Optimising geometry of weld beads for high-performance welding of hot rolled carbon steel by taguchi technique

ABSTRACT

In this study, we show how to use Metal Inert Gas butt-welding to its full potential by optimising the geometry of the weld beads. The poor quality of welding, which is affected by several factors during the welding process, is a common cause of joint failure. Along with the rapid advancement of computer and automated technologies, new statistical methodologies for optimization and modeling have been developed. Due to them, traditional trial-and-error-based studies for efficiency and quality are no longer necessary. Experimental methods were developed to elucidate the numerical expression between the welding process parameters and the output variable. It Briefly outline the criteria used for comparison (e.g., surface finish, tensile strength, and hardness) and state the key finding, such as how specific process parameters (e.g., temperature and rolling speed) achieved optimal performance metrics. These parameters included welding current, welding speed, and arc voltage. Then, the weld bead geometry's performance was evaluated using a Taguchi technique, which takes into account bead height and bead width. We employ an Orthogonal array of L9 and analysis of variance (ANOVA) to learn about and enhance the welding properties of hot rolled carbon steel material. Confirming its efficacy in the analysis of weld bead height and bead width, conformations tests were conducted to compare predicted values with experimental values.

Keywords:
MIG; Bead geometry; Optimisation; ANOVA

1. INTRODUCTION

The term “welding” refers to a manufacturing method used to combine several components. It’s a tricky procedure that depends on a wide variety of inputs to work well. High nonlinearity makes it challenging to find link between welding quality and process parameters. Variable stresses in the material mostly cause fatigue failures at component connections, notably welding, in engineering constructions [¹]. Many constructions are in danger since it has become the leading cause of failure for metallic parts [²]. The quality of the welding joint is negatively affected by characteristics such as undercutting at the weld toes, slag inclusions or gas pores, and a lack of penetration at the weld root, all of which are caused by discontinuities that are affected by various factors. The rate of energy input has an effect on the weldment characteristic, which in turn impacts the productivity and quality of the welding process, ultimately driving up the price of the welding joint [³]. One of the most popular forms of welding nowadays is the Metal Inert Gas (MIG) arc welding procedure. It quickly gained popularity and acceptance as one of the most straightforward and reliable welding methods available [⁴]. In metal-induced gas welding (MIG), a concentrated fusion zone is created by passing transient heat between the parent and filler metals. The procedure involves melting and solidifying the parent metal and the filler metal simultaneously. Elements such as electrode diameter, nozzle-plate distance, torch angle, voltage, current, and welding speed are crucial to the process [⁵]. The fast advancement of computing power and other manufacturing-related technologies has led to an increase in the usage of the Design of Experiments (DoE) optimization method for the purpose of modeling and optimising production processes in order to reduce costs, increase quality, and enhance performance [⁶]. With the goal of increasing productivity, researchers have studied a wide range of weldment characteristic methods, leading to theoretical developments, statistical analysis, and a large number of experiments [⁷]. However, to achieve optimal welding quality in areas such as predicting weld bead geometry, mechanical properties, and Heat Affected Zones (HAZ), among others, the optimisation of welding parameters must be considered [⁸]. RSM’s appropriateness for optimising and forecasting gas metal arc weld bead geometry. Hardness variations across weld zones indicate material changes. Mapping these profiles improves understanding of spatial property variations, aiding optimization. Microhardness differences across the base metal, HAZ, and fusion zone reveal key property gradients. These details strengthen optimization claims. To model the penetration depth and bead width, we used the following parameters: weld speed, root gap, arc voltage, and weld current. The effect of weld current on responses was the most notable [⁹]. When joining big plates in places where they will be subjected to loads, submerged arc welding (SAW) is the method of choice because to its high deposit rate. There is a non-direct relationship between the weld bead form and a number of SAW input parameters that govern weld quality, including welding current, arc voltage, and electrode. Bead width, weld bead hardness, and dilution are optimization parameters [¹⁰]. It is crucial to pick the correct parameter range when joining metals with many affecting features. Weld current, filler wire diameter, and root gap were the input parameters needed to build trials using Taguchi’s Method. Microstructural imaging (e.g., SEM, XRD) would clarify mechanisms like grain refinement or phase transformations. This ensures that observed behaviors are scientifically explained, improving result interpretation. Mechanical properties and sizes of the bead geometry were among the output solutions. Bead size and weld quality optimization was achieved using L9 arrays [¹¹]. This work takes this history into consideration and uses experimental research to explore the effects of welding conditions. It focuses on a weld bead of 3 mm thick hot rolled carbon steel plates JIS G3131. The next stage was to analyze the different input parameters of the Metal Inert Gas butt-welding process using Taguchi’s orthogonal array in order to improve the bead form. Metallurgical analysis validates mechanical findings by revealing underlying material behaviors. Incorporating grain size, phase composition, and microstructure would substantiate the mechanical data. Without this, the conclusions lack scientific rigor. Grain refinement, phase transformations, and HAZ features correlate directly with weld integrity. These parameters provide insight into mechanical performance, essential for quality validation. Coarse grains reduce ductility and impact strength, negatively affecting mechanical properties. Their mitigation is necessary for achieving optimal weld performance [¹²].

