aircraft assemblies [1, 2]. Still unaddressed, however, is the question of machinability

improvement of titanium alloys. In recent times, the machinability of titanium alloys has become

an important area of research [11-19]. Machinability is defined as "easiness of machining" [13].

The broad criterions for determining machinability are: tool life; cutting temperature; surface

integrity; magnitude of cutting forces, and chip condition. Which criterion is to be chosen for

determining machinability varies in accordance with the requirements of the particular operation

to be carried out.

As discussed earlier, titanium poses several machining challenges. Titanium machining requires

low machining parameters to avoid heat build-up. For instance, during the machining of
aluminium, most of the heat generated gets transferred to the chip, whereas during the machining

of titanium-based parts, most of the heat generated gets transferred to the cutting tool, thus

creating a greater heat concentration on the cutting edge of the tool. This leads to rapid tool

failure. To avoid this, generally, machining is carried out at a lower speed leading to lowered

productivity. Machining of titanium can cost 10 times more than machining aluminium [20-21].

To improve the productivity of titanium alloy components, efforts are being made in the diverse

__
areas affecting machinability development of newer titanium alloys, heat treatment, tool

material and geometry, cutting conditions, etc. Titanium alloys like Ti54M and Ti10.2.3 are

increasingly being used in the aerospace industry. Ti54M alloy is an α+β titanium alloy that was

developed to improve the production costs compared to Ti6Al4V while providing similar

properties to that of Ti6Al4V but with better machinability (roughly 10% to 20% better than

Ti6Al4V). In the near future, Ti54M is likely to replace the Ti6Al4V in the aerospace

applications [4-15]. Ti10.2.3 is a β titanium alloy developed in the 1970's; it has improved

forgeability [2]. Among all titanium materials, the group of β (Ti10.2.3) titanium alloys offers

the highest strength-to-weight ratio. Increased usage of materials like Ti10.2.3 is due to their

deeper hardenability in comparison to α+β titanium alloys (Ti6Al4V and Ti54M). These are

mainly being used in the landing gear parts of the aircrafts. Unfortunately, β alloys are even more

difficult to machine than α+β titanium alloys. This leads to higher manufacturing costs [17-19].

Ongoing development work on newer titanium alloys with varying chemical compositions is

driven by the need to continuously improve machining efficiency and productivity. [13, 14] have

found that the machinability depends on the chemistry, i.e. on strength and microstructure. A few
research papers have been published dealing with the machining of Ti54M and Ti10.2.3 alloys

[3-19]. The experimental studies carried out on Ti54M and Ti10.2.3 alloy in various heat

treatment conditions showed that these alloys have the better machinability in the annealed heat

treated condition [6-11].

It is evident from the comprehensive review on Ti54M and Ti10.2.3 titanium alloys and to the

best knowledge of the author; very limited effort has been made to investigate the machining

performance of these titanium alloys by using design-of-experiments (DOE) technique. This

comprehensively used technique ensures valid and unbiased conclusions. DOE methods such as

response surface methodology, factorial design and Taguchi methods are now extensively used

in place of one-factor-at-a-time experimental approach which is time consuming and

unreasonable in cost. The knowledge of the contribution of individual control variable in any

manufacturing process is vital in deciding the nature of control to be administered during

operation [22-24]. Therefore, the primary objective of this exhaustive investigation is the

parametric optimization of the orthogonal machining of three titanium alloys i.e. Ti6Al4V,

Ti54M and Ti10.2.3 titanium alloys in relation to cutting force (FC), feed force (FK), and cutting

tool temperature using DOE analysis. The previous studies [8-11] were succinct and the present

investigation provides detailed description and methodology of the experimental design and its

in-depth statistical analysis. The study will be carried out for the orthogonal machining process

with tailor-made carbide cutting tools at the above mentioned control variables. These control

variables have been finalized as per the literature review and requirements of the sponsoring

industries.
2. Experimental Plan and Setup

2.1 Material Details

Chemical composition of the three analyzed titanium alloys is summarized in Table 1. The

comparison of mechanical properties is shown in Fig.1. All the three titanium alloys were taken

in annealed heat treated condition. Ti10.2.3 alloy shows superior strength, inferior ductility and

fatigue strength over Ti6Al4V and Ti54M alloys. The strength increases with increased content

of β stabilizers amid reduction in ductility.

