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Optimization of process parameters in plasma arc cutting of commercial-grade aluminium plate

  • Mridusmita Roy Choudhury EMAIL logo , Hrishikesh Dutta , Utpal Deka , Basireddy Bhavani , Kuldeep K. Saxena and Anil Borah
Published/Copyright: April 10, 2024
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High Temperature Materials and Processes
From the journal High Temperature Materials and Processes Volume 43 Issue 1

Abstract

Plasma arc cutting (PAC) has emerged as a versatile and efficient method for the precision cutting of various materials, including commercial-grade aluminium plates. The optimization of process parameters is crucial for achieving high-quality cuts, minimizing material wastage, and enhancing overall productivity. This study aims to systematically investigate and optimize the key process parameters in PAC of commercial-grade aluminium plates. The experimental design involves the manipulation of parameters such as arc current, gas pressure, and workpiece thickness. A Design of Experiments approach, specifically Taguchi’s orthogonal array, is employed to efficiently explore the parameter space and identify the optimal combination of settings. The response variables considered for optimization include minimum surface roughness, minimum burr height, and maximum material removal rate (MRR). Analysis of variance is performed to get the percentage influence of each process parameter on the performance characteristic. The results obtained from the optimization process are expected to provide valuable insights into enhancing the efficiency and precision of PAC for commercial-grade aluminium plates. Arc current is found to be the most significant parameter in altering the surface roughness. The thickness of the material is the most significant parameter in altering burr height. None of the parameters is found to be significant in altering the MRR from Analysis of Variance analysis. From signal-to-noise ratio analysis and average performance graph, the optimum combination of processes in altering the bur height and MRR are found as arc current at 50 amp, the gas pressure at 5.4 bar, and the thickness of the workpiece at 6 mm.

Keywords: plasma arc welding; optimization; ANOVA; Taguchi’s orthogonal array

1 Introduction

The promptitude and accelerated output in the manufacturing, machinery, and material engineering industries greatly depend upon the selection of cutting tools used in the relevance of economy and time consumption. The rapidity, mass manufacturing, and cost optimization are the major factors linking the growth and business in these industrial sectors. There are primarily three different types of thermal cutting techniques namely gas-cutting, laser cutting, and plasma arc cutting (PAC) techniques applied for cutting materials in different industrial usages like shipbuilding, railway wagons, manufacturing industries, material engineering, machine fabrication, etc. Considering different parameters like minimal slag, environmental factors, economy, safety, system complexity, maintenance, etc., PAC is the most advantageous [1].

PAC is one of the highly acclaimed cutting processes that use thermal energy. It has gained huge applications in cutting stainless steel, manganese steel, titanium alloys, copper, alloy materials, metals with high density and high melting points, and other difficult-to-machine alloys [2,3]. The expense involved in the PAC method is two to three times less and efficiency is about 2.5 times higher than other oxy-fuel processes [4,5]. PAC can also be used to cut complex shapes with accuracy and precision [4]. PAC is commonly used in metal fabrication shops and manufacturing plants to cut and shape metals such as steel, aluminium, and stainless steel, automotive industry for cutting and shaping metal parts during the fabrication of vehicle components. Shipyards use PAC for the fabrication of ship components, including steel plates and sections used in the construction of hulls, decks, and other structural elements. Plasma cutting is employed in construction for cutting metal components used in buildings, bridges, and infrastructure projects. PAC plays a role in the metal recycling industry by efficiently cutting and separating metal components from larger structures. The PAC process is characterized by a plasma arc carrying a high current established between a tungsten electrode and the conductive metal piece. The electrode is connected to the negative terminal and the metal piece is connected to the positive terminal of the power supply. An arc is established between the constricted electrode nozzle and the workpiece. The plasma arc produced has a very high temperature, and high energy density sufficient to melt the workpiece and has a high velocity bearing the features of a plasma jet for removal of the molten material. In this process, an electric arc is passed through an inert gas blown at high speed out of a nozzle. During the processes, some of the inert gas converts to plasma. When this plasma falls on the surface of the metal to be cut, it instantly moves by melting and carrying away the molten metal from the cutting region [6]. However, the quality of the cut varies depending on the arc current, the geometry of the torch, the type of inert gas, the gas flow rate, and the cutting speed [7].