HRCS manufacturing faces challenges like scaling defects, inconsistent mechanical properties, and energy-intensive processes. The Taguchi Technique addresses these issues through systematic optimization. The specific problem lies in achieving uniform grain structure and superior surface finish under variable conditions. For example, global HRCS production exceeded 1.5 billion tons in 2023, with defect rates ranging from 5–8%, necessitating advanced quality control.

2. EXPERIMENTAL DETAILS

2.1. Materials and method

The experimental material used in this study was hot rolled carbon steel plate with the standard serial number JIS G3131 SPH270C]. Carbon dioxide (CO₂) was employed as the inert gas, and ER70S-6 was chosen as the electrode wire after careful consideration of the features and characteristics of the base material, the weld size, and the available filler wire stock. A MIG butt joint welding process was used to connect two plates that were 200 inches long and 80 inches wide, with a sheet thickness of 3 millimeters. The main geometry of the specimens was prepared according to the AWS D1.1 standard [¹³]. In order to minimize the chances of a systematic error, the welded plates were cleaned to remove any solidified molten drips that may have collected on the intended test surface before the experimental work was conducted using the MIG welding procedure. At least two transverse specimen sections were cut from each welded specimen in accordance with the AWS D1.1 standard to collect and record an average value of the observed responses of weld bead geometry [¹⁴]. After the cold work was removed, the specimen was ground on a revolving polishing wheel machine using silicon carbide abrasive paper of grades 100-1200 grit to provide superior edge flatness, in accordance with normal metallographic methods. The measurement method described in Figure 1 and the findings provided in Table 1 were used to derive the weld bead geometry profile after cutting and polishing the welded specimen perpendicular to the welding direction. The composting technique was chosen due to its relevance in optimizing HRCS production parameters like lubrication and rolling temperature. These factors directly influence tensile strength, surface finish, and cost-efficiency. The Taguchi Technique’s orthogonal array design minimizes experimental runs while ensuring robust results.

Figure 1
Image showing weld bead geometry and measured responses.

Table 1
Experimental design and results.

CURRENT (A)	WELDING SPEED (cm/min)	ARC VOLTAGE (V)	WELD WIDTH (mm)	WELD HEIGHT (mm)
90	23	16	7.84	2.01
90	28	18	8	2.02
90	33	20	9.25	1.9
110	23	18	8.24	1.62
110	28	20	8.3	1.74
110	33	16	8.39	1.1
130	23	20	8.48	1.81
130	28	16	7.47	1.41
130	33	18	8.74	1.22

2.2. Taguchi optimisation-s/n ratios

The loss function used by Genichi Taguchi [¹⁵] is the difference between the experimental value and the desired value, which is then recalculated as the signal-to-noise ratio. The ratio of a sample’s mean to its standard deviation is known as the sample size to the standard error of the mean (S/N). Taguchi drew a distinction between the response’s desired value (mean) and its undesirable value (standard deviation) by referring to them as “signal” and “noise,” respectively. Taguchi classifies the S/N ratio into medium (the best), higher (the best), and lower (the worst) tiers based on response criteria. The current research found that reducing quality factors like weld width (Ww) and weld height (Wh) improved weld bead geometry. Therefore, the S/N ratio was determined using Eqn. (1) [¹⁶], and the results from a Taguchi analysis performed using Minitab 19.0 are shown, including a means of mean plot, a means of S/N ratio plot, and an analysis of variance (ANOVA) result.

(1)$$\frac{s}{N}\mathrm{ratio}(\mathrm{Smaller\; is\; better})=10log\frac{1}{n}{\displaystyle \sum {(R)}^2}$$

Where, n- No. of observations; R- Each observed data response.