2.2 Machining Arrangement Details

The workpieces of all the three alloys were machined from the solid bars to the final dimension

having 2 mm wall thickness. Dry machining of 5 second duration was conducted on a CNC

machining centre (Fig. 2).

Table 1. Chemical composition of titanium alloys.

Titanium Chemical composition (by weight %)

alloy V Mo Fe Al O
Ti6Al4V 4 - 0.15 6 0.18
Ti54M 4 0.8 0.5 5 0.18
Ti10.2.3 10 - 2 3 0.13
 Ti6Al4V Ti54M Ti10.2.3
1200 23 102
1100
990 935
910 860 18 84
31 31 32

9 44

YS(MPa) UTS (MPa) Elongation (%) K1C (MPa √m) Hardness (HRC)

Fig. 1. Comparison of the mechanical properties for α+β (Ti6Al4V and Ti54M) and metastable β
(Ti10.2.3) titanium alloys.

Experiments were performed using uncoated carbide inserts (TNMG 160408-23 H13A) at

three feed rates (0.1 mm.rev-1, 0.15 mm.rev-1 and 0.25 mm.rev-1), two cutting speeds (40 m.min-1

and 80 m.min-1) and three variants of titanium alloys (Ti10.2.3, Ti54M and Ti6Al4V) at 2 mm

depth of cut. The inserts were having following geometry:

Insert Included Angle = 60º,

Rake Angle (γ0) = 7º,

Inclination Angle (λS) = 0º,

Clearance Angle major (α0) = 0º,

Corner Radius (rε) = 0.8 mm, and

Cutting Edge Roundness (rβ) = 34 ± 2 µm

The temperature at the cutting tool rake face was measured, using infrared radiation technique,

with the help of a FLIR thermal imaging system (FLIR 550M+). This was a medium wavelength

infrared (MWIR) camera system and consisted of a digital camera head and a high-speed data

acquisition system. The control of the camera includes, but is not limited to, adjusting the

integration time to change sensitivity due to temperature and the measured surface characteristics.
The camera had a 320 by 256 Indium-Antimonide (InSb) detector array, sensitive to the MWIR in

the range of 3.97 to 4.01 microns. To reduce noise, the detector array is cooled with liquid nitrogen.

Temperature range for the calibration was set between 300 to 1500 °C for the both the sides of

50 mm camera lens. The frame size was adjusted to decrease or increase the number of frames

captured per second. This depends on the area to be analyzed and the amount of information to be

saved. The integration time was fixed at 200 µs. The frame size was abridged to 80 by 64 pixels

(enough to see the cutting area with a spatial resolution of 10 µm per pixel), allowing an

acquisition frequency of 1250 Hz (40000 Pixels/s). Moreover, the frequency is limited by the

window size.

Temperature values were measured at 0.3 mm distance (d) from the grounded surface of the

insert (Fig. 3(a)). Although the position of the workpiece/insert contact was also a controlled

parameter before the experiment, high intensity during experimentation may involve some small

variations thereof. The value of d is a critical parameter in determining the final temperature;

therefore, inserts were inspected with a microscope to determine the value of d after each

experiment. To analyze the influence of the parameter d on the final temperature, a series of

experiments were conducted on a titanium alloy at collaborative university. Therefore, the inserts

were inspected with a Leica Z16 APO optical microscope after each experiment to determine the

value of ‘d’, using which the acquired temperature values were proportionally corrected in

accordance with Figure 3 (b) to obtain the actual temperature. This was done to make the results

proportional. The average uncertainties for the cutting tool temperature were approximately

