Experimental Study on Ultimate Tensile Strength and Impact Energy of Al-2024 Friction Stir-Welded Joints ^†

Abstract

This paper focuses on the multi-objective optimization of friction stir welding process parameters. Three input variables, including the axial load (AL), tool rotation speed (RS), and tool tilt angle (TA), were selected to optimize the mechanical characteristics of Al-2024 friction stir-welded (FSW) joints. The ultimate tensile strength (UTS) and impact energy (IE) were selected as output responses to measure the mechanical characteristics of Al-2024 FSW joints. A total of nine experiments, using the L9 orthogonal array as part of the Taguchi method, were performed to determine the significance of the process parameters. Gray relational analysis (GRA) was employed to conduct the multi-objective optimization of these combinations of process parameters. The results of the analysis of variance (ANOVA) showed that the AL has the most significant effect on the UTS and IE of Al-2024 FSW joints, followed by the TA and RS. The Taguchi-based GRA analysis revealed that an AL of 10 KN, a TA of 2 degrees, and an RS of 1500 rpm resulted in an optimal UTS of 333.06 MPa and an IE of 40.62 Joules. In these optimal experimental settings, optical microscopy analysis revealed the presence of a recrystallized fine-grain structure in the heat-affected zone of the welded region.

1. Introduction

Al-2024 has vast applications in the automotive, aerospace, and railway industries, due to its high formability, good fatigue properties, and high stiffness [1]. Conventional and non-conventional welding and squeeze overcasting methods can join multiple parts in complex structures [2]. Friction stir welding has the potential to replace other joining methods for aluminum alloys because of its energy-efficient process and environmental benefits, as it eliminates the need for shielding gas or flux materials [3]. Moreover, friction stir welding is utilized to join similar and dissimilar Al alloy welds, due to its compatibility with dissimilar material, which includes the differences in melting temperature that conventional welding processes require regarding most Al joints. In the friction stir welding process, a spinning tool generates heat through friction as it moves along the joint. This heat plasticizes the material without melting it, creating a softened flow around the tool. As the pin of the tool rotates and traverses the joint, it extrudes the material, forming a friction stir processing zone and effectively joining the aluminum alloys [4].

Experimental research has established that input variables, such as the feed rate (Fr), tool tilt angle (TA), welding speed (WS), tool material (TM), tool rotation speed (RS), and axial force (AL), significantly influence the mechanical characteristics of FSW joints. According to Ghosh et al. [4], the RS and WS have a significant impact on the micro-hardness (MH) and UTS of AA6061/AA356 FSW joints. Furthermore, the author stated that residual stresses reduce at an RS of 1200 rpm and a WS of 0.50200 mm/min, which refines the grain structure within the interface area and contributes to improving the strength of welded joints. Mehdi et al. [1] claimed that the RS has the more significant effect as compared to the WS on the MH and UTS of Al-60661/Al-2014 FSW joints. Another study by Ahmed et al. [2] determined that the UTS and MH of AA7075-T6/AA5083-H111 FSW joints improved by increasing the WS from 50 to 200 mm/min at a TA of 2 degrees and an RS of 300 rpm. Samuel et al. [5] employed the Taguchi-based gray relational approach to examine the effect of the tool profile, RS, and AL, on the UTS and MH of an FSW Al-7075 reinforced, activated carbon, composite sheet. The results demonstrated that the AL had the most prominent impact on the UTS and MH of FSW joints, followed by the RS and tool profile. Elangovan et al. [6] employed parameters including the tool profile, RS, WS, and AL to study A-2219 FSW joints. The results revealed that the square pin profile, an RS of 1600 rpm, a WS of 0.75 mm/s, and an AL of 12 KN, developed a defect-free weld region and yielded a maximum tensile strength of 249 MPa. Meshram and Reddy [7] examined the impact of the TA on the quality of Al-2219 welded joints. The results showed that a TA of 1 to 2 degrees reduced the tool torque, increased the welding temperature, and improved the flow of material, which filled the voids and improved the weld quality. Mahany et al. [8] observed the influence of the RS and the AL on the mechanical properties of Al-2024 and Al-7075 FSW butt joints. The results indicated that both the RS and the AL contributed directly to refining the grain size, leading to improved mechanical properties. Haribalaji et al. [3] employed the L9 Taguchi design to optimize the friction stir welding process parameters for dissimilar joints made of Al-7075 and Al-2024. The RS, WS, and AL were selected as the process parameters, while the UTS was taken as a response parameter. The results indicated that the AL has the most significant effect on the UTS, followed by the WS and the RS. Rajendran et al. [9] concluded that Al-2014-T6 FSW lap joints have 14.42 KN of shear strength and 84% joint efficiency, with a TA of 2 degrees, an RS of 900 rpm, and a WS of 90 mm/min.

