In-situ temperature monitoring during rotary ultrasonic-assisted drilling of fiber-reinforced composites

Abstract

Drilling polyetherimide composite is difficult due to the anisotropic and inhomogeneous structure of fiber and matrix. Drilling of fiber-reinforced composites is a thermo-mechanical friction procedure required in various aerospace and automotive applications. The major reason for component rejection during manufacturing processes is due to thermal damage and temperature elevation defects such as delamination and fiber pullout caused during drilling. This work aims to propose a rotary ultrasonic-assisted drilling technique for composite materials to reduce thermal damage using diamond-impregnated tools. The influence of drilling parameters such as rotational speed, feed rate, abrasive grit size, and ultrasonic power was monitored on temperature elevations. The temperature was monitored using thermocouples located at different distances of 1.0, 2.0, and 3.0 mm from the main drilled hole. The response surface methodology was used for the design of experiments during the drilling of composite material and optimization of process parameters was carried out using analysis of variance. Rotational speed and abrasive grit size were observed to have the highest contribution of 42% and 37% for temperature elevations respectively. It was witnessed that temperature increases with an increase in rotational speed, feedrate, and abrasive grit size. Increasing the ultrasonic power during drilling temperature can be minimized. The temperature elevations at the tool-composite interface can be reduced by leveraging the application of rotary ultrasonic-assisted drilling.

Introduction

The composite materials are broadly used in various industries such as automobiles, aviation, aerospace, architecture, sports equipment, marine, turbomachinery, and electronic shielding.^1–4 Polyetherimide composite has been widely utilized due to their cost-effectiveness and unique properties.^5–7 The excellent mechanical properties of polyetherimide composites ensure easy machining, high dimensional rigidity, and high strength.^8–10 High temperatures during composite drilling influence micro and macro geometrical damages such as delamination, hole quality, microcracks, fiber pullout, and surface morphology damage.^11–13 Various methods were used in the literature to monitor temperature change during drilling processes such as thermocouples, photographic images, infrared pyrometers, and thermal cameras.^14–16 During drilling, thermocouples are mostly used for adequate measurement of temperature elevations. Composite drilling involves converting mechanical energy into thermal energy, causing a temperature rise of the composite and drill tool continuously. During the composite drilling, direct contact at the tool-composite interface results in significant amounts of friction, temperature generation and heat accumulation due to the removal of material. Temperature increases near the drilling site can result in thermal degradation, delamination, fiber pullout, microcracks and surface topology defects resulting to weakens the strength and rejection of composite material.

There are various methods of drilling in the literature to reduce the thermal damage at the drill site of composite material.^17–24 One of the most used machining methods for composites drilling is ultrasonic drilling (UD). Ultrasonic drilling has proved its effectiveness in drilling brittle materials that are also hard independent of their electrical properties. The material removal mechanism is through abrasion by slurry suspended with abrasive particles, which is introduced at the rotary tool and workpiece interface. ²⁵ Rotary ultrasonic drilling (RUD) is an advanced hybrid non-traditional machining process that utilizes the synergetic effect of conventional machining and diamond grinding by employing a tool impregnated with diamond abrasives.^26–29 Cong et al. ³⁰ studied the influence of temperature during RUD of carbon fiber-reinforced polyetherimide (CFRP) using two different temperature measurement techniques, fiber optic sensors and thermocouples. The dynamic relationship between the process parameters and temperature has also been determined. It was observed that temperature increases with increasing the spindle speed of tool. Wang et al. ³¹ studied the effect of temperature on the quality and properties of machined CFRP composite.

Zou et al. ³² observed the temperature rise during RUD of composite material. It was observed that ultrasonic power has an important impact on cutting temperature. The authors ascertained cutting temperatures at four different ultrasonic powers (0, 20, 40 and 60%). Lotfi et al. ³³ used rotary ultrasonic-assisted milling of CFRP and titanium to improve the surface morphology at drill site. Kumaran et al. ³⁴ attempted RUD of CFRP composite. It was observed that RUD in a cryogenic domain delivered a reduced burr region and surface roughness. Chen et al ³⁵ studied the influence of drilling parameters on temperature elevations and delamination. It was observed that the cutting temperature decreases with decreasing feedrate during drilling. Gupta et al. ³⁶ employed RUD of porcine bones to monitor temperature elevations. The influence of drilling parameters such as spindle speed, feedrate, drill diameter and amplitude on temperature rise during RUD was studied. It was concluded that temperature increases with an increase in tool speed, drill diameter and feedrate. Agarwal et al. ³⁷ observed a decreasing trend of temperature rise with an increase in vibrational amplitude during RUD. Gupta et al. ³⁸ compared the drilling of bones with conventional and ultrasonic drilling using hollow drill tools. It was observed that drilling with ultrasonic-assisted drilling using a hollow tool resulting lower temperature rise.