±500C. These uncertainties were attributed to fluctuations around the mean emissivity, stray light

from other sources, surface location fluctuations, and focus conditions due to the low Young's

modulus of titanium alloys. A MATLAB program was used to find these uncertainties in the
 Figure 2. Experimental setup
temperature profiles. The tool was mounted on a stationary dynamometer (Kistler 9121) in order

to measure the cutting force. The dynamometer was firmly connected to the base plate of the

machining centre. The Medatek software and Altair software were used to capture force and

thermal sequences respectively. The data acquisition system was a combination of PC based

hardware and software to provide a flexible and user friendly measurement system. When the

data acquisition was completed, the sequences automatically loaded itself within the Medatek

and Altair softwares and the files were saved. The frames were examined for pixel saturation and

image quality. Integration time of 200 µs provided acceptable image quality. If the image quality

was poor, the test was repeated following slight adjustments to the setup.
 Fig. 3. (a) Schematic representing the parameter ‘d’ on the cutting tool insert, and (b)
Graph showing the influence of ‘d’ on the acquired temperature.

2.3 Experimental Design

This paper makes use of Taguchi’s method for designing the experiments. Taguchi

recommends use of orthogonal arrays for laying out the experiments. The optimum condition is

identified by studying the main effects of each of the variables. The main effects indicate the

general trend of influence of each variable. The knowledge of contribution of individual

variables is a key factor in deciding the nature of control to be established on a production

process [25- 31]. The major steps required for the experimental design using Taguchi method are

(1) determination of objective function, (2) classification of the factors and their levels, (3)

selection of an appropriate orthogonal array (OA), (4) experimentation and data collection, and

(5) investigation of the data and determination of the optimal levels.

The purpose of experimentation should be to reduce and control variation of a product or a

process; subsequently, decisions must be made regarding which variables affect the performance

of a product/process. Analysis of variance (ANOVA) is the statistical treatment applied to the

results of the experiments in predicting the contribution of each controlled variable against a

stated level of confidence. The study of ANOVA table for a given analysis helps to determine

which of the variables need control and which do not [26]. ANOVA and mean effect plots were

determined using Minitab16 Software.

2.3.1 Determination of Objective Function

The approach to be used for completing the analysis, which Taguchi strongly recommends

for multiple runs, is to use Signal-to-Noise (S/N) ratio. The S/N ratio is a concurrent quality

metric linked to the loss function [23]. By maximizing the S/N ratio, the loss associated can be

minimized. The S/N ratio determines the most robust set of operating conditions from variation

within the results. The objective function in this work is minimization, the ratio of S/N ratio

defined according to the Taguchi method:

S/N = −10 log 10 [1/n∑  ] (1)

 Where S/N denotes the observed value (unit: dB), and Yi is the value of the characteristic i

and n is the number of observations or number of repetitions in a trial.

In the present investigation, both analysis; i.e. the mean data analysis and S/N data analysis

have been performed. The effects of the selected process variables on the selected performance

characteristics have been investigated through the plots of the main effects based on mean data,

S/N data and respective response tables. The optimum condition for each of the performance

characteristics has been established through S/N data analysis aided by the mean data analysis.

2.3.2 Classification of the Factors and their Levels

The numbers of experimental variable levels were chosen based on the requirement of

aerospace industry in terms of: feed rate, cutting speed, and titanium alloy variant. The cutting

speed factor has two levels whereas other two variables such as feed rate and the alloy variant

have three levels each. The details of selected control parameters have been already discussed.

2.3.3 Selection of an Appropriate Orthogonal Array (OA)

The Taguchi method uses S/N ratio to measure the variations of the experimental design. Based

on the previous sub-section, L18 array was selected for the present investigation. In order to

guarantee that the chosen OA design will provide sufficient degrees of freedom for the proposed

experiment, the following condition must be fulfilled: Number of degrees of freedom for OA

greater than or equal to number of degrees of freedom required for studying the main and

interaction effect [31, 32]. L18 array has a special property that the two way interactions between

the various variables are partially confounded with various columns and hence their effect on the
assessment of the main effects of the various variables is minimized. It is not possible to assess

the possible two factor interactions in L18 array but the main effects of different process variables

can be assessed with reasonable accuracy. No outer array has been used instead; experiments

have been repeated three times at each experimental condition.

