Multi-response Optimization of GTAW Process Parameters in Terms of Energy Efficiency and Quality

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INTRODUCTION
Gas tungsten arc welding (GTAW) is a popular welding method that joins metals with a nonconsumable electrode.It involves heating the metal to produce a weld pool by creating an arc between the electrode and the specimen.A shielding gas keeps the weld area safe from airborne contaminants.GTAW produces high-quality welds, exact control, excellent weld penetration, minimal spatter, and a variety of metals.
Different GTAW operations have been considered and optimized to enhance the performances measured.For the GTAW Inconel 718 alloy, response surface method (RSM) models of the bead width (BW), depth of penetration (DP), heat-affected zone (HAZ), and area of the fusion zone were developed [1].The authors stated that weld bead properties were mostly impacted by the welding current (I) and torch speed (S), respectively.The BW, Brinell hardness (BH), and micro-hardness (MH) were improved for the welded Inconel 625 using the grey relational analysis and TOPSIS [2].The results presented that the optimal I, S, and arc gap (G) were 300 A, 90 mm/min, and 5 mm, respectively.In terms of the I, S, and G, the DP and BW models of the titanium joints were proposed [3].The authors claimed that to create TWB joints free of defects, the ideal parameters of 135 A, 4.1 mm/s, and 3 mm may be used.An aspect ratio of 0.421 and the ideal hardness of 262 HB of the welded Inconel 625 could be generated at I = 300 A, S = 75 mm/ min, and G = 1 mm using the TOPSIS [4].For the welded 5052 alloys, predictive models were provided in terms of the I, V, and S for the penetration shape factor, MH, and reinforcement form factor of the weld specimens [5].The optimal I, V, and S reported were 140 A, 18 V, and 300 mm/min, respectively.The BW and DP models of the welded AISI316L were proposed in terms of the I and S, in which the PSO was used to find the best ANN model [6].The small deviations (less than 4 %) indicated that the developed correlations were efficiently used in the GTAW operation.Wan et al. find that the tensile strength (TS) and elongation (EL) could be improved using the optimized weld shape [7]; the TS and EL of the welded 2219-T8 aluminium alloys were enhanced by 70 % and 4 %, respectively.Vijayakumar et al. [8] presented that the peak current of 50 A, inter-pulse current of 30 A, and inter-pulse frequency of 20 kHz could be used to improve the characteristics of the IP-TIG welded Ti6Al4V alloy.The RSM model of the joint strength was developed for the IN-718 weld by Sonar et al. [9], who found that the developed weld exhibited 32 % higher strength and superior corrosion resistance than TIG ones.The RSM models of the maximum yield strength and EL were developed for the IP-TIG welded Alloy 718 joints [10].The authors stated the yield strength and EL of the IP-TIG joint were 94.5 % and 82.9 % of base metal, respectively.The ANOVA is used to determine the optimal values of the GTAW mild steel plates [11].The maximum impact strength was obtained at the I of 158.605, a notch angle of 59°, and a single pass, respectively.A convolutional neural network was used to train the BW and DP models [12].The R 2 value of 0.998 indicated that the developed models could be used to control the quality in real time.The Taguchi and RSM were used to enhance the TS of the welded SS316L stainless steel pipes [13]; the optimal working cycle and peak current were 66.5 % and 114.7 A, respectively.Deep learning was proposed to predict the DP value [14].The low error level showed that the developed models can be utilized in the GTAW process.Pandya et al. [15] indicated that oxide flux increased the weld penetration and mechanical strength of welded 2205 duplex stainless steel.Moreover, the DP of 6.23 mm, TS of 775 MPa, and MH of 322 HV were obtained using the RSM.Similarly, Baskoro et al. presented that the DP and TS of the welded 304 stainless steel could be increased by 89.9 % and 17.2 %, respectively, with the SiO 2 flux [16].
However, the limitations of related works can be expressed as follows.
The mechanical and shape characteristics are frequently taken into account, while the heat input has not been discussed.Reducing the heat input will help the GTAW operate more energy-efficiently.
The RSM is widely used to propose performance models, while the application of the ANN has been rare.Moreover, the HI, TS, and MH models have not been developed for the GTAW Ti40A plates.
The impacts of GTAW process parameters on the HI, TS, and MH have not been analysed.
The optimal GTAW parameters have not been selected to improve the HI, TS, and MH simultaneously.
The Taguchi and RSM methods are highly likely to find local outcomes.Therefore, an efficient approach to finding global data is necessary.