It is feasible to find good number of research works to study the influence of process parameters of PAC and to obtain an optimum cutting condition. Due to high-temperature operations, the process of cutting by PAC on soft material like aluminium is treated as a challenging one. Improvement of the process depends on several quality characteristics namely, material removal rate (MRR), surface roughness, cutting width (kerf), kerf taper angle (conicity), edge roughness, heat-affected zone, burr formation [8], flatness, clean cut bevel angle [9], unevenness of cut surface, etc. [5]. Despite a lot of experimental and theoretical works, there are still grey areas of the PAC process that need a better understanding of process mechanics and physics for improving its quality of performance. Researchers have tried to increase the energy density generated by the plasma arc system so that a higher cutting thickness can be achieved maintaining the quality of the cut. For that purpose, enormous investigations have been carried out on the PAC process by considering the process parameters. Cutting speed, cutting current, cutting height or standoff (the gap between the plasma torch and workpiece), and nature of the cut, cutting pressure, and flow of the plasma gas are obtained to be the most dominating parameters during the cutting of various materials [10,11].

Traditional designs of the experiment (DOE) approaches demand a greater quantity of experimentation when the process parameters and their consequence levels increase. The increased number of experimentations again demands time and resources. The DOE proposed by Taguchi is a powerful tool as it demands a lesser number of experimentations for the same process parameters and levels increase. Specially designed orthogonal arrays (OAs) are used in the Taguchi method to investigate the complete process parameters [12]. This method allows a balanced comparison of levels of any process parameters by fractional factorial matrix. Each process parameters are to be assessed separately from all other parameters in Taguchi’s DOE. This powerful tool for experiment design can also be used to optimize the cutting parameters of a machining process to evaluate the best possible combination for getting a better surface finish, a higher tool life, etc., and to identify the most effective parameters [13,14,15,16,17]. Several scientists have operated Taguchi’s OA method for the optimization of process parameters in PAC operations. Charmarthi et al. [5] have evaluated the unevenness of Hardox-400 material plate of 12 mm thickness machined by the PAC process. The process parameters have been optimized by the authors using DOE to study the surface profile of the cut and constancy for the workpiece material. Das et al. [7] have conducted parametric optimization of process parameters for PAC of EN31 steel by combining the Taguchi method and grey relation analysis. Asiabanpour et al. [18] have used the design of experiments (DOE) to optimize the automated plasma-cutting process on a 0.25-inch-thick stainless steel sheet. The authors have used a response surface methodology approach followed by regression analysis to study the significant factors that influence the surface quality characteristics of the workpiece. To combine models obtained for each response and to balance the trade-offs between responses, the desirability function, and multi-response optimisation technique has been used. Bini et al. [19] have used a high tolerance plasma arc cutting system to cut mild steel of 15 mm thickness. The authors have performed response surface methodology and analysis of variance (ANOVA) to study the process parameters. The arc voltage was found to be the most significant parameter influencing the cutting quality. Chen et al. [20] have used Six Sigma and lean paradigms based on the Taguchi optimization technique in altering the roundness of holes made by the PAC process. For four controllable factors with three corresponding levels, L9 Taguchi OA was used for the conduction of experiments. The optimized setting obtained by the authors was as follows: the smaller tip of the torch, medium range of feed rate, lower range of voltage, and higher range of current. Bhuvenesh et al. [21] have optimised the process parameters of a manual plasma arc cutter (Selco Genesis 90) to cut standard AISI 1017 Steel of 6 mm thickness. The authors have found an inverse relationship between surface roughness and material removal rate. Ismail and Taha [22] have used Taguchi’s technique to optimise the machining process parameters of PAC during the cutting of two steel grades such as ASSAB618 and ASSABDF3. Arc current, scanning velocity, and carbon content of steel were selected as the process parameters. Arc current was found to be the most significant parameter influencing the surface hardness of the cutting surface. Many researchers have studied the machining and welding characteristics of Aluminium, Aluminium alloy, and Aluminium composites [23,24,25,26,27]. Adin [28] has studied the mechanical properties of Aluminium Alloys welded by laser welding and optimized the welding parameters using Taguchi and ANOVA methods. Adin [29,30] has investigated the Machining aerospace aluminium alloy with cryo-treated and untreated HSS cutting tools.