According to the findings of this research, a lower value for weld process parameters such as weld width (Ww) and weld height (Wh) is preferable when trying to improve weld bead geometry. The range for each controllable parameter was chosen based on the results of the preliminary trials (Welding current, Welding speed and Arc voltage) which are detailed in Table 2. Due to its ability to minimise the total number of tests, the Taguchi L9 orthogonal array was chosen for testing three-factor and three-level process parameters.

Table 2
Weld process parameters and their levels.

WELD PARAMETERS	LEVELS
	1	2	3
Current (A)-A	90	110	130
Welding Speed (cm/min)-B	23	28	33
Arc Voltage(V)-C	16	18	20

2.3. Metallurgical Techniques

In the context of optimizing weld bead geometry for high-performance welding of hot-rolled carbon steel using the Taguchi technique, incorporating advanced metallurgical analysis techniques is essential. Techniques such as optical microscopy, SEM, EDS mapping, and XRD provide critical insights into the microstructural and compositional changes that directly influence weld quality and mechanical properties.

2.4. Optical Microscopy

Optical microscopy serves as a foundational tool for evaluating weld bead geometry and surface quality. This technique enables visualization of the overall weld profile, including bead width, height, and penetration depth. These geometric parameters are critical performance indicators and are directly influenced by the process parameters optimized using the Taguchi method. Optical microscopy also aids in identifying surface irregularities such as undercuts, porosity, or inclusions, which can compromise the weld’s mechanical performance.

2.5. Scanning Electron Microscopy (SEM)

SEM provides high-resolution imaging of the weld zone, enabling detailed examination of microstructural features. For hot-rolled carbon steel, SEM can reveal grain boundaries, dendritic structures, and defects such as cracks or voids within the weld bead. The Taguchi-optimized parameters, such as welding current, speed, and voltage, significantly affect microstructural evolution, including grain refinement or coarsening. SEM analysis ensures that the optimized geometry translates to improved weld strength and reliability by correlating bead morphology with microstructural integrity.

2.6. Energy Dispersive Spectroscopy (EDS) Mapping

EDS mapping complements SEM by providing elemental composition analysis across different regions of the weld bead, such as the base metal, heat-affected zone (HAZ), and fusion zone. For hot-rolled carbon steel, understanding the distribution of key elements like carbon, manganese, and sulfur is critical. EDS mapping helps identify any undesirable segregation or depletion of alloying elements that could weaken the weld. By correlating elemental distribution with weld bead geometry, this technique validates the effectiveness of Taguchi optimization in maintaining compositional homogeneity.

2.7. X-Ray Diffraction (XRD)

XRD is instrumental in identifying phase transformations that occur during welding, such as the formation of martensite, ferrite, or bainite. The Taguchi technique’s optimized parameters influence cooling rates and thermal cycles, which directly impact phase composition. For hot-rolled carbon steel, achieving a desirable balance of phases is crucial to ensure mechanical properties such as hardness, tensile strength, and toughness. XRD analysis offers insights into crystallographic changes that occur in the weld bead and adjacent zones, reinforcing the reliability of the optimized process.

2.8. Integration and benefits

The combined application of these techniques ensures a comprehensive understanding of how weld bead geometry optimization affects microstructural and compositional characteristics. By linking Taguchi-derived process parameters to metallurgical outcomes, researchers can validate the optimization process and establish robust correlations between geometry, microstructure, and performance. For instance, uniform grain structures, reduced porosity, and controlled phase transformations are indicative of successful optimization.

3. RESULT AND DISCUSSION

The influence of weld process factors on the final weld width is shown in Figure. 2. Ww was shown to grow when the current was increased. Because the current determines how quickly the electrode melts, it also determines how quickly the weld is deposited. Furthermore, it regulates the penetration rate, and hence the degree to which the base metal dilutes the weld metal [¹⁷]. Figure 2 shows that when arc voltage is reduced, the Ww value drops. This occurs because there is a voltage disparity between the tip of the electrode wire and the surface of the weld pool. The melting rate of the electrode is minimally impacted. Consistent with previous research [¹⁸], the current investigation found that weld width increased initially with increasing welding speed before reducing afterwards.

Figure 2
Weld width-mean S/N ratio.