Polyetherimide composites have various attractive properties in automotive and aerospace applications. But the temperature elevations and thermal damage during the conventional drilling of composite influence the surface morphology, defects, damage, and mechanical properties of the polyetherimide composites. Hence, there is a need for an efficient drilling process to minimize temperature rise during drilling to avoid thermal damage and drill-induced defects. The key focus of this study is to minimize heat generation during the drilling of composite material leveraging rotary ultrasonic-assisted drilling using a diamond-impregnated hollow drill tool. The influence of various drilling parameters such as rotational speed, feed rate, abrasive grit size, and ultrasonic power was observed on temperature rise. The temperature was monitored using thermocouples. Response surface methodology was used for the design of experiment for drilling. The in-situ monitoring of temperature elevations and optimization of drilling parameters during rotary ultrasonic drilling of composite material is the key contribution of the present work.

Material and methodology

Workpiece details

The workpiece used for the present study is polyetherimide composites (PEC) which were fabricated using carbon fibers of polyetherimide and a solution of dichloromethane. During the fabrication of the composite, the solution was prepared and cutting of carbon fabric was done. The direction of cutting of fabrics strongly impacted the wear function of the workpiece. After cutting the fibers, the solution was poured over the carbon fiber to dip uniformly. Fabrics were taken out of the solution and dried under pressure. During the last step, the compression of fabric was achieved by using a moulding machine. The detailed steps of fabrication were discussed in our previously published article. ³⁹ The density of composite was 1.28 g/cm³, a glass transition temperature of 217°C, ⁴⁰ a process temperature ranging from 330°C to 390°C, ⁴¹ and a degradation temperature of 400°C. ⁴²

Experimental setup

For the drilling investigation of composite material, a hollow drill tool was designed and manufactured. For the drill tool, the shank diameter was fixed at 6.5 mm. The hollow tools had an inner diameter of 2.8 mm and an outer diameter of 4.5 mm. Experiments were conducted with a diamond-impregnated tool with varying the grit size of abrasives. Diamond abrasive particles were electroplated on an EN-31 steel hollow shank. The diamond-impregnated tool was fixed in the ultrasonic transducer to act as an ultrasonic horn during drilling as illustrated in Figure 1(a). The experimental study was carried out on a CNC milling machine with the attached assembly of the ultrasonic horn as shown in Figure 1(b). The mild steel vertical angle rod was connected to the spindle head. Carbon brushes along with tool holders were installed on these steel rods. The carbon brushes were connected to the slip rings that provide voltage signals to the ultrasonic horn to perform rotary ultrasonic-assisted drilling of the composite.

Figure 1.

(a) Schematic of ultrasonic tool assembly, (b) experimental RUD setup.

All experiments were conducted on the same drilling setup and the corresponding temperature was monitored using k-type thermocouples. Three different k-type digital thermocouples were placed in the predrilled craters in the polyetherimide composites at three different positions (1.0, 2.0 and 3.0 mm). The schematic diagram for thermocouples position from the main drilled hole is presented in Figure 2 k-type thermocouples with an accuracy of ±0.3%°C were used to record temperature elevations. Before recording the value of temperature, all digital thermocouples were calibrated carefully. The initial value (T _in ) and final value (T _fin ) of temperatures were noted with each digital thermocouple, and changes in the temperature (ΔT) were ascertained to obtain a set of optimal parameters for the generation of minimum temperature. The experiments were conducted at room temperature.

Figure 2.

(a) Location of thermocouples from the drilled crater, (b) Phoenix thermocouple (K type) with Al–Cu probe.

Process parameters and design of experiments

Different drilling parameters were selected during the current investigation such as rotational speed, feedrate, abrasive grit size, and ultrasonic power. Table 1 presents the machining input factors with their different levels chosen for the current study. The rotational speed is the speed of the rotating drilling tools and is found to be an important parameter during drilling. Based on the literature review and limitations of the experimental setup, a range of 550 and 2550 r/min rotational speeds have been selected. Composite drilling is also affected by feed rate. As feedrate increased beyond 50 mm/min, delamination and fractures were observed near the drill site.^30,43 According to previously reported studies regarding composite drilling, a feed rate of 5–45 mm/min has been chosen.^44–49

Table 1.

Input process parameters and their different levels.