A comprehensive literature review revealed that there is no evidence of multi-objective process parameter optimization for impact energy (IE) related to Al-2024 FSW joints. Furthermore, it has been noted that only a few studies have discussed the multi-objective optimization of process variables, such as the TA, AL, and RS, regarding the UTS of Al-2024 FSW joints. However, Jin et al. [10] stated that Al-2024 is difficult to weld using the friction stir welding process, because 3.79% by weight copper increases the possibility of corrosion attack, and they also highlighted that the stirring action generates excessive heat in the welding zone. Hence, it is challenging to perform the multi-objective optimization of friction stir welding process variables. Therefore, this research work aims to perform the multi-objective optimization of process variables, such as the TA, AL, and RS, regarding the UTS and IE of Al-2024 FSW joints. This research work employs the Taguchi-based gray relational analysis method. This research work significantly contributes to the development of the aerospace and automotive industries, by achieving high tensile and impact strength in Al-2024 FSW joints.

2. Materials and Method

An Al-2024 sheet, with dimensions of 100 × 80 × 5 mm, was selected for the experimentation process. An optical emission spectroscopy test was conducted to confirm the material composition of Al-2024, as demonstrated in

Table 1. Chemical composition of Al-2024 by weight.

Materials	Cu	Si	Cr	Fe	Mn	Ni	Mg	Zn	Ag	Cr	Ti	Al
Al-2024 (wt.%)	3.79	0.160	0.008	0.294	0.563	0.007	1.551	0.065	0.004	0.008	0.014	93.54

. Moreover, it has been observed that an RS of 1600 rpm, a WS of 0.75 mm/s, and an AL of 12 KN yield the maximum tensile strength in FSW joints [11]. Ahmed et al. [2] claimed that the mechanical strength of dissimilar Al FSW joints increased with an increment in the WS from 50 to 200 mm/min at an RS of 300 rpm, the use of a cylindrical pin, and a TA of 2 degrees. Rajendran et al. [9] similarly reported that they achieved the highest FSW joint efficiency and shear strength with a TA of 2 degrees, an RS of 900 rpm, and a WS of 90 mm/min. Therefore, information from these sources has been used for the selection of the process parameters and the corresponding three different levels, including the axial load (8, 10, and 12 KN), tool tilt angle (1, 2, and 3 degrees), and rotation speed (500, 1000, and 1500 rpm). To evaluate the effects in terms of the middle level of the process parameters, three levels for each process parameter were selected. Furthermore, cylindrical pins made of H13 tool steel, with a 2.6 ratio in terms of the shoulder diameter (D) to the pin diameter (d) and a WS of 200 mm/min were selected to perform the experimentation, because these parameters provide optimal results for FSW Al joints [12]. A vertical milling machine was used to perform the experiments, with a butt-welding configuration, as depicted in Figure 1a. The tool tilt angle is calculated based on the absence of physical contact between the workpiece and the tool shoulder, as shown in Figure 1b. The control panel of the vertical milling machine was used to control the tool’s rotational speed and the axial load.

3. Experiment Design and Setup

The Taguchi optimization technique is a distinctive and potent discipline in terms of optimization that enables the achievement of optimal results with a minimal number of experiments. Taguchi’s experimental design enhances the quality, minimizes the cost, and offers resilient design solutions [13]. Therefore, the Taguchi method was chosen for the experiments. With three levels for the three process parameters, the L9 orthogonal array is the most suitable choice, providing the highest accuracy and the lowest noise. It is also worth mentioning that the L9 orthogonal array for the three factors with the three levels provides the most accurate results with the lowest noise factors [14,15]. The Minitab 19.0 software was used for the experiment design and analysis of the process parameters. Moreover, each experiment was performed three times for both the UTS and IE to ensure the repeatability and the accuracy of the results.

Table 2. Evaluation of the responses as per experiment design.