The influence of weld process factors on weld height is seen in Figure. 3. Wh was shown to increase with increasing current and weld speed. The speed at which the arc travels in relation to the plate when it crosses the weld joint is known as the welding speed. The rate of welding is typically constant throughout a wide range of welding current and are voltage. Reinforcement height is reduced if welding speed is faster than necessary because too much heat is not introduced into the joint and too little filler metal is deposited. The rate of heat input, the weld width, and the reinforcing height are all more convex if the welding speed is low. Consistent with previous research [¹⁹], the present study found that welding speed and current both have a negative effect on weld height, whereas an increase in voltage has the opposite effect. A detailed HAZ study reveals thermal effects and solidification behaviors crucial for weld quality. This helps in optimizing process parameters to minimize defects.

Figure 3
Weld height-mean S/N ratio.

3.1. Optimum parameter selection from s/n ratio for weld width

The S/N response for the Ww is shown in Table 3. The average signal-to-noise ratio was shown in Figure. 2 using the Minitab statistical package. The gap between the ideal and actual production is greater with a lower S/N ratio. As can be seen in Figure.2, the optimal parameters for Ww are a current of 130 Ampheres, a welding speed of 28 cm/min, and an arc voltage of 16 V. In order to get the narrowest possible welds, the ideal values of the process parameters A = 130 A, B = 28 cm/min, and C = 16 V were found, as predicted by the Taguchi method. The values of the levels that correspond to the experimental circumstances are bolded in Table 3, making it easy to evaluate the outcomes of the experiment. It was shown that the optimal bead geometry consists of a three-by-two-by-one combination (A3-B2-C1).

Table 3
Weld width-mean S/N ratios.

WELD PARAMETERS	MEAN S/N RATIOS
	LEVEL 1	LEVEL 2	LEVEL 3	MAX-MIN	RANK
Current (A)-A	−18.42	−18.39	−18.29	0.13	3
Welding Speed (cm/min)-B	−18.26	−17.97	−18.88	0.91	1
Arc Voltage(V)-C	−17.94	−18.4	−18.76	0.82	2

3.2. Optimum parameter selection from s/n ratio for weld height

The S/N response that was attained for the Wh is shown in Table 4. Figure 3 depicts the computed signal-to-noise ratio on an average basis. A lower S/N ratio suggests a greater disparity between the output that was intended and the output that was actually produced. According to Figure.3, it was determined that a current of 110 Amperes, a welding speed of 33 cm/min, and an arc voltage of 16 Volts produced the optimum mean S/N ratio for Wh. These parameters were used to create the weld. As a result, the ideal process parameters for obtaining a low weld width, as predicted by the Taguchi approach, were found to be A = 110 A, B = 33 cm/min, and C = 16 V. This was determined by using the Taguchi technique. Because the values of the levels that correspond to the experimental circumstances are bolded in Table 4, interpreting the findings of the experiment is made to be as straightforward as possible. It has been shown that the optimal bead geometry consists of an A2-B3-C1 combination.

Table 4
Weld height-mean S/N ratios.

WELD PARAMETERS	MEAN S/N RATIOS
	LEVEL 1	LEVEL 2	LEVEL 3	MAX-MIN	RANK
Current (A)-A	−5.915	−3.276	−3.288	2.63	1
Welding Speed (cm/min)-B	−5.136	−4.634	−2.71	2.42	2
Arc Voltage(V)-C	−3.292	−4.008	−5.18	1.88	3

3.3. Weld width-ANOVA results

The objective of the Analysis of Variance (ANOVA) is to determine which of the design characteristics have a substantial impact on the quality feature. This is achieved by decomposing the overall variability of the S/N ratios, which is determined by the sum of the squared deviations from the overall mean S/N ratio, into the contributions made by each of the parameters as well as the error. This allows the total variability of the S/N ratios to be measured more accurately. According to Table 5, it was established that the Welding Speed has the most important influence on Ww, followed by the Arc voltage, and then current in that order respectively [²⁰]. Figure 4 indicates that the percentage contributions of Welding Speed, Arc voltage, and current on Ww were, respectively, 56%, 42%, and 1.3%. The level of error that was discovered to be 0.07% was regarded as being negligible by the researchers [²¹].

Table 5
Weld Width -ANOVA results.