Table 2. Design and experimental results of the L18 orthogonal array experiment.

Control Variables Average of Responses S/N Ratio (dB)

S.No. Vc F Titanium Temperature Fc Fk
Temperature Fc Fk
(m/min) (mm/rev) Alloy (oC) (N) (N)
1. 40 0.10 Ti6Al4V 531 409 285 -54.5019 -52.2345 -49.0969
2. 40 0.10 Ti54M 547 400 291 -54.7597 -52.0412 -49.2779
3. 40 0.10 Ti10.2.3 645 450 354 -56.1912 -53.0643 -50.9801
4. 40 0.15 Ti6Al4V 623 558 313 -55.8898 -54.9327 -49.9109
5. 40 0.15 Ti54M 650 550 308 -56.2583 -54.8073 -49.771
6. 40 0.15 Ti10.2.3 743 590 356 -57.4198 -55.417 -51.029
7. 40 0.25 Ti6Al4V 715 780 346 -57.0861 -57.8419 -50.7815
8. 40 0.25 Ti54M 784 818 352 -57.8863 -58.2551 -50.9309
9. 40 0.25 Ti10.2.3 864 874 420 -58.7303 -58.8302 -52.465
10. 80 0.10 Ti6Al4V 754 408 281 -57.5474 -52.2132 -48.9741
11. 80 0.10 Ti54M 894 406 302 -59.0268 -52.1705 -49.6001
12. 80 0.10 Ti10.2.3 865 404 294 -58.7403 -52.1276 -49.3669
13. 80 0.15 Ti6Al4V 850 525 304 -58.5884 -54.4032 -49.6575
14. 80 0.15 Ti54M 977 529 306 -59.7979 -54.4691 -49.7144
15. 80 0.15 Ti10.2.3 962 539 314 -59.6635 -54.6318 -49.9386
16. 80 0.25 Ti6Al4V 947 747 344 -59.527 -57.4664 -50.7312
17. 80 0.25 Ti54M 1012 752 340 -60.1036 -57.5244 -50.6296
18. 80 0.25 Ti10.2.3 1069 801 365 -60.5796 -58.0727 -51.2459

2.3.4 Experimentation and Data Investigation

Each trial was replicated thrice, hence, three cuts were made for each of the eighteen trial

runs and moreover, all the fifty four trial runs in all were executed in completely randomized

fashion to reduce the effect of experimental noise to the maximum possible extent. Each test,

with a particular process condition, was carried out using a new insert edge. The average of
experimental results and their calculated S/N ratios are summarized in Table 2 for force

components and cutting tool temperature as the response variables. Then after collecting all the

data for all combinations, ANOVA was carried out, the contribution of each factor was predicted

and the best parametric levels along with confidence intervals were established.

3. Results and Discussion

3.1 ANOVA

The experimental results were analyzed using ANOVA for identifying the significant factors

affecting the performance measures. The results of ANOVA for the cutting force and the feed

force are shown in Tables 3(a) and 3(b), respectively. The ANOVA result for the cutting tool

temperature is shown in Table 3(c). This analysis was carried out for a significance level of α =

0.05 (confidence level of 95%). Tables 3(a, b and c) show the realized significance levels,

associated with the F tests for each source of variation. The second from last columns of the

tables show the P-values of ANOVA. When P-values are less than 0.05, the source effect on

response is considered to be statistically significant at 95% confidence level. The principle of the

F test is that the larger the F value for a particular variable, the greater the effect on the

performance characteristic due to the change in that process variable [26-29]. The portion of the

total variation observed in the experiment attributed to each factor is reflected by the %

contribution in the last column of the tables.