OPTIMIZING FRAMEWORK
The HI is defined as a ratio of energy consumption per length and computed as: where I i , V i , S, and η are the instant current, instant voltage, torch speed, and thermal efficiency, respectively.
The TS is computed as: where TS i and n are the tensile strength of the i th sample and the number of samples, respectively.
The MH is computed as: where MH i and n are the micro-hardness of the i th location and the number of positions, respectively.Optimization approach for the GTAW Fig. 2 shows the optimization framework for the GTAW operation.
Step 2: The RBFN models of the outputs are developed regarding GTAW parameters [18].
The RBFN with Gaussian function is used to present the correlations between the inputs and outputs.The input parameters are combined into the hidden one, while the output for the given input (s) and vector (c i ) is expressed as: where || s i -c i || is the Euclidean distance between s and c i .The Gaussian function is expressed as: where γ is a parameter that is found using the crossvalidation stage.
The RBFN model for a given input s is expressed as: where w 0 and w m are the bias and weight, respectively.
Step 3: The WPCA is used to compute the weights.
The normalized response (n r ) is computed as: The correlative data (S jl ) is computed as: where Cov(R i (j) and Ri(l)) denote the covariance of sequences I i (j) and I i (l), respectively.Eigenvalues (λ k ) and eigenvectors (V ik ) are computed as: The major principal coefficient is computed as follows: Step 4: The ANSGA-II is used to find solutions.The adaptive crossover probability and adaptive mutation probability are used in the ANSGA-II to identify the best solutions.Fig. 3 illustrates how the ANSGA-II operates.

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Producing initial population X(0) and computing the function value f(x) for each individual.• The middle generation X'(t) is created by performing the adaptive crossover and mutation operators.
• When an individual's fitness exceeds the average, they can be passed on to the next generation, which lowers the crossover probability (p c ) and mutation probability (p m ).However, if an individual's fitness level is lower than the average, they may be removed, which would result in greater p c and p m levels.The crossover and mutation operations' equations are written as follows: • Creating a new non-dominate set P'(t) by joining the non-dominate solutions.• To create a new population X(t +1), randomly generate new individuals and join them to the non-dominated solutions.
Step 4: The EAMR is used to select the best optimality.
The positive solution is computed as: The negative solution is computed as: The performance indicator (I i ) is computed as: The best solution is selected with the highest I i .

EXPERIMENTAL FACILITIES
Using the WEDM method, the specimens are sliced from the Ti40A plates and then cleaned using carbide sheets.The measurements of each specimen are 3 mm in thickness, 64 mm in width, and 80 mm in length.
The anti-corrosion cover of the marine ship is mostly made of welded Ti40A plates.Each filler stick has the following measurements: 8 mm for length, 2 mm for breadth, and 3 mm for thickness.The chemical compositions of the Ti40A are shown in Table 2.All testing is conducted using a ZX7-200 welding machine and a designed fixture (Fig. 4).During the processing stage, two welded layers are produced to enhance the junction.Tensile and micro-hardness tests were conducted using the Exceed E45 and Wilson hardness machines, respectively.The instantaneous voltage and current are measured using the clamp meter known as PAC22.As shown in Fig. 7, the microstructure of the various welding locations, such as the base material (BM), heat-affected zone (HAZ), and welded zone (WZ), are examined.Fig. 7a displays the BM's microstructure with large particles and irregular dimensions in comparison to the preceding sections.In the welded zone, the grain size is approximately 170 µm.The uniformly fine grain (about 15 µm) without any faults, such as flash, fractures, voids, and porosity, is produced in the welded region (Fig. 7a).In Fig. 7b, the coarser grain and uneven size of the heataffected zone (HAZ) are detected without any flaws, including voids and pits.Because of the heat from the welding, the grain size in the HAZ is about 105 µm.