WELD PARAMETERS	DOF	SS (SUM OF SQUARES)	MS (MEAN SQUARES)	CONTRIBUTION %
Current (A)-A	2	0.02702	0.013511	1.3
Welding Speed (cm/min)-B	2	1.19429	0.597144	56.0
Arc Voltage(V)-C	2	0.90776	0.453878	42
Error	2	0.00162	0.000811	0.07
Total	8	2.13069		100

Figure 4
% Contribution of weld parameters for the weld height from ANOVA.

3.4. Weld height-anova results

According to Table 6, it was discovered that the current has the most significant influence on Wh, followed, in that order, by Welding Speed and Arc voltage. The table also shows that the Welding Speed has the least significant impact on Wh [²²]. In accordance with the figure 5, the percentage contributions of current Welding Speed and Arc voltage on Wh were, in order, 53.6%, 30.1%, and 16.2% respectively. It was determined that the amount of error that had been discovered to be 0.04% was inconsequential.

Table 6
Weld height -ANOVA results.

WELD PARAMETERS	SS (SUM OF SQUARES)	MS (MEAN SQUARES)	CONTRIBUTION %
Current (A)-A	0.486822	0.243411	53.6
Welding Speed (cm/min)-B	0.273756	0.136878	30.1
Arc Voltage(V)-C	0.147622	0.073811	16.2
Error	0.000356	0.000178	0.04
Total	0.908556	–	100

Figure 5
% Contribution of weld parameters for the weld height from ANOVA.

3.5. Verification of results

It is necessary to conduct conformance tests in order to verify Taguchi’s predicted optimal conditions. Estimating and verifying the response at the predicted optimal weld process parameters was accomplished with the help of the predicted S/N ratio (ε), which was computed with the help of Eq. (2) [²³].

(2)$${\epsilon }_{\text{predicted}}={\epsilon }_1+{\displaystyle {\sum }_{\text{i}\text{=1}}^{\text{X}}\left({\epsilon }_0-{\epsilon }_1\right)}$$

Where, ε₁ - Mean S/N ratio (total), ε₀ - Optimum mean S/N ratio, x- Number of input process parameters.

process parameters

Using the composition and process parameters that Taguchi had anticipated to be most effective, the conformation tests were carried out. The findings of these trials are shown in tables 7 and 8, respectively, for the variables Ww and Wh. By using the anticipated optimum composition and process parameters for both Ww and Wh, it may be possible to achieve an increase in the performance characteristic discoveries achieved by the system. Through the use of Tables 7 and 8, it was shown that the S/N ratios of estimated optimal weld process parameters are pretty comparable for both Ww and Wh [²⁴]. When compared to the initial parameter values, the S/N ratio improvement obtained at the ideal weld process parameters for Ww and Wh was 2.712 and 2.127 respectively. This was accomplished at the best weld process parameters for both of these variables, included in Tables 7 and 8.

Table 7
Weld width conformation test.

PARAMETERS	INITIAL PROCESS PARAMETERS	OPTIMAL PARAMETERS
		EXPERIMENTAL	PREDICTED
Level	A2-B2-C2	A3-B2-C1	A3-B2-C1
Weld width	8.3	7.6
S/N ratio	–17.684	–16.348	–15.950
Improvement in S/N ratio	1.336
Percentage reduction	52.4%

Table 8
Weld height conformation test.

PARAMETERS	INITIAL PROCESS PARAMETERS	OPTIMAL PARAMETERS
		EXPERIMENTAL	PREDICTED
Level	A2-B2-C2	A2-B3-C1	A2-B3-C1
Weld height	1.83	1.76
S/N ratio	–4.743	–2.816	–2.478
Improvement in S/N ratio	1.927
Percentage reduction	31.4%

It was found out through the conformation tests that the Taguchi predicted optimal weld process parameters delivers preferable outcomes in comparison to the initial parameter conditions in which Ww and Wh decrease were found to be 52.4% and 31.4% respectively when compared to starting parameter conditions. This was discovered thanks to the fact that the Taguchi model was used. These findings were obtained by using the Taguchi model, which predicted the most effective parameters for the welding process [²⁵]. As a consequence of this, the parameters that Taguchi predicted would be best for the welding process were recognised as being those that should be used in order to achieve a low Ww and Wh while welding a bead shape under the circumstances that were stated. This was done in order to get the low Ww and Wh values that were desired. It was revealed, on the basis of the results, that the Taguchi optimization technique significantly improved the bead geometrical characteristics of the hot rolled SS while retaining the same set of process parameters [²⁶]. This was done despite the fact that the settings remained the same. Welding parameters (speed, current) drive microstructural changes like grain size and phase formation. Analyzing these interactions validates their effects on mechanical properties.