In Tables 3(a) and 3(b), the ANOVA result shows that the F value for the factor feed rate is

larger than that of the other two factors, i.e., the largest contribution to the cutting force and feed

force is due to the feed rate. The effect of all the control variables on cutting force is found to be

statistically significant (P-value < 0.05). Cutting speed, feed rate and alloy contributed 1.20%,
96.63% and 1.13%, respectively. The effect of cutting speed factor on feed force is found to be

statistically insignificant (P-value > 0.05). Feed rate and alloy contributed 53.59% and 24.39%,

respectively.

In Table 3(c), the ANOVA result shows that the F value for the factor cutting speed is larger

than that of the other two variables, i.e., the largest contribution to the cutting tool temperature is

due to the cutting speed. Cutting speed, feed rate and alloy contributed 62.23%, 25.09% and

10.13%, respectively.

As shown in Figures 4, 5 and 6, the lowest cutting and feed force, and cutting tool temperature

values are obtained with the Ti6Al4V titanium alloy. This can be attributed to the low

mechanical properties of α+β titanium alloys due to the lower content of β stabilizers. These

results disagree to some extent with the recent studies held by [3, 5-7, 12-15], where Ti54M was

found to have slight better machinability than Ti6Al4V alloy. This modest dissimilarity in results

may be attributed to little variation in chemical composition and heat treatment steps performed

for both the alloys. It was found that cutting speed did not have a significant impact with a

percentage contribution of 7.85% on feed force. Cutting and feed forces decrease with increasing

cutting speed in the analyzed cutting speed range. This can be explained by the increased thermal

softening effect, due to higher temperatures generated in the tool/chip contact area as a function

of increased cutting speed. This finding also suggests that the corresponding temperature

increase over this range at all feed rates is sufficient to dominate strain rate hardening for almost

all the alloys apart from an exception. This tendency is expected because machining becomes

highly adiabatic for titanium alloys due to their low thermal conductivity and the heat generated

in the primary shear zone cannot be carried away during the very short interval of time during

which the material passes through this zone.

It is observed that the feed rate had the highest impact, exhibiting a contribution of 96.63% and

53.59% on cutting and feed force, respectively. Additionally, as the increase in the feed rate

causes an increase in the chip section, the cutting and feed forces also increase considerably. It

was found that cutting speed had the highest impact with a percentage contribution of 62.23% on

cutting tool temperature. The relationship between the cutting tool temperature and the cutting

and feed forces for all machining trials can be observed from Figures 4, 5 and 6.The cutting tool

temperature is inversely proportional to cutting force and directly proportional to feed force for

all the machining trials. β titanium alloy (Ti10.2.3) is having the superior hardness (Fig.1). This

leads to an increase in applied stress for plastic deformation of the material in shear zone, as a

result; the cutting and feed forces and the cutting tool temperature increase significantly as

compared to α+β alloys.

Table 3. Analysis of Variance of means for (a) cutting forces, (b) feed forces, and (c) cutting tool
temperature.
(a)
Source DF Seq SS Adj SS Adj MS F P Contribution%
Cutting Speed 1 5618 5618 5618 15.44 0.004 1.20
Feed Rate 2 451277 451277 225638 620.10 0.000 96.63
Alloy 2 5297 5297 2649 7.28 0.016 1.13
Feed Rate*Alloy 4 1903 1903 476 1.31 0.345 0.41
Residual Error 8 2911 2911 364 0.62
Total 17 467006

(b)
Source DF Seq SS Adj SS Adj MS F P Contribution%
Cutting Speed 1 1701.4 1701.4 1701.39 5.08 0.054 7.85
Feed Rate 2 11621.8 11621.8 5810.89 17.36 0.001 53.59
Alloy 2 5288.4 5288.4 2644.22 7.90 0.013 24.39
Feed Rate*Alloy 4 396.6 396.6 99.14 0.30 0.873 1.83
Residual Error 8 2678.1 2678.1 334.76 12.35
Total 17 21686.3