Factors representation	Input parameter	Units	Levels
			−2	−1	0	1	2
RS	Rotational speed	rpm	550	1050	1550	2050	2550
FR	Feedrate	mm/min	5	10	25	35	45
AGS	Abrasive grit size	μm	100	180	260	340	420
UP	Ultrasonic power	%	20	40	60	80	100

Based on the physical foundation of the developed RUM setup, the range of ultrasonic power was determined. This drilling setup was designed to support ultrasonic power at 100%. Hence, the ultrasonic power range was set from 20 to 100%. Using the central composite design, the drilling experimentation was conducted after selecting the range of input process parameters.

Experimentation in the current investigation was planned to ascertain the influence of control factors of RUD on temperature elevations. Design of experiments (DOE) is one of the essential steps in an exploratory study involving statistical analysis. Different design methodologies such as Taguchi methodology, orthogonal arrays, response surface methodology (RSM) etc. Are widely used in experimental research for the minimization of experiments and to draw valid conclusions from experimentation.

Taguchi technique is generally limited to the exploration of 1^st order effects. So, RSM was chosen to achieve the 2^nd-order mathematical model of cutting temperature for RUD of PEC. ⁵⁰ The central composite design of RSM is employed for the design of experiments. The general response surface equation is given by equation (1).

$\mathrm{X}={\mathrm{\alpha }}_0+{\displaystyle \sum }_{\mathrm{i}=1}^{\mathrm{k}}{\mathrm{\alpha }}_{\mathrm{i}}{\mathrm{A}}_{\mathrm{i}}+{\displaystyle \sum }_{\mathrm{i}=1}^{\mathrm{k}}{\mathrm{\alpha }}_{\mathrm{i}\mathrm{i}}{\mathrm{A}}_{\mathrm{i}}^2+{\displaystyle \sum }{\displaystyle \sum }_{\mathrm{i}<j}{{\mathrm{\alpha }}_{\mathrm{i}\mathrm{j}}\mathrm{A}}_{\mathrm{i}}{\mathrm{A}}_{\mathrm{j}}+\mathrm{\epsilon }$

(1)

Where X means output performance measure, k denotes the number of machining input parameters, α₀, α_i, α_ii and α_ij are constant coefficients. A_i, ${\mathrm{A}}_{\mathrm{i}}^2$ , and A_iA_j denotes the 1^st order, 2^nd order and the correlation terms of the output response, respectively. ε, describe the random error during the experimentation. For four factors at five levels, 31 experiments need to be conducted. A total of 31 experiments were performed using the RUD experimental setup. The plan included 16 factorial runs, 8 axial runs, and 7 central points, as shown in Table 2. The RUD experiments were performed in the run order as presented in Table 2. The corresponding values of output responses of cutting temperature are presented in Table 3 against each experiment run as specified in Table 2.

Table 2.

Run order to carry out the experiments.

Run order	RS (rpm)	FR (mm/min)	AGS (μm)	UP (%)
1	2050	35	180	80
2	1050	35	340	40
3	1050	15	180	80
4	1550	25	260	60
5	1550	25	260	60
6	1050	15	340	80
7	1050	35	180	80
8	1050	35	340	80
9	2050	15	180	80
10	1050	15	340	40
11	1550	25	260	100
12	1550	25	420	60
13	1550	5	260	60
14	1050	35	180	40
15	1550	25	260	60
16	1550	25	100	60
17	1550	25	260	60
18	1550	25	260	60
19	1550	25	260	60
20	2050	15	340	80
21	1550	45	260	60
22	2050	35	180	40
23	2050	35	340	80
24	1550	25	260	60
25	550	25	260	60
26	2050	15	180	40
27	2050	15	340	40
28	2550	25	260	60
29	1550	25	260	20
30	1050	15	180	40
31	2050	35	340	40

Table 3.

Summarized data of temperature at different locations of thermocouples.