The above discussion reveals that although the plasma arc process has been used to study the cutting behaviour of various ferrous materials like steel, a negligible study has been found to study non-ferrous and soft materials like aluminium. The present article discusses the optimization of the machining process parameters in PAC of a commercial aluminium plate of grade 1050 applying Taguchi’s OA technique. For optimization purposes, two distinct levels were employed for each parameter, including Arc current (C), Gas pressure (P), and Thickness of the workpiece (T). Taguchi’s L8 experimental design has been used to derive an optimal configuration of PAC process parameters, aiming to achieve the best possible values for surface roughness, burr height, and Material Removal Rate (MRR).

2 Experimental investigation

All experiments have been performed on a manual air PAC system (Model– Powermax 105; Make – Hypertherm, Inc.). A hand torch (Torch – DuramaxTM series 75°) has been used for cutting purpose hand torch. The details of the manual air plasma arc-cutting system are shown in Figure 1. In the figure, zone A is composed of channels through which secondary gas passes for cooling the torch. This secondary gas also helps in blowing out the molten metal from the cutting zone to create a fast and slag-free cut. Cool gas enters zone B, and a pilot arc is established between the electrode and the torch tip. This pilot arc heats and ionises the gas. The main cutting arc then transfers to the workpiece in zone C through the column of plasma gas. By forcing the plasma gas and electric arc through a small orifice, the torch delivers a high concentration of heat to a small area.

Figure 1 PAC System.
Figure 1

PAC System.

Commercial aluminium plates of grade 1050 are used as work material for the present study. Specimens of dimensions 152 mm × 50 mm × 6 mm and 152 mm × 40 mm × 10 mm are used for plasma cutting along the length of specimens. Material has been chosen based on the availability and accessibility of materials can be a practical consideration. Aluminium slabs are readily available or can be easily sourced within the constraints of the research environment, budget, and timeline. The cuts are performed in the middle of the workpiece as shown in Figure 2. The work clamp must be connected to the workpiece while cutting. The workpiece and the work clam must have good metal-to-metal contact. To measure the surface roughness, the five highest peaks and five highest valleys of the surface texture (ten-point height method) within a sample length of 52 mm of the workpiece are considered. Peaks and valleys are measured with the help of the profile projector (Make – Banbros) from the datum parallel to the mean line of the workpiece. The uncut surface of the workpiece is used as a datum line for the present study. In the present study burr height is measured using Tool Maker’s Microscope (Make – Banbros) of least count 0.01 mm. The experimental setup is shown in Figure 3.

Figure 2 Commercial-grade aluminium plate before and after plasma cutting.
Figure 2

Commercial-grade aluminium plate before and after plasma cutting.

Figure 3 Experimental setup for measuring surface roughness.
Figure 3

Experimental setup for measuring surface roughness.

To measure the surface roughness, the five highest peaks and five highest valleys of the surface texture (ten-point height method) within a sample length of 52 mm of the workpiece are considered. Peaks and valleys are measured with the help of the profile projector (Make – Banbros) from the datum parallel to the mean line of the workpiece. The experimental setup for measuring surface roughness has been shown in Figure 3. The uncut surface of the workpiece is used as a datum line for the present study. In the present study, burr height is measured using Tool Maker’s Microscope (Make – Banbros) of least count 0.01 mm as shown in Figure 4.

Figure 4 Experimental setup for measuring burr height.
Figure 4

Experimental setup for measuring burr height.