(c)
Source DF Seq SS Adj SS Adj MS F P Contribution%
Cutting Speed 1 275777 275777 275777 207.78 0.000 62.23
Feed Rate 2 111177 111177 55588 41.88 0.000 25.09
Alloy 2 44876 44876 22438 16.91 0.001 10.13
Feed Rate*Alloy 4 709 709 177 0.13 0.966 0.16
Residual Error 8 10618 10618 1327 2.40
Total 17 443157
 Main Effects Plot for SN ratios for Cutting Force
Data Means

Cutting Speed Feed Rate

-52

-54
Mean of SN ratios

-56

-58
40 80 0.10 0.15 0.25
Alloy
-52

-54

-56

-58
Ti10.2.3 Ti54M Ti6Al4V

Signal-to-noise: Smaller is better

Main Effects Plot for Means for Cutting Force

Data Means
Cutting Speed Feed Rate
800

700

600
Mean of Me ans

500

400
40 80 0.10 0.15 0.25
Alloy
800

700

600

500

400
Ti10.2.3 Ti54M Ti6Al4V

Fig. 4. Main effect plots for S/N ratio and means for cutting force (N).
 Main Effects Plot for SN ratios for Fee d Force
Data Means

Cutting Speed Feed Rate

-49.5

-50.0

-50.5
Mean of SN ratios

-51.0

40 80 0.10 0.15 0.25

Alloy
-49.5

-50.0

-50.5

-51.0

Ti10.2.3 Ti54M Ti6Al4V

Signal-to-noise: Smaller is better

Main Effects Plot for Me ans for Fee d Force

Data Means
Cutting Speed Feed Rate
360

345

330
Mean of Means

315

300
40 80 0.10 0.15 0.25
Alloy
360

345

330

315

300
Ti10.2.3 Ti54M Ti6Al4V

Fig. 5. Main effect plots for S/N ratio and means for feed force (N).
 Main Effects Plot for SN ratios for Cutting Tool Temperature
Data Means
Cutting Speed Feed Rate

-57

-58
Mean of SN ratios

-59

40 80 0.10 0.15 0.25

Alloy

-57

-58

-59

Ti10.2.3 Ti54M Ti6Al4V

Signal-to-noise: Smaller is better

Main Effects Plot for Means for Cutting Tool Temperature

Data Means
Cutting Speed Feed Rate

900
850
800
750
Mean of Means

700

40 80 0.10 0.15 0.25

Alloy

900
850
800
750
700

Ti10.2.3 Ti54M Ti6Al4V

Fig. 6. Main effect plots for S/N ratio and means for cutting tool temperature (oC).
Tensile strength is one of the most crucial metallurgical parameters which controls the

machinability of the titanium alloys. The tensile strength for as received workpieces of Ti6Al4V,

Ti54M and Ti10.2.3 alloys in annealed condition are shown in Fig.1. The tensile strength of the

Ti10.2.3 alloy is maximum. Therefore, it can be deduced that machining a stronger alloy resulted

in the generation of higher forces even at relatively lower cutting speeds due to significantly

larger amount of energy required to plastically deform the material into chips. Yield strength

achieved in Ti6Al4V and Ti54M alloys is lower and, thus, these alloys produced lower cutting

forces during plastic deformation, i.e. shearing. It is interesting to note that Ti10.2.3 alloy that

had exhibited inferior machinability in terms of cutting forces, revealed higher cutting tool

temperature as compared to Ti6Al4V and Ti54M alloys. It shows that the strength determines the

machinability of titanium alloys which further depends upon the alloy chemistry and

microstructure. These results correlated well with the chemical composition and mechanical

properties of these titanium alloys as presented in Table 1 and Fig. 1 respectively. [1, 3, 13-14,

21] reported similar results regarding the machinability of titanium alloys. They claimed that the

poor machinability was due to the higher molybdenum equivalent value. This higher

molybdenum equivalent value was found due to the higher content of beta stabilizers in the

respective titanium alloy.