Run order	Temperature (in °C)
	1st at 1 mm			2nd at 2 mm			3rd at 3 mm
	T i	T f	ΔT	T i	T f	ΔT	T i	T f	ΔT
1	22.1	62	39.9	22.1	60.5	38.3	22.1	58	35.9
2	22	63.7	41.7	22	62.2	40.3	22	59.8	37.8
3	22.2	46.4	24.2	22.2	44.6	22.4	22.2	42	19.8
4	22	62.7	40.7	22	61.2	39.2	22	58.5	36.5
5	22	62.6	40.6	22	61.4	39.4	22	58.6	36.6
6	22	54.4	32.4	22	52.9	30.9	22	50.4	28.4
7	22.1	55.2	33.1	22.1	53.7	31.6	22.1	51.2	29.1
8	21.9	61.8	39.9	21.9	60.3	38.4	21.9	57.7	35.8
9	22	57.8	35.8	22	56.3	34.3	22	54	32
10	22	58.2	36.2	22	56.7	34.7	22	54.5	32.5
11	22.2	60	37.8	22.2	58.5	36.3	22.2	55.9	33.7
12	22	72.7	50.7	22	71.2	49.2	22	68.5	46.5
13	22	55.9	33.9	22	54.4	32.4	22	51.6	29.6
14	22	56.9	34.9	22	55.3	33.3	22	52.6	30.6
15	22.2	61.7	39.5	22.2	60.2	38	22.2	57.5	35.3
16	22	52.1	30.1	22	50.6	28.6	22	48	26
17	22.1	60.8	38.7	22.1	59.3	37.2	22.1	56.7	34.6
18	22	60.5	38.5	22	59	37	22	56.6	34.6
19	22	60.2	38.2	22	58.9	36.9	22	56.4	34.4
20	22	70.5	48.5	22	68.6	46.6	22	65.9	43.9
21	22.1	67.2	45.1	22.1	65.7	43.6	22.1	63.5	41.4
22	22	64.8	42.8	22	63	41	22	60.2	38.2
23	22	73.3	51.3	22	71.8	49.8	22	69.6	47.6
24	22	62.3	40.3	22	60.8	38.8	22	58.1	36.1
25	22	49.4	27.4	22	47.8	25.8	22	45.2	23.2
26	22.2	58.8	36.6	22.2	57.3	35.1	22.2	54.8	32.6
27	22	68.4	46.4	22	66.9	44.9	22	64.7	42.7
28	22	68.7	46.7	22	67	45	22	64.3	42.3
29	22.1	66.4	44.3	22.1	64.9	42.8	22.1	62.3	40.2
30	22	47.2	25.2	22	45.7	23.7	22	43.3	21.3
31	22.2	75.1	52.9	22.2	73.7	51.5	22.2	71.2	49

T _in : Initial temperature, T _fin : Final temperature, ΔT: Change in temperature.

Results and discussion

The data described in Table 3 were analysed using the standard procedure given by response surface techniques. Analysis of variance was performed to observe the influence of parameters on the temperature. The mathematical modelling and data examination have been discussed below.

Mathematical modelling of temperature

The competence of the planned models for cutting temperature has been examined by analysis of variance. The F-ratio of the designed models was calculated from the analysis of variance and against the standard F-ratio for a 95% confidence level. The F-value of the cutting temperature of planned models from the ANOVA seems to be above the tabulated value of F at a 95% confidence interval and their lack of fit is also insignificant. Therefore, these designs qualify the eligibility for the model adequacy test. The predicted models of output parameter cutting temperature are highly complex. There are some non-significant terms also. These predicted models were upgraded by removing the terms that have a non-significant effect on the output parameters by employing backward elimination. The statistical pooled regression analysis of the new predicted models are presented in Table 4, Table 5, and Table 6, respectively, whereas the regression equations for cutting temperature are represented by equations (2) to (4).

Table 4.

Pooled ANOVA for ΔT_{1.0 mm}.

Source	DF	Adj SS	Adj MS	F-value	p-value	Remarks
Model	7	1517.36	216.766	145.94	0.000	F0.05,7,23 = 2.44 F > F0.05,7,23 Hence, the model is adequate
Linear	4	1484.22	371.054	249.82	0.000
RS	1	653.13	653.127	439.73	0.000
FR	1	225.71	225.707	151.96	0.000
AGS	1	580.17	580.167	390.61	0.000
UP	1	25.22	25.215	16.98	0.000
Square	1	16.31	16.307	10.98	0.003
RS*RS	1	16.31	16.307	10.98	0.003
2-way interaction	2	16.84	8.420	5.67	0.010
RS*FR	1	9.00	9.000	6.06	0.022
RS*AGS	1	7.84	7.840	5.28	0.031
Error	23	34.16	1.485	—	—	F0.05,17,23 = 2.09 Flack of fit < F0.05,17,23 Lack of fit is insignificant
Lack-of-fit	17	27.54	1.620	1.47	0.333
Pure error	6	6.62	1.103	—	—
Total	30	1551.52	—	—	—
R-Sq = 97.80%		R-Sq (pred) = 95.70%			R-Sq (adj) = 97.13%

Table 5.

Pooled ANOVA for ΔT_{2.0 mm}.