Trial No.	Axial Load (KN)	Tool Tilt Angle (degree)	Tool Rotation Speed (rpm)	UTS (MPa)	Impact Energy (Joule)
1	8	1	500	290.18 ± 3.80	16.17 ± 1.42
2	8	2	1000	310.93 ± 4.72	28.13 ± 2.95
3	8	3	1500	240.14 ± 4.81	12.34 ± 2.98
4	10	1	1000	350.13 ± 2.48	35.43 ± 3.86
5	10	2	1500	333.06 ± 1.27	40.62 ± 3.06
6	10	3	500	200.52 ± 2.59	28.61 ± 3.09
7	12	1	1500	250.92 ± 2.39	21.41 ± 2.99
8	12	2	500	178.14 ± 3.30	30.04 ± 2.24
9	12	3	1000	155.78 ± 3.76	24.06 ± 2.45

exhibits the mean and standard deviation calculated from these experiments for each trial run. It is pertinent to mention that the ASTM E08 [16] and ASTM-E23-12c [17] standards were adopted to measure the UTS and impact energy of the welded joints, respectively. An electrical discharge machine (EDM) was used to extract samples from the welded plates. A computerized universal tensile tester conducted the UTS tests at an ambient temperature, with a strain rate of 5 × 10⁻³ mm/s. The specimens for the IE were 50 × 10 × 10 mm, with a V-notch (2 mm depth, 45° angle, 0.25 mm radius). Charpy machines measured the impact energy (IE) absorbed during the fracture. The IE observed for the specimen, due to the applied impact load, is calculated by Equation (1) [18] and illustrated in Figure 1c.

$${IE}_{observed}=mg\times R\times (cos{\theta }_2-cos{\theta }_1)$$

(1)

where m and R are the mass and radius of the fork, respectively, g is the gravitational acceleration, θ₁ is the initial reference angle, and θ₂ represents the angle at the end of the swing, and IE depicts the impact energy.

4. Results and Discussion

4.1. Statistical Analysis

This research aims to enhance the mechanical strength of Al-2024 FSW joints, using the larger-the-better objective function for the UTS and IE. The best UTS value (350 MPa) was achieved with an AL of 10 KN, a TA of 1 degree, and an RS of 1000 rpm, as shown in Table 2. The stress–strain curve that corresponds to the maximum value of the UTS is shown in Figure 2a. Similarly, the highest IE (40.62 J) was attained with an AL of 10 KN, a TA of 2 degrees, and an RS of 1500 rpm, as illustrated in Table 2. Moreover, the mean table has been developed to rank the most significant parameters for both responses, as shown in

Table 3. Mean response table for the UTS (MPa) and IE (J).

Mean Response Table for UTS (MPa)				Mean Response Table for Impact Energy (J)
Level	Axial Load	Tool Tilt Angle	Rotation Speed	Level	Axial Load	Tool Tilt Angle	Rotation Speed
1	150.3	291.1	118.2	1	18.67	24.00	24.67
2	290.9	149.0	147.8	2	34.33	32.67	29.00
3	103.9	105.0	260.0	3	25.00	21.33	24.33
Delta	56.9	56.1	30.7	Delta	15.67	11.33	4.67
Rank	1	2	3	Rank	1	2	3

. From the mean table, it has been noticed that the AL has the most significant effect on the UTS and IE, respectively. Moreover, an analysis of variance (ANOVA) was executed for both output responses to determine the significance of the process parameters. From

Table 4. ANOVA table for the UTS (MPa).

Parameters	DF.	Seq. SS.	Adj. SS.	Ad. MS.	F	P	Percentage Contribution (%)
AL (KN)	2	17,428.7	17,428.7	8714.34	368.21	0.003	45.39
TA (degree)	2	15,847.2	15,847.2	7923.62	334.80	0.003	41.27
RS (rpm)	2	5118.7	5118.7	2559.36	108.14	0.009	13.33
Residual error	2	47.3	47.3	23.67
Total	8	38,442.0
Model summary: R-squared (adj.) = 99.83%, R-squared (96.96%), and S = 1.612

and

Table 5. ANOVA table for the IE (J).