3.2 Evaluation of Means and S/N Ratios for Optimal Design

The mean response refers to the average values of the performance characteristics for each

variable at different levels. The average values of mean data and S/N data for cutting speed, feed

rate and chemical composition (in terms of different alloys) were obtained separately and are

given in Tables 4, 5, and 6. These values are plotted in Figures 4, 5, and 6. In the Taguchi

method, the higher the levels for S/N ratio, the better the overall performance, meaning that the
factor levels with the highest S/N ratio value should always be selected. Regardless of the lower-

the-better/higher-the-better quality characteristic, the greater S/N ratio corresponds to the smaller

variance of the response characteristics around the target value [25-29].

Based on the S/N ratio and ANOVA, the optimal control variables for cutting force are the

cutting speed at level 2, the feed rate at level 1 and alloy variant at level 3 (Table 4). The optimal

control variables for feed force are the feed rate at level 1 and alloy variant at level 3 (Table 5). It

is clear from the Table 6 that the cutting speed at level 1, the feed rate at level 1 and alloy variant

at level 3 are optimal control factors in terms of the cutting tool temperature.

The Tables 4, 5, and 6 include ranks based on Delta statistics, which compare the relative

magnitude of effects. The Delta statistic is the highest minus the lowest average for each factor.

Ranks were assigned based on Delta values; rank 1 to the highest Delta value, rank 2 to the

second highest, and so on. The descending order of ranks is given as Vc > f > alloy; f > alloy >

Vc and f > alloy > Vc for cutting tool temperature, cutting and feed forces, respectively. Control

variables and their selected values for optimal cutting force, feed force and cutting tool

temperature are summarized in Table 7.

Table 4. Response table for signal to noise ratios and means for cutting forces.

S/N data Mean data

Level
Vc f Titanium alloy Vc f Titanium alloy

1 -55.27 -52.31 -55.36 603.2 412.8 609.7

2 -54.79 -54.78 -54.88 567.9 548.5 575.8

3 -58.00 -54.85 795.3 571.2

Rank 3 1 2 3 1 2
 Table 5. Response table for signal to noise ratios and means for feed forces.
S/N data Mean data
Level
Vc f Titanium alloy Vc f Titanium alloy

1 -50.47 -49.55 -50.84 336.1 301.2 350.5

2 -49.98 -50.00 -49.99 316.7 316.8 316.5

3 -51.13 -49.86 361.2 312.2

Rank 3 1 2 3 1 2

Table 6. Response table for signal to noise ratios (smaller is better) and means for cutting tool
temperature.
S/N data Mean data
Level
Vc f Titanium alloy Vc f Titanium alloy

1 -56.52 -56.79 -58.55 678.0 706.0 858.0

2 -59.29 -57.94 -57.97 925.6 800.8 810.7

3 -58.99 -57.19 898.5 736.7

Rank 1 2 3 1 2 3

Table 7. Control variables and their selected values (for optimal response variables).

Optimal Values for Response Variables

Control Variables Cutting Force Feed Force Cutting Tool Temperature

Cutting Speed Suitable to

80 40
(m/min) Industry

Feed Rate
0.1 0.1 0.1
(mm/rev)

Titanium Alloy Ti6Al4V Ti6Al4V Ti6Al4V

3.3 Estimation of Optimum Quality Characteristics

A most important advantage of conducting Taguchi’s methodology is to determine the near

optimum or the range of process variable levels where global optimum exists [30]. The optimum

level for a factor is the level that gives the minimum value of cutting and feed forces and cutting

 , .
tool temperature. The significant factors selected for the cutting force are  .
̅ and 64

The optimal value of the cutting force can be computed as

 = 
 + .  − 2
̅ + 64 (2)


Where  is mean value of the cutting force,  = 585.56 N (Table 2) and  ̅ and

 , .

 are average values of the cutting speed, feed rate and titanium alloy variant,
64

respectively (Table 4), i.e.  = 380.78 N.

CI = Fα !1, f% &V% /n%** (3)

Where Fα !1, f% &= the F-ratio at a confidence level of 95% against DOF 1 and error DOF f% ,

V% = error variance, n%** is the effective number of replications:

n%** = N / {1 + !Total DOF in the estimation of mean&} (4)

Where N = total number of results. The confidence interval (C.I.) at 95% is ±14.67 N. Thus,

the predicted optimum cutting force is 366.11 <  < 395.45 N.