Source	DF	Adj SS	Adj MS	F-value	p-value	Remarks
Model	7	1522.31	217.473	153.97	0.000	F0.05,7,23 = 2.44 F > F0.05,7,23 Hence, the model is adequate
Linear	4	1487.27	371.819	263.25	0.000
RS	1	648.96	648.960	459.46	0.000
FR	1	228.17	228.167	161.54	0.000
AGS	1	584.11	584.107	413.55	0.000
UP	1	26.04	26.042	18.44	0.000
Square	1	18.75	18.746	13.27	0.001
RS*RS	1	18.75	18.746	13.27	0.001
2-way interaction	2	16.29	8.145	5.77	0.009
RS*FR	1	9.00	9.000	6.37	0.019
RS*AGS	1	7.29	7.290	5.16	0.033
Error	23	32.49	1.412	—	—	F0.05,17,23 = 2.09 Flack of fit < F0.05,17,23 Lack of fit is insignificant
Lack-of-fit	17	25.63	1.508	1.32	0.388
Pure error	6	6.85	1.142	—	—
Total	30	1554.80	—	—	—
R-Sq = 97.91%		R-Sq (pred) = 95.92%			R-Sq (adj) = 97.27%

Table 6.

Pooled ANOVA for ΔT_{3.0 mm}.

Source	DF	Adj SS	Adj MS	F-value	p-value	Remarks
Model	7	1532.50	218.928	174.52	0.000	F0.05,7,23 = 2.44 F > F0.05,7,23 Hence, the model is adequate
Linear	4	1498.09	374.522	298.55	0.000
RS	1	648.96	648.960	517.32	0.000
FR	1	230.64	230.640	183.85	0.000
AGS	1	592.03	592.027	471.93	0.000
UP	1	26.46	26.460	21.09	0.000
Square	1	18.42	18.418	14.68	0.001
RS*RS	1	18.42	18.418	14.68	0.001
2-way interaction	2	15.99	7.996	6.37	0.006
RS*FR	1	8.70	8.702	6.94	0.015
RS*AGS	1	7.29	7.290	5.81	0.024
Error	23	28.85	1.254	—	—	F0.05,17,23 = 2.09 Flack of fit < F0.05,17,23 Lack of fit is insignificant
Lack-of-fit	17	23.44	1.379	1.53	0.314
Pure error	6	5.42	0.903	—	—
Total	30	1561.35	—	—	—
R-Sq = 98.15%		R-Sq (pred) = 96.32%			R-Sq (adj) = 97.59%

${\mathrm{\Delta }T}_{1.0\hspace{0.17em}\mathrm{m}\mathrm{m}\hspace{0.17em}}\left(°\mathrm{C}\right)=-3.00+0.01890\mathrm{R}\mathrm{S}+0.5399\mathrm{F}\mathrm{R}+0.0342\mathrm{A}\mathrm{G}\mathrm{S}-0.0513\mathrm{U}\mathrm{P}-0.000003\mathrm{R}\mathrm{S}\times \mathrm{R}\mathrm{S}-0.000150\hspace{0.17em}\mathrm{R}\mathrm{S}\times \mathrm{F}\mathrm{R}+0.000018\hspace{0.17em}\mathrm{R}\mathrm{S}\times \mathrm{A}\mathrm{G}\mathrm{S}$

(2)

${\mathrm{\Delta }T}_{2.0\hspace{0.17em}\mathrm{m}\mathrm{m}}\left(°\mathrm{C}\right)=-5.25+0.01969\mathrm{R}\mathrm{S}+0.5416\mathrm{F}\mathrm{R}+0.0354\mathrm{A}\mathrm{G}\mathrm{S}-0.0521\mathrm{U}\mathrm{P}-0.000003\mathrm{R}\mathrm{S}\times \mathrm{R}\mathrm{S}-0.000150\mathrm{R}\mathrm{S}\times \mathrm{F}\mathrm{R}+0.000017\mathrm{R}\mathrm{S}\times \mathrm{A}\mathrm{G}\mathrm{S}$

(3)

${\mathrm{\Delta }T}_{3.0\hspace{0.17em}\mathrm{m}\mathrm{m}\hspace{0.17em}}\left(°\mathrm{C}\right)=-7.76+0.01954\mathrm{R}\mathrm{S}+0.5394\mathrm{F}\mathrm{R}+0.0358\mathrm{A}\mathrm{G}\mathrm{S}-0.0525\mathrm{U}\mathrm{P}-0.000003\mathrm{R}\mathrm{S}\times \mathrm{R}\mathrm{S}-0.000147\mathrm{R}\mathrm{S}\times \mathrm{F}\mathrm{R}+0.000017\mathrm{R}\mathrm{S}\times \mathrm{A}\mathrm{G}\mathrm{S}$

(4)