Material Removal Rate (MRR) is a crucial parameter that quantifies the volume of material removed per unit of time during a machining or material processing operation. The material removal is determined by weighing the workpiece before and after the machining process. The weight difference, combined with the material density, is used to calculate the volume removed. MRR has been assessed by measuring the reduction in weight of the Aluminium workpiece (expressed in g·s−1). The time required for cutting has been measured by a stopwatch. The weight of each workpiece has been measured in electronics balance (Make – Digitron) before and after cutting. The difference between the two measurements gives the material removal rate. The parameters, viz., arc current (C), gas pressure (P), and thickness of workpiece (T) have been considered in the present study for optimisation of the machining process parameters. Two levels of each parameter have been taken into consideration. Table 1 shows the process parameters with their levels. In the present work, the responses like surface roughness, burr height, and MRR have been considered for analysis. The significance of the parameter has been studied using ANOVA, and the optimal setting of the parameters has been found by using Signal-to-Noise (S/N) ratio and average performance graph. An illustration of the experimental setup is provided in the flowchart presented in Figure 5.

Table 1

Condition of cutting

Factors Levels
1 2
Arc current (C) 33 50
Gas pressure (P) 4.3 5.4
Thickness of the workpiece (T) 6 10
Figure 5 Flowchart for experimental setup.
Figure 5

Flowchart for experimental setup.

3 Results and discussion

3.1 OAs and experimental design

As per Taguchi’s experimental technique, a set of two levels is allotted to each process parameter. Therefore, each parameter has one degree of freedom (DOF) (level 1), and the interaction between two parameters namely, C × P, C × T, and P × T has one DOF. The DOFs are shown in Table 1. This gives a total of 7 DOF for three process parameters. So, Taguchi’s OA L8 has been designed for the present work. Using more than two levels for each parameter was not practical or feasible due to experimental constraints and the nature of the factors being studied. Two-level designs strike a balance between simplicity and informativeness. Before conducting the main experiment, the authors performed preliminary studies or pilot experiments. These small-scale trials helped in identifying influential parameters and refining the experimental design. The results of these preliminary studies have been taken as a reference for the selection of parameters for the main experiment. Taguchi’s layout for OA8 has been selected for the present analysis, and result obtained from the experimentation is shown in Table 2.

Table 2

Taguchi’s layout of L8 with responses namely, surface roughness, burr height, metal removing rate

TC Surface roughness (µm) Burr height (mm) MRR (g·s−1)
1 302 2.59 0.088
2 634 5.61 0.053
3 594 3.19 0.059
4 1025 6.01 0.030
5 320 0.92 0.171
6 269 3.98 0.114
7 188 0.58 1.025
8 271 2.81 0.509

TC – Treatment condition.

3.2 ANOVA

The intention of performing ANOVA is to investigate design parameters that significantly affect the quality characteristic. In the present study, ANOVA is carried out (shown in Table 3) for responses, namely, surface roughness, burr height, and MRR. The calculated F-ratio of the respective factors should be greater than 162.45 according to a 95% confidence level. The significant values are represented in bold as given in Table 3. From the ANOVA table, Arc current (C), Gas pressure (P), and Thickness of the workpiece (T) were found to be significant in altering surface roughness. Arc current (C) was observed to be the most significant parameter as compared to the other parameters namely, gas pressure (P) and thickness of the workpiece (T) for the parametric levels and confidence level considered in this study. Arc current (C) and thickness of the workpiece (T) was found to be significant, for the parametric levels and confidence levels considered in this study in altering burr height. The thickness of the workpiece (T) was observed to be the most significant parameter as compared to the other parameters namely, arc current (C) and gas pressure (P). No cutting parameter is found to be significant at a 95% confidence level in altering MRR.

Table 3

ANOVA for surface roughness, burr height, MRR

Factors Responses F-ratio calculated Percent contribution (%)
C Surface roughness 1854.82 50.9
Burr height 207.480 38.6
MRR 12.0920 37.5
P Surface roughness 249.76 6.9
Burr height 0.66 0.13
MRR 6.8621 21.3
C × P Surface roughness 539.83 14.8
Burr height 15.75 2.9
MRR 5.1073 25.1
T Surface roughness 516.19 14.172
Burr height 309.68 57.566
MRR 1.9425 6.0214
C × T Surface roughness 436.42 11.982
Burr height 0.756 0.141
MRR 1.2414 3.8480
P × T Surface roughness 144.339 1.217
Burr height 2.64 0.491
MRR 0.9847 3.0523

Bold values represent significant parameters.