̅ and 64
The significant factors selected for the feed force are .  (Table 5). The optimal

value of the feed force can be computed as

 ̅ + 64
9 = .  − 
9

i.e. 9 = 287.01 N.

By using equations 3 and 4, the confidence interval (C.I.) at 95% is ±11.49 N. Thus, the

predicted optimum feed force is 275.52 < 9 < 298.5 N.

: , .
The significant factors selected for the cutting tool temperature are   (Table
̅ and 64

6). The optimal value of the cutting tool temperature can be computed as

̅ + 64
;<< = : + .  − 2;<<

i.e. ;<< = 517.14 oC.

By using equations 3 and 4, the confidence interval (C.I.) at 95% is ±28.01 oC. Thus, the

predicted optimum cutting tool temperature is 489.13 < ;<< < 545.15 oC.

4. Conclusions

It is desirable to provide aero components made-up of titanium alloys with superior strength

without sacrificing machining performance. Keeping this in mind the cutting and feed forces and

cutting tool temperature of orthogonally machined titanium alloys have been investigated using

the Taguchi techniques. Three important control variables, that is the cutting speed, the feed rate

and the titanium alloy variant have been studied. The following conclusions can be drawn from

the research:

1. The optimum control parameters have been obtained for getting minimum cutting force, feed

force and cutting tool temperature.

2. The minimum cutting force is found with cutting speed of 80 m.min-1 , feed rate of 0.1 mm.rev-
1
and Ti6Al4V titanium alloy. The minimum feed force is found with feed rate of 0.1 mm.rev-1

and Ti6Al4V titanium alloy. The cutting speed was found to have statistically insignificant

affect on feed force and thus must be set at a level which is most appropriate and economical to

industry. The minimum cutting tool temperature is found with cutting speed of 40 m.min-1 ,

feed rate of 0.1 mm.rev-1 and Ti6Al4V titanium alloy.

3. The optimum selection of the chemical composition is very important to ascertain the affable

evolution of microstructure for attaining the minimum cutting and feed forces and cutting tool

temperature.

4. The lowest cutting and feed force and cutting tool temperature values are obtained with the

Ti6Al4V titanium alloy. This can be attributed to the low mechanical properties of α+β

titanium alloys due to the lower content of β stabilizers. These results disagree to some extent

with the recent studies, where Ti54M was found to have slight better machinability than

Ti6Al4V alloy. This modest dissimilarity in results may be attributed to little variation in

chemical composition and heat treatment steps performed for these alloys. Moreover, tool life

and surface integrity issues need to be analyzed further for comprehensive interpretation of

machinability.

The experimental studies performed for the industry requirement have also added machinability

data to the existing scarce database on machinability of Ti54M and Ti10.2.3 alloys. This research

data contributes to the existing database and will help the researchers/practitioners in this area to

develop numerical models in future for cost effective research. This research also concludes further

modifications in thermo-mechanical processing and chemical composition of increasingly used

Ti54M and Ti10.2.3 titanium alloys, so as to improve their machinability with respect to Ti6Al4V
alloy.

Acknowledgements

The experiments were performed within the collaboration of BITS Pilani (India) and MGEP

(Spain) to facilitate Mr. Navneet Khanna to undergo training within the High Performance

Cutting Research Group of University of Mondragon– Faculty of Engineering (MGEP) and Dr.

Navneet Khanna is thankful to them. Dr. Navneet Khanna is extremely grateful to the associated

faculty and staff members at MGEP for their support.

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NOMENCLATURE
α = alpha
β = beta
d = distance
YS = yield strength
UTS = ultimate tensile strength
HT = heat treatment
CTT = cutting tool temperature
CC = chemical composition
Vc = cutting speed
f = feed rate
Fc = cutting force
Fk = feed force
dB = decibel
ANOVA = analysis of variance