Adequacy of the developed temperature models

The adequacy and lack of fit of the proposed statistical models were checked and verified using the F-values from the ANOVA. F-values corresponding to the models are 145.94, 153.97 and 174.52, respectively, for cutting temperature at locations 1.0 mm, 2.0 mm and 3.0 mm from the centre of the crater. These measured F-values are greater than tabulated F- values given in the ANOVA Table corresponding to F_{0.05, 7, 23} = 2.44, which shows that models are adequate. Similarly, F- values for lack of fit of the proposed models are 1.47, 1.32 and 1.53 for cutting temperatures. The measured F-values are less than the tabulated F- values given in the ANOVA Table 4 corresponding to F_{0.05, 17, 23 =} 2.09, which also shows that the lack of fit of proposed models is insignificant. Corresponding R² values are also given in Table 5 for cutting temperature. Table 6 reveal that adjusted (R²) are reasonable and in agreement with (R²) predicted for all three cases. This authenticates the adequacy and reliability of the proposed regression models to predict temperatures. Thus, the proposed statistical equations can be used to predict the cutting temperature within the limit of parameter ranges as listed in Table 1.

Precision of statistical models of temperature

A significant small irregular error is generated during experimental work and regression analysis. Therefore, it is necessary to check and validate the accuracy of the proposed models as represented by equations (2) to (4). The accuracy of these models is determined by equation (5).

$\mathrm{e}\mathrm{r}\delta \left(\mathrm{T}\mathrm{e}\mathrm{m}\mathrm{p}\right)=\pm \left({\mathrm{t}}_{\frac{\mathrm{\alpha }}{2},\mathrm{D}\mathrm{F}\hspace{0.17em}}\times \sqrt{{\mathrm{V}}_{\mathrm{e}}}\right)$

(5)

Where: δ = error value, t = t value obtained from the regression analysis, α = confidence interval, DF = degree of freedom for the proposed model, V_e = Variance values

The variance gives the calculated error interval from equations 6.7 to 6.9 are ±2.5°C, ± 2.4°C and ± 2.3°C, respectively for temperature. Verification tests were carried out to validate the predicted models given by equations (2) to (4). To validate the predicted model and corresponding output responses, the considered input values are shown in Table 7 and Table 8. Table 8 indicates the results of confirmatory experiments and the related errors obtained in the prediction of outcomes. Table 8 reveals that the value of temperature obtained at 1.0 mm, 2.0 mm, and 3.0 mm from the centre of drilled holes during confirmatory experiments lies in the range suggested by statistical models. Hence, these statistical models can effectively predict the temperature at any point in the fabricated composite after drilling.

Table 7.

Run order and the corresponding value of input variables to carry out the confirmation experiments.

Trial no.	Process parameters
	RS (rpm)	F (mm/min)	AGS (mm)	UP (%)
1	600	10	100	40
2	1800	40	260	60
3	1200	20	340	20
4	2400	40	180	80

Table 8.

Summarized data of confirmation tests.

Change in temperature in (°C)
1st at 1.0 mm			2nd at 2.0 mm			3rd at 3.0 mm
From model (Eqn2)	Optimized values range	From exp	From model (Eqn3)	Optimized values range	From exp	From model (Eqn2)	Optimized values range	From exp
13.7 ± 2.5	11.2–16.2	15.1	12.4 ± 2.4	10–14.8	13.6	9.9 ± 2.3	7.6–12.2	11.1
45.6 ± 2.5	43.1–48.1	46.8	44.7 ± 2.4	42.3–47.1	44.2	42.2 ± 2.3	39.9–44.5	42.6
41.5 ± 2.5	39–44	40.3	38.9 ± 2.4	36.5–41.3	38.8	36.4 ± 2.3	34.1–38.7	36.2
42.6 ± 2.5	40.1–45.1	43.4	40.4 ± 2.4	38–42.8	41.6	37.7 ± 2.3	35.4–40	38.8

Influence of RUD control factors on temperature

The effect of machining control factors on the temperature change of the workpiece is discussed in this section. Figure 3 demonstrates the main effect plots of the effects of drilling control factors on change in temperature at a fixed position of thermocouples: (a) at 1.0 mm, (b) at 2.0 mm, and (c) at 3.0 mm from the centre of the drilled crater. A similar trend of variation was seen for change in temperature of thermocouples located at three different places (Figures 3(a)–(c)) corresponding to various control factors. The percentage contribution of control factors on the cutting temperature regarding thermocouples located at different places has been demonstrated in Figure 4.

Figure 3.

Influence of drilling control factors on cutting temperature at a distance of (a) 1.0 mm, (b) 2.0 mm and (c) 3.0 mm.

Figure 4.

Percentage contribution of control factors on cutting temperature at a distance of (a) 1.0 mm, (b) 2.0 mm, and (c) 3.0 mm from the centre of the drilled hole.