Impacts of Process Parameters on Responses
The experimental data of the GTAW Ti40A are shown in Table 3.
As the I increases from 70 A to 130 A, the HI is increased by 52.6 % (Fig. 8a).An increased I causes a higher instant current; hence, more heat input is produced.As V increases from 22 V to 26 V, the HI is increased by 39.2 % (Fig. 8a).An increased V causes a higher arc voltage, leading to higher heat input.The similar impacts of the I and V on the HI were explained in the works of [7], [15], and [16].As F increases from 12 L/min to 20 L/min, the HI is decreased by 24.3 % (Fig. 8b).A higher F increases the amount of shielding gas, leading to a reduction in the instant current; hence, the HI reduces.As the G increases from 1.5 mm to 4.5 mm, the HI is decreased by 21.3 % (Fig. 8b).A higher G increases the electrode stick out, leading to a higher resistance.The instant current decreases, and the HI decreases.Similar influences can be found in the literature [19].
The TS improves by 18.6 % when the I rises from 70 A to 130 A (Fig. 9a).Due to the poor diffusion between the two plates caused by the low energy input supplied at a low I, a low TS was formed.A greater I results in a better fusion and an improvement in the TS by increasing the energy delivered to the base metal.As V increases from 22 V to 24 V, the TS is increased  9a).A weak joint is created because a low V causes a low heat input and a poor specimen combination.The heat input rises with a V increase, strengthening the joint.A further V, however, transfers too much energy into the base material.The overheating temperature may lead to a higher grain size; hence, the TS decreases.Similar impacts of the I and V can be found in the works of [7], [13], and [16].
As F increases from 12 L/min to 16 L/min, the TS is increased by 7.5 %, while a further F decreases by around 11.1 % in the TS (Fig. 9b).A higher F may cause a proper welding condition, leading to a lower grain size; hence, the TS decreases.However, an excessive F decreases the heat input transferred into the base material, leading to an improper fusion; hence, a weak joint is produced.As the G increases from 1.5 mm to 4.5 mm, the TS is reduced by 10.1 % (Fig. 9b).Higher G results in an incorrect fusion due to lower input energy transferred to the base material; hence, the TS decreases.The welding gap length and the energy input have an inverse relationship.Consequently, at the lowest G, the TS can be maximized.Similar results were presented in the literature [20].
The MH decreases by 23.3 % when the I rises from 70 A to 130 A (Fig. 10a).A rise in the I causes the energy input at the welding zone to increase, which raises the grain size and causes the MH to drop.The MH decreases by 22.5 % when V rises from 22 V to 24 V (Fig. 10a).A rise in the V results in a higher energy input, which in turn leads the welding area to become coarser; as a result, the MH reduces.Similar results were presented in the literature [5] and [20].
The MH rises by 23.2 % when F increases from 12 L/min to 16 L/min (Fig. 10b).A greater F is linked to lower energy input, which causes solidification to happen more quickly and produces a higher MH.The MH increases by 18.5 % as G increases from 1.5 mm to 4.5 mm (Fig. 10b).Higher heat input at a low G could result in larger grain sizes and a low MH.Lower energy input results from a higher G.The result is a tiny grain size, which raises the MH.Similar outcomes can be found in the work of [20].

ANOVA Analysis for Welding Responses
ANOVA results for the HI model are shown in Table 4.The I, V, F, G, IF, VG, FG, I 2 , V 2 , F 2 , and G 2 are significant terms for the HI model.The contributions of the I, V, F, and G are 22.32 %, 15.38 %, 13.31 %, and 11.12 %, respectively.The contributions of the IF, UG, and FG are 3.43 %, 14.44 %, and 3.83 %,  I 2 , V 2 , F 2 , and G 2 are 9.46 %, 4.91 %, 3.82 %, and 4.27 %, respectively.
Table 7 presents confirmations of the precision of the HI, TS, and MH models.The small deviations (less than 5 %) indicate the allowable validity of the RBFN correlations.

Optimal Outcomes Produced by the ANSGA-II
The weights of the HI, TS, and MH are 0.36, 0.33, and 0.31, respectively.Fig. 11 shows the Pareto graphs produced by ANSGA-II.Consequently, a low HI causes a reduction in the TS (Fig. 11a), while a larger HI results in an enhanced MH (Fig. 11b).Accordingly, the optimal point with the highest PI i is chosen as the best one (Table 8).The best results produced by the I, V, F, and G are 89 A, 23 V, 20 L/min, and 1.5 mm, respectively (Table 10).Whereas the TS and MH are improved by 1.2 % and 19.8 %, respectively, the HI is down 18.4 %.

Optimal Outcomes Produced by the NSGA-II
To prove the strength of the proposed approach, the conventional NSGA-II is applied to find optimal data.The optimal values of the I, V, F, and G are 81 A, 22 V, 19 L/min, and 1.4 mm, respectively (Table 8).The corresponding values of the HI, TS, and MH are 18.17 %, 14.82 %, and 4.74 %, respectively.As a result, the I is the most effective factor, followed by the G, F, and V, respectively.ANOVA results for the MH model are shown in Table 6.The I, V, F, G, IV, IF, VF, VG, FG, I 2 , V 2 , F 2 , and G 2 are significant terms.The contributions of the I, V, F, and G are 15.66 %, 16.28 %, 15.05 %, and 12.29 %, respectively.The contributions of the IV, IF, VF, VG, and FG are 3.33 %, 8.85 %, 1.79 %, 2.38 %, and 1.74 %, respectively.The contributions of the 638.31J/mm, 450.22 MPa, and 316.8 HV.However, the conventional NSGA-II provides a higher HI and lower TS, as well as MH.In comparison to the traditional NSGA-II, it can be stated that the ANSGA-II produces superior optimal results.