3.3 Analysis of the S/N ratio

The S/N ratio is analysed for determining the performance characteristics of the parameters of the experiment.

The S/N ratio is calculated for each trial number, and then, the average S/N ratio is determined for each parameter at their respective level. The largest value of the S/N ratio gives the optimal settings of parameters at their respective level. Basically, there are three types of S/N ratios, namely, larger the better, smaller the better, and nominal the best. This analysis aims to explore the optimal parametric combination for minimum surface roughness, minimum burr height, and maximum MRR. Hence, in the present study, the smaller the better characteristics are used for the calculation of S/N ratios for surface roughness and burr height, and the larger the better characteristics are used for the calculation of S/N ratios for MRR.

The S/N ratio is given by equation (1)

(1) S / N ratio = − 10 log 10 ( MSD ) .

The S/N ratios are calculated using the smaller the better characteristics. The MSD (mean square deviation) is given by equation (2)

(2) MSD = Sum of the square of the responses Total number of reponses .

The calculated S/N ratios are shown in Table 4. The larger values of S/N ratios of the parameters at each level are shown in bold text in the tables.

Table 4

Average values of S/N ratio for surface roughness, burr height, and MRR level wise in PAC

Factors Surface Roughness Burr height MRR
Levels Levels Levels
1 2 1 2 1 2
C −55.33 −48.21 −12.23 −3.88 −25.42 −9.96
P −51.08 −52.46 −8.63 −7.48 −20.21 −15.17
C × P −49.95 −53.60 −6.87 −9.23 −13.07 −22.31
T −50.17 −53.38 −3.22 −12.88 −15.21 −20.17
C × T −50.58 −52.96 −6.28 −9.83 −17.77 −17.61
P × T −52.14 −51.40 −8.02 −8.08 −18.20 −17.19

3.4 Average performance graph

The average performance graph serves as a visual tool to assess the dominance of each parameter at different levels within the experimental design. The average performance graph is obtained by plotting the average values of all the parameters shown in Table 2. For this, the average values of all the responses of the parameters are considered level-wise. The average performance graph is obtained by plotting the average response values.

In the present study, the optimal settings for surface roughness and burr height will be given by the minimum values of the responses of the parameters level-wise, so the line connecting the minimum response values of all the parameters in the average performance graph will give the optimal settings. The optimal settings for MRR will be given by the maximum values of the responses of the parameters level-wise, so the line connecting the maximum response values of all the parameters in the average performance graph will give the optimal settings. Figure 6(a)–(c) shows the average performance graphs for PAC for responses, namely, surface roughness, burr height, and MRR. The S/N ratio (Table 4) the average performance graphs are shown in Figure 6(a)–(c), and the optimal settings of the influencing parameters in PAC are shown in Table 5.

Figure 6 (a)–(c) Average performance graphs for surface roughness, burr height, and MRR.
Figure 6

(a)–(c) Average performance graphs for surface roughness, burr height, and MRR.

Table 5

Optimal setting of the parameters

Responses Optimized values
amp bar mm
Surface roughness C 2 = 50 P 1 = 4.3 T 1 = 6
Burr height C 2 = 50 P 2 = 5.4 T 1 = 6
MRR C 2 = 50 P 2 = 5.4 T 1 = 6

3.5 Test of interaction

“Interaction” describes a condition in which the control of one factor upon the output responses is dependent on the condition of another. To determine the presence of interaction between the parameters, interaction plots were made. The interaction between the Arc current (C) and gas pressure (P) (C × P), arc current (C) and thickness of the workpiece (T) (C × T), gas pressure (P) and thickness of the workpiece (T) (P × T) in altering surface roughness, burr height, and MRR are shown graphically in Figure 7(a)–(c). No strong interaction between the parameters is found in altering surface roughness and MRR. An intense interaction between gas pressure (P) and the thickness of the workpiece (T) is found in altering burr height.

Figure 7 (a)–(c) Interaction graph for C, P, and T in altering for surface roughness, burr height, and MRR.
Figure 7

(a)–(c) Interaction graph for C, P, and T in altering for surface roughness, burr height, and MRR.