Rotational speed vs change in temperature

Figure 5 reveals that cutting temperature increased with the change in rotational speed from 550 r/min to 2550 r/min when other control factors are held constant. Because of friction and shear forces between the tool and the workpiece, cutting temperatures increase as spindle speed increases. ⁵¹ The analysis determined that the cutting temperature at a 1.0 mm distance from the thermocouple was 26.9°C at 550 r/min and 47.1°C at 2550 r/min. Figure 5 also demonstrated that temperature change attained near the boundary between the workpiece and tool, i.e. thermocouple, which was located at 1.0 mm, 2.0 mm, and 3.0 mm distance from the drilling crater surface. Gradually, the cutting temperature decreased as the distance between the thermocouple and the drilling crater surface increased. These effects match the results reported by previous studies of the RUD ³⁰ and RUBD.^36,38

Figure 5.

Influence of rotational speed on change in temperature of thermocouples placed at three different positions from the drill hole. [FR = 25 mm/min, AGS = 260 μm and UP = 60%].

Feed rate vs change in temperature

Figure 6 reveals that cutting temperature is strongly affected by feed rate. Cutting temperature is increased linearly with an increase in feed rate from 5 mm/min–to 45 mm/min. An increase in feed rate presents worse material to the cutting tool per unit of time. More frictional forces exerted on the tool lead to more heat transfer to the workpiece, leading to higher temperatures on the drilled hole. ⁵² These effects match the outcomes presented in their investigational study on standard drilling and ultrasonic drilling. ⁵³

Figure 6.

Influence of feed rate on change in temperature of thermocouples placed at three different positions from the drill centre. [RS = 1550 r/min, AGS = 260 μm and UP = 60%].

Abrasive grit size vs change in temperature

The influence of the grit size on cutting temperature is also examined for RUD. Figure 7 illustrates that the cutting temperature increased with the abrasive average grit size from 100 μm to 420 μm. This is attributed to an increase in the contact surface between the tool and workpiece by the rise in abrasive grit size. More frictional heat is generated, thereby increasing the temperature at the workpiece.

Figure 7.

Influence of abrasive average grit size on change in temperature of thermocouples placed at three different positions from the drill centre. [RS = 1550 r/min, FR = 25 mm/min and UP = 60%].

Ultrasonic power versus change in temperature

Figure 8 reveals that cutting temperature significantly decreases with an increase in ultrasonic power from 20% to 80%. This is because, in RUD, there is an irregular contact between the workpiece and the drill tool. The tool-workpiece contact ratio is reduced with an increase in ultrasonic vibrational power. ³⁶ This effectively reduces the interaction of the drill with the workpiece per unit of time. So, less material is removed per unit of time from the workpiece, which lowers the heat.

Figure 8.

Influence of ultrasonic power on change in temperature of thermocouples placed at three different locations from the centre of dill. [RS = 1550 r/min, FR = 25 mm/min and AGS = 260 mm].

Interaction effects contributing to change in temperature

It was observed from equations (2) to (4) that significant interaction effects play an essential role in their impact on change in temperature. These interaction effects involve the interaction effect of RS and FR on temperature and the interaction effect of AGS and RS on temperature. These significant interactions of input factors on the cutting temperature of the thermocouple placed at a 1.0 mm distance have been plotted by using equation (2) and shown in Figures 9 and Figure 10. In line with the main effect plots, the trend of interaction plots of temperature readings at 2.0 mm and 3.0 mm from the centre of drilled hole exhibit the same trend as illustrated in Figures 9 and Figure 10.

Figure 9.

Interaction effect plot of RS and FR vs temperature (a) 3-D response surface plot (b) 2-D plot. [AGS = 260 μm and UP = 60%].

Figure 10.

Interaction effect plot of RS and AGS vs temperature (a) 3-D response surface plot (b) 2-D plot. [FR = 25 mm/min and UP = 60%].

Figure 9 presents the interaction plot between the feed rate and rotational speed. It can be noted from Figure 9 that temperature increased with an increase in the feed rate and rotational speed. This is due to the rise in MRR coupled with the generation of more frictional forces at higher MRR.

The interaction plot between the abrasive average grit size and rotational speed is shown in Figure 10. It can be noted from Figure 10 that temperature increased with an increase in the grit size and rotational speed of the tool. An increase in grit size leads to an increase in the contact area between the tool and the workpiece. By this, more heat is generated due to an increase in frictional forces. Further, this effect becomes more pronounced at higher rpm because of more interaction between the tool and workpiece per unit of time.