Novelty and Applications of the Findings
The novelty of this work can be expressed as follows.
This work proposed an efficient optimizing algorithm entitled ANSGA-II, which could be effectively applied to solve complicated issues and find global results instead of traditional algorithms.
The trade-off analysis between the HI, TS, and MH was successfully solved using optimal parameters.The optimality can be used to obtain a sustainable GTAW process.The highly accurate models of the HI, TS, and MH were developed using the ANN approach, as compared to the conventional RSM ones.
The proposed optimization technique comprising RBFN-ANSGA-II-EAMR can be used to address optimization problems related to various GTAW operations and machining processes.
The applications of the findings can be expressed as follows.
The findings can be utilized to develop an expert system that will allow the GTAW to operate in many industries.
The practical HI, TS, and MH values of the GTAW Ti40A can be predicted using the RBFN models.
The optimal data can be utilized to improve quality indicators and energy efficiency of the practical GTAW Ti40A.
By leveraging the effects of GTAW inputs on the output, the technological knowledge of the GTAW process can be improved significantly.
The range of output objectives may be considered crucial technical advice for welding researchers.

CONCLUSIONS
The objective of the current study was to select the optimal GTAW inputs (I, V, F, and G) in order to decrease heat input (HI) and increase welding quality (TS and MH).The ANSGA-II was utilized to produce feasible solutions, and the RBFN method was applied to recommend GTAW solutions.The WPCA and EAMR were applied to calculate the weights and select the best optimal results.The conclusions are presented as follows: 1.The highest F and G values were recommended, but to reduce the HI, the low values of I and V were used.The medium values of V and F were addressed, and the highest I and lowest G were utilized to improve the TS.The lowest I and V were utilized, while the highest F and G were used to optimize the MH. 2. The HI, TS, and MH increase from 388.56 J/mm to 980.14 J/mm, 388.73 MPa to 530.87 MPa, and 319.3 HV to 364.2 HV, respectively, for the GTAW parameters considered.3. The I and V contributed the most to the HI and MH models.The I and G were named as the most effective parameter in the TS model.4. The I, V, F, and G have optimal data of 89 A, 23 V, 20 L/min, and 1.5 mm, in that order.While the HI was saved by 18.4 %, the TS and MH improved by 1.2 % and 19.8 %, respectively.5.The Pareto graphs produced by the ANGA-II could be used to select optimal parameters and responses for different GTAW purposes.6.Compared to the conventional NSGA-II, the developed ANSGA-II might be used to tackle complex problems and produce better results.7. The ANSGA-II could be utilized to obtain global data instead of conventional algorithms.8.The designed and fabricated fixture can be utilized in other GTAW operations.9. Improving HI, TS, and MH are practical benefits to the GTAW Ti40A operation.10.The impacts of the GTAW factors on air pollution and elongation will be explored in future work.

Fig. 1 .
Fig. 1.The scheme of the GTAW processFig. 1 illustrates the GTAW process.Table 1 displays the process parameters with their respective levels.The machine's manufacturer's recommendations are used to calculate the values of I and V.The F is chosen in accordance with the attributes of the air supplier, whilst the G is cited from relevant sources.The optimization issue is expressed as: Finding X = [I, V, F, and G].Maximizing TS and MH; Minimizing HI.Constraints: 70 A ≤ I ≤ 130 A; 22 V ≤ V ≤ 26 V; 12 L/min ≤ F ≤ 16 L/min; 1.5 mm ≤ G ≤ 4.5 mm.

Figs. 5
Figs. 5 and 6 display the representative values obtained at experimental No. 3 and 31, respectively.As shown in Fig.7, the microstructure of the various welding locations, such as the base material (BM), heat-affected zone (HAZ), and welded zone (WZ), are examined.Fig.7adisplays the BM's microstructure with large particles and irregular dimensions in comparison to the preceding sections.In the welded zone, the grain size is approximately 170 µm.The uniformly fine grain (about 15 µm) without any faults, such as flash, fractures, voids, and porosity, is produced in the welded region (Fig.7a).In Fig.7b, the coarser grain and uneven size of the heataffected zone (HAZ) are detected without any flaws, including voids and pits.Because of the heat from the welding, the grain size in the HAZ is about 105 µm.

Fig. 8 .Fig. 9 .Fig. 10 .
Fig. 8.The impacts of process parameters on the HI; a) HI versus I and V, and b) HI versus F and G

Table 1 .
Process parameters of the GTAW operation

Table 3 .
Experimental results of the GTAW operation

Table 6 .
ANOVA results for the MH model

Table 4 .
ANOVA results for the HI model

Table 5 .
ANOVA results for the TS model

Table 7 .
Comparative data for RBFN models

Table 8 .
Optimization results produced by the ANSGA-II and NSGA-II