Parameters	DF.	Seq. SS.	Adj. SS.	Adj. MS.	F	P	Percentage Contribution (%)
AL	2	372.667	372.667	186.333	186.33	0.005	59.72
TA	2	210.667	210.667	105.333	105.33	0.009	33.76
RS	2	40.667	40.667	20.333	20.33	0.047	6.52
Residual error	2	2.000	2.000	1.000
Total	8	626.000
Model summary: R-squared (adj.) = 98.72%, R-squared (99.68%), and S = 1.00

, it can be noted that all the process parameters have a significant impact on the UTS and IE, respectively, because the p-values are less than 0.05 for both responses. Moreover, the R-squared (96.96%) and adjusted R-squared (99.83%) of the UTS model summary are remarkably close to 1, indicating the accuracy of the developed model [19,20,21]. Similarly, an R-squared of 99.68% and an adjusted R-squared of 98.72 for the IE response signify the accuracy of the results. It is worth mentioning that these variables play a key role in determining the accuracy of the model, as also reported by other researchers [9]. It can be further noted from the ANOVA results in Table 4 for the UTS that the AL has the most prominent effect, followed by the TA and RS on the UTS due to these parameters having the highest F-value (368.21) as compared to other responses. Similarly, it has been observed that the AL has the highest significance, followed by the TA and RS on the IE of Al-2024 FSW joints, as shown in Table 5. These results are also evident from the computation of the percentage contribution, because the AL’s contribution of 45.39% and 59.72% are the leading parameters regarding the UTS and IE, respectively.

4.2. Analysis of the Main Effect Plots

To explore the trend in the process variables with respect to the output responses, main effect plots were developed, as depicted in Figure 2b,c, respectively. Figure 2b reveals an increase in the UTS when the AL changes from 8 to 10 KN, followed by a decrease when the AL changes from 10 to 12 KN. However, it was observed that a sharp increase in the UTS occurred when the RS increased from 500 to 1000 rpm, while the increase in the UTS with the change in the RS from 1000 to 1500 rpm was relatively less significant. Contrarily, the UTS sharply decreases with an increase in the TA from 1 to 3 degrees, as shown in Figure 2b, respectively. The increase in the welding material flow strain occurs as the AL and RS increase to 10 KN and 1000 rpm, respectively, generating appropriate heat energy in the welded region. Eventually, the appropriate mixing of both the Al plates occurred, which led to the creation of a recrystallized fine-grain structure at the interface region. The optical microscopy analysis was performed with experimental settings 4 to further confirm the results, as shown in Figure 3a. It is clear in the interface region that the formation of a recrystallized fine-grain structure has occurred, which leads to the development of compact and high-strength metallurgical bonding. The formation of a finer grain structure at the interface region of FSW joints is a sign of high-strength metallurgical bonding [3]. At 1 degree of TA, the plate establishes complete contact with the shoulder, facilitating the transfer of more material from the leading side to the trailing edge, thereby filling any voids and defects. The optical microscope analysis of specimens welded with experimental settings 4 confirmed the absence of any voids or inherent defects in the interface region, as illustrated in Figure 3a. Ahmed et al. [2] reported a similar trend, indicating that a decrease in the TA increment of the RS and at the middle level of the AL significantly boosts the strain rate of the FSW joints made of Al-7075 and Al-5083, thereby enhancing the weld joint quality and lowering the likelihood of defects. Therefore, previous studies validate the present study’s findings.

The main effects plot on the impact energy indicated that the IE increases with an increment in the AL from 8 to 10 KN. After that, it sharply decreases with the increase in the AL from 10 to 12 KN, as shown in Figure 2c. This is due to the significant increase in absorbed energy with the increase in the AL to 10 KN, which would make the fracture more ductile at room temperature and assist plastic deformation. However, further increments in the AL of up to 12 KN cause stress concentrations in the interface region, which increases the stresses at the heat-affected area of the Al weld. Eventually, the impact test revealed the initiation of cracks and inherent defects, which significantly reduced the toughness of the welded joint. Consequently, a lower IE was attained with an AL of 12 KN and a higher IE was attained with an AL of 10 KN. The optical microcopy analysis of the specimens prepared with experimental settings 4 also reveals that there were no cracks or inherent defects in the heat-affected areas of the weld joints, as shown in Figure 3a. Similar trends have been observed for the TA and TS parameters, respectively. It has been observed that the sharp increments in the IE with the increase in the TA and RS from 1 to 2 degrees and 500 to 1000 rpm, respectively, generate adequate heat that enable suitable mixing of both materials in the interface region, as demonstrated in Figure 2c. Substantially, this improved the capabilities of the welded specimen to deform and increased the observed energy during the impact test. This phenomenon significantly increases the dispersed efficiency of welded joints, leading to increased impact resistance. Resultantly, a higher magnitude of IE was observed with a TA of 2 degrees and an RS of 1000 rpm. In contrast, inadequate heat generated at higher and lower levels of TA and RS significantly reduces the ability to observe and deform energy during an impact test. At higher or lower levels of TA and TS, a lower magnitude of IE has been observed. It is worth mentioning that TA, RS, and AL significantly contributed to generating adequate heat in the welded region of the joints [2,3].