Optimization of input variables for change in temperature

The desirable approach suggested by response surface methodology is used to carry out single objective optimization using MINITAB software. Using the desirability approach, optimization is carried out to minimise the temperature value for the RUD of PEC. Table 9 illustrates the constraints for optimising temperature. The predicted values of the response at the optimal set of parameters are given in Table 10 for three locations. Confirmatory experiments are carried out to check the validity of optimisation results and data of observations, along with the error presented in Table 11. Table 11 indicates that the error band suggested by MINITAB in the optimized temperature value is ±2.5 (at 1.0 mm). It permits the variation of the predicted optimal temperature value from 4.5°C to 9.5°C and so on for locations at 2.0 mm and 3.0 mm from the centre of the drilled hole. During the conduct of the verification experiment at the optimal set of parameters, the temperature value obtained is 7.3°C which lies in the permitted range of temperature variation. As the error in the prediction of temperature is approximately 4.1% [(7.3–7)/(7.3) = 4.1%], this confirms better reproducibility of the results.

Table 9.

Range of control factors and weights.

Process parameter/output response	Goal	Lower Bound	Upper Bound	Lower weight	Upper weight
RS (rpm)	In range	550	2550	1	1
FR (mm/min)	In range	5	45	1	1
AGS (μm)	In range	100	420	1	1
UP (%)	In range	20	100	1	1
Temperature (°C)	Minimize	24.2	52.9	1	1

Table 10.

The optimized value of the drilling control factors for temperature.

RS (rpm)	FR (mm/min)	AGS (μm)	UP (%)	From model (Eqn2)	From model (Eqn3)	From model (Eqn4)
550	5	100	100	7 ± 2.5	6.3 ± 2.4	5.7 ± 2.3

Table 11.

Output value of temperature related to optimized machining input factors.

Temperature (°C)
1st at 1.0 mm
From model (equation (2))	Optimized values range	From experiments	Error
7 ± 2.5	4.5–9.5	7.3	4.1%

2nd at 2.0 mm
From model (equation (3))	Optimized values range	From experiments	Error
6.3 ± 2.4	3.9–8.7	6.5	3.07%

3rd at 3.0 mm
From model (equation 4))	Optimized values range	From experiments	Error
5.7 ± 2.3	3.4–8.0	6.0	5%

Micromorphology at the drilled edge site

With the optimized drilling parameters presented in Table 10, conventional and rotary ultrasonic-assisted drilling of CFRP composite was performed to observe micromorphology at the drill site. During conventional drilling, no ultrasonic power with a conventional twisted drill bit was used. Scanning electron microscopy (SEM) is used to monitor the effect of various drilling methods on polyetherimide composite surfaces. Different micromorphology and heat-accumulated zones were observed at the drill edge site by drilling techniques as presented by SEM micrographs in Figure 11. With the conventional drilling of CFRP composite, a heat-accumulated zone or thermally damaged composite surface was observed as illustrated in Figure 11(a). With the microstructure of the drill edge site, fiber pullout with a delaminated zone was witnessed during the conventional drilling of composite material. This is due to continuous drilling that accumulates heat at the drill edge site which leads to damage to the drilled hole surface. The delamination and fiber pullout that occurs in polyetherimide composites may reject the application of composite material for aerospace and automotive application. Whereas with the rotary ultrasonic-assisted drilling of CFRP composite, no delamination and fiber pullout was witnessed as depicted in Figure 11(b). Due to non-continuous and intermittent contact between the tool and the workpiece surface, heat accumulation at the drill edge site can be minimized. With the ultrasonic vibrations provided during the drilling of composite, thermal damage and heat-affected zone can be mitigated to improve the surface morphology and microstructure of fiber-reinforced composite material.

Figure 11.

SEM micrographs at the drill edge site during (a) conventional drilling, and (b) rotary ultrasonic-assisted drilling of CFRP composite.

Conclusions

The present investigation is focused on reducing the thermal damage, heat-accumulated defects, temperature elevations and delamination during the drilling of carbon fiber-reinforced polyetherimide (CFRP) composite. The composite material was fabricated using the hand-laying technique and compacted with a power press. For reducing the temperature rise at the drill site, non-conventional advanced machining of rotary ultrasonic drilling (RUD) is leveraged. The influence of various drilling parameters such as rotational speed, feed rate, abrasive grit size, and ultrasonic power was observed on the temperature elevations. The diamond-impregnated hollow drill tool with a RUD setup provides intermittent contact between the composite material and the drilling tool that reduces the temperature elevation at the drill site. It was observed that temperature rises with an increase in rotational speed, feedrate, and grit size and reduces with an increase in ultrasonic power. Rotational speed was the most significant parameter with the highest contribution of 42% that affects the cutting temperature during the drilling of CFRP. The thermal damage, temperature elevations, defects, and delamination during the drilling of composite material can be reduced by leveraging the rotary ultrasonic-assisted drilling of fiber-reinforced composites.