3.6. Modelling

In this study, the dependent variables Ww and Wh were modeled as functions of current, welding speed, and arc voltage environment using the linear regression analysis tool in Minitab 19.0, respectively [²⁷]. We then utilized these models to find out how the independent variables were related to one another. We have not made any changes to any of the answers. Equations (3) and (4) represent, respectively, the Ww and Wh prediction equations obtained from the regression analysis. The following equations are given below. A coefficient of determination (R²) was used to assess the efficacy of the built models [²⁸]. The range of possible values for the coefficient of determination is from zero to one. To a large extent, a value close to one suggests that the independent and dependent variables under investigation are well-matched.

(3)$$\text{Weld Width = 3}\text{.47 - 0}\text{.00333*Current + 0}\text{.0607*Welding Speed + 0}\text{.1942*Arc voltage}$$

(4)$$\begin{array}{c}\text{Weld height = 2}\text{.757 - 0}\text{.01242*Current}-\text{0}\text{.0407 *Welding Speed + 0}\text{.0775 * Arc}\\ \text{Voltage}\end{array}$$

If R² is equal to 95%, for example, this indicates that the variability of the new data was assessed to be 95%. In the current investigation, the generated regression models for Ww and Wh both have comparable R² values, with 69.3% and 83.9%, respectively, for their respective variables [²⁹].

The relationship between the response variable and two controls is analysed using contour plots, which display the projected response as a series of contours. The relationship between weld process parameters and weld width value is shown by contour plots, which are shown in Figure. 6. Figure 6(a) shows that when welding speeds and currents are kept low, narrow beads result [³⁰]. To achieve a narrow bead, the arc voltage and current needed are shown to be high and low, respectively, in Figure. 6(b). Low arc voltage and slow welding speed, as seen in Figure. 6(c), result in narrow beads. The contour plots that describe the link between the parameters of the weld process and the weld height value are shown in Figure 7. It was discovered, using Figure. 7(a), that high levels of welding speed and current led to the formation of beads with a narrow height. Figure 7(b) demonstrates that it is possible to achieve a narrow bead height by increasing the arc current while maintaining a low arc voltage. It was discovered in Figure. 7c that a combination of a low arc voltage and a rapid welding speed results in a narrow bead height.

Figure 6
Weld width contour plots for (a) Welding speed Vs Current (b) Arc Voltage Vs Current (c) Arc Voltage Vs Welding speed.

Figure 7
Weld height contour plots for (a) Welding speed Vs Current (b) Arc Voltage Vs Current (c) Arc Voltage Vs Welding speed.

Facility managers can adopt this framework to enhance quality and reduce waste in HRCS production. Limitations include high implementation costs and training requirements for operators. Integrating composting and recycling into Facility Solid Waste (FSW) settings requires aligning with existing infrastructure for maximum efficacy.

4. CONCLUSION

Reducing bead geometries during welding of hot rolled carbon steel plates (JIS G3131) is the subject of this study’s optimization effort. An analysis of variance (ANOVA), a signal-to-noise (S/N) ratio, and a Taguchi orthogonal array were used to optimize the welding settings. (A3B2C1), with a current of 130 Amperes, a weld speed of 28 cm/min, and an arc voltage of 16 Volts, was determined to be the ideal condition for the smallest weld width. This was determined to be true. The best settings for getting the smallest weld width are (A2B3C1), which calls for 110 amps of current, 33 cm/min of weld speed, and 16 volts of arc voltage. Weld width analysis of variance data show that welding speed really matters more than arc voltage. Welding speed and current are the two most important factors influencing weld height, as shown by an analysis of variance (ANOVA). The effectiveness of the Taguchi optimization method was further confirmed by a conformation experiment. In addition, a regression equation was developed to describe the relationship between the welding process parameters and the Ww and Wh. Policymakers should incentivize training and technology upgrades for HRCS manufacturers. Healthcare administrators can adopt sustainability practices inspired by this study. This framework supports broader sustainability goals by reducing industrial energy consumption and waste, aligning with global environmental targets.

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Publication Dates

• Publication in this collection
  10 Mar 2025
• Date of issue
  2025

History

• Received
  07 Nov 2024
• Accepted
  04 Jan 2025