4.3. Analysis of the Interaction Plots

An interaction plot was developed to explore the simultaneous impact of two process parameters on UTS and IE. Therefore, the effect of three combinations of process parameters on both responses is analyzed in these sections, as shown in Figure 3b,c, respectively. It is important to highlight that the significance of the combination of process parameters in the interaction plot depends on the degree of nonparallel lines [19]. Therefore, it was observed that a combination of AL and TA has the most significant effects on the UTS and IE, respectively, due to the highest degree of nonparallel lines as compared to other combinations of process parameters. Conversely, other combinations of process parameters, such as the RS and TA, RS and AL, have the least effect on the UTS and IE, respectively, due to the relatively low degree of nonparallel lines in the plots.

5. Multi-Response Optimization

The gray relational analysis is used to perform multi-object optimization of the process parameters. It is a technique that derives from the single-objective optimization problem. The larger-the-better objective functions were selected for both responses, because a higher UTS and IE indicate a higher magnitude of responses [2]. The gray relational grades (GRGs) and the gray relational coefficient (GRC) were calculated using Equations (2) and (3) to achieve the normalized values and ranking. After that, Equation (4) was used to estimate the GRGs [12]. It is worth mentioning that a higher grade represents the most ideal combination of process parameters [5]. The GRGs are computed by Equation (2) [12]:

$$x_{ij}={\displaystyle \frac{y_{ij}-{min}_j{y}_{ij}}{{max}_j{y}_{ij}-{min}_j{y}_{ij}}}$$

(2)

In this equation, $y_{ij}$is the calculated response for the i value in the j experiment, x_ij is the normalized i response for the j experiment, ${min}_j$ is the minimum value of the j experiment, and ${max}_j$shows the maximum value of the j experiment, respectively.

Similarly, the GRC describes the relationship between the ideal output parameters and the normalized values. Equation (3) was used to calculate the GRC [12]:

$${\xi }_{ij}={\displaystyle \frac{{△}_{min}+{\varsigma △}_{max}}{{△}_{ij}+{\varsigma △}_{max}}}$$

(3)

In this equation, ${△}_{min}={min}_i\times {min}_j\left|x_i^0-x_{ij}\right|$, ${△}_{max}={max}_i\times {max}_j\left|x_i^0-x_{ij}\right|$, and ${△}_{ij}=\left|x_i^0-x_{ij}\right|$. Moreover, $x_{ij}$ indicates the normalized i response for j experiment, $x_i^0$ is the ideal value, ${\xi }_{ij}$ is the GRC for the i value in the j experiment, and $\varsigma$ is the identification coefficient, which lies between 0 to 1.

The GRG is the correlation level between the comparability and reference sequence. It is calculated by using Equation (4) [12]:

$${\sigma }_j={\displaystyle \frac{1}{m}}\sum _{i=1}^m{w}_i\times {\xi }_{ij}$$

(4)

In this equation, $w_i$ represents the weight factor and m indicates the number of output responses.

After conducting the GRA, it was found that the highest GRG value was achieved with experimental settings 5, as illustrated in

Table 6. GRA for UTS and IE.

Exp. Run	Normalized Values		Grey Relational Coefficients (GRCs)		Gray Relational Grade (GRG)	Rank
	UTS	IE (Joule)	UTS	IE (Joule)
1	0.308	0.865	0.618	0.366	0.492	4
2	0.202	0.442	0.713	0.531	0.622	3
3	0.566	1.000	0.469	0.333	0.401	8
4	0.000	0.184	1.000	0.732	0.866	2
5	0.088	0.000	0.851	1.000	0.925	1
6	0.770	0.425	0.394	0.541	0.467	5
7	0.510	0.679	0.495	0.424	0.459	7
8	0.885	0.374	0.361	0.572	0.467	6
9	1.000	0.586	0.333	0.461	0.397	9

. Therefore, the optimal IE (40.62 J) and UTS (333.06 MPa) were attained for experimental settings 5 (10 KN of AL, 2 degrees of TA, and 1500 rpm of RS). Furthermore, an ANOVA for the GRGs was developed to figure out the significance of the process parameters that are best suited to both the UTS and IE, as shown in

Table 7. GRGs ANOVA table.

Process Variable	DF.	Seq. SS.	Adj. SS.	Adj. MS.	F	P	Contribution (%)
AL	2	29.917	29.917	14.9584	25.07	0.038	0.51
TA	2	22.526	22.526	11.2629	18.87	0.050	0.38
RS	2	6.209	6.209	3.1045	5.20	0.161	0.11
Residual error	2	1.194	1.194	0.5968
Total	8	59.845
Model summary: R-squared (adj.) = 84.02%, R-squared = 96.05%, and S = 0.0788

. The ANOVA revealed that the AL is the most significant parameter for both the UTS and IE, respectively. The adequacy parameters, such as the R-squared (96.05%), are close to 1, whereas adjusted R-squared (84.02%) and standard deviation (0.0788) confirmed that the developed model has significant adequacy.

6. Confirmatory Test

A confirmation test was carried out to make sure that the output responses had a low level of uncertainty, using Equation (5) [9,12]. This equation has been used to validate the results of Taguchi methods by calculating the errors in the predicted and actual values of process parameters.

$$Y_{Calculatedvalue}=Y_{average}+(Y_{AL}-Y_{average})+(Y_{TA}-Y_{average})+(Y_{RS}-Y_{average})$$

(5)

By using Table 3, Y_AL, Y_TA, and Y_RS were taken as the maximum values for the AL (290.9), TA (291.1), and RS (260) for the UTS. Similarly, the AL (34.43), TA (32.67), and RS (29.00) were selected for the IE. Y_Average represents the average value of the mean of the UTS and IE (Joule), respectively. From Table 2, the average values for the UTS and IE were computed as 256.64 MPa and 26.31 J, respectively. In addition, the experimental results with the optimal experimental settings (trial no. 05) were considered for comparison.

Table 8. Experimental uncertainty analysis.

Output Responses	Calculated Value	Actual Value	Uncertainty (Error)
UTS (MPa)	328.711	333.06	4.349
IE (Joule)	43.475	40.62	2.855

depicts the calculated uncertainty for both values. Ramarao et al. [18] claimed that a calculated error of less than 5 was considered a sign of accuracy in terms of the Taguchi method’s results. Therefore, the confirmation test determined that the error was less than 5, indicating the accuracy of the results.

7. Conclusions

The study’s objective is to optimize the effects of the AL, RS, and TA on the UTS and IE of Al-2024 FSW joints. The Taguchi-based GRA method was used to conduct this research work. It has been determined that the optimal IE of 40.62 J and UTS of 333.06 MPa were achieved at an AL of 10 KN, a TA of 2 degrees, and an RS of 1500 rpm. The ANOVA results concluded that the AL has the most significant effect on the UTS and IE of Al-2024 FSW joints, followed by the TA and RS. The AL has a leading contribution of 45.39% and 59.72% in terms of the UTS and IE, respectively. The main effect plot depicts that the UTS increases with an increasing AL, TL, and RS of up to an AL of 10 KN, a TA of 2 degrees, and an RS of 100 rpm, and then decreases with an increment in the parameters to a higher level. Similarly, it has been determined that the IE increases with the increase in the process variables up to the middle level and then sharply decreases when it goes reaches higher levels because at the middle level of the process parameters, appropriate heat is generated at the welding zone that increases the ability to absorb and deform energy during an impact test. Resultantly, this increased the impact resistance and formed a recrystallized fine-grain structure in the interface region. Optical microscopy analysis confirmed that a recrystallized fine-grain structure and compact interface joint were formed with optimal experimental conditions. Additionally, the interaction plot depicts that the AL and TA have the most significant effects on the UTS and IE and the ANOVA for the GRGs indicated that the AL is the most significant parameter for both the UTS and IE.