Confirmation tests are done to determine the optimum setting of the parameter using equations (3) and (4). Optimum performance without considering interaction effect (Y opt1) and Optimum performance with considering interaction effect (Y opt2) are calculated as follows:

  1. Without considering interaction effect (P × T), i.e. for (C 2, P 2, T 1) from the data obtained from Table 1

    (3) Y opt 1 = T ̅ + ( C 2 ̅ − T ̅ ) + ( P 2 ̅ − T ̅ ) + ( T 1 ̅ − T ̅ ) , = 0.619 ,

    where Y opt1 is the optimum performance without considering the interaction effect, T ̅ is the average result of eight trials = 3.211, C 2 ̅ is the average result of C 2 = 2.073, P 2 ̅ is the average result of P 2 = 3.148, and T 1 ̅ is the average result of T 1 = 3.211.

  2. Considering interaction effect (P × T), i.e. for (C 2, P 1, T 1), from the data obtained from Table 1

(4) Y opt 2 = T ̅ + ( P 1 T 1 ̅ − T ̅ ) + ( C 2 ̅ − T ̅ ) = 0.617 ,

where Y opt2 is the optimum performance considering interaction effect, and P 1 T 1 ̅ is the average result of P 1, T 1 = 1.755.

It is obtained that Y opt2 < Y opt1, i.e. the optimum performance calculated without considering the interaction (P × T), is more than the optimum performance considering interaction (P × T). Hence, the optimum cutting condition considered is C 2 (50 amp), P 2 (5.4 bar), and T 1 (6 mm) in altering burr height which is like the result obtained from Table 5.

3.6 Response vs Treatment condition plot

The response parameters are plotted against the treatment condition shown in Figure 8(a)–(c). It is observed that treatment condition 7 gives a minimum value of burr height and a maximum value of MRR. The level of the cutting parameters obtained for treatment 7 from Table 5 is C 2 = 50 Amp, P 2 = 5.4 bar, and T 1 = 6 mm in Figure 6. From Figure 8(a)–(c), it is observed that treatment condition 7 gives the minimum surface roughness. The level of the cutting parameters obtained for treatment 7 (from Table 5 is C 2 = 50 Amp, P 2 = 5.4 bar, and T 1 = 6 mm). But the optimum value obtained from the average performance graph and S/N ratio analyses is for the treatment condition 5 (C 2 = 50 Amp, P 1 = 4.3 bar, T 1 = 6 mm). This is because the S/N ratio represents the magnitude of the mean process compared to its variation [8].

Figure 8 (a)–(c) Response, namely, surface roughness, burr height, and MRR vs treatment condition plot.
Figure 8

(a)–(c) Response, namely, surface roughness, burr height, and MRR vs treatment condition plot.

4 Conclusions

The present work represents a parametric optimisation of PAC operation for different responses namely surface roughness, burr height, and MRR in commercial-grade aluminium plates (grade1050). Experimental analyses of the PAC operations in altering surface roughness, burr height, and MRR for the parametric levels and confidence levels considered in this study can be summarised as follows:

  • From ANOVA analysis, arc current is found to be the most significant parameter in altering surface roughness. The thickness of the workpiece is determined to be the most significant parameter in altering the burr height. However, none of the parameters is found to be significant in altering the MRR from ANOVA analysis.

  • The optimum combination of process parameters in altering surface roughness is found as arc current at 50 amp (level 2), gas pressure at 4.3 bar (level 1), and thickness of the workpiece at 6 mm (level 1). However, the optimum combination of processes in altering the bur height and MRR is found as arc current at 50 amp (level 2), the gas pressure at 5.4 bar (level 2), and the thickness of the workpiece at 6 mm (level 1).

  • A strong interaction between gas pressure (P) and workpiece thickness (T) is found in altering burr height. But the optimum performance calculated without considering the interaction (P × T) is more than the optimum performance considering interaction (P × T). Hence, the optimum cutting condition considered is C 2 (50 amp), P 2 (5.4 bar), and T 1 (6 mm) in altering burr height. No strong interaction between the parameters is found in altering surface roughness and MRR.

  • The percentage contribution of the arc current (C) is more than other parameters, namely, gas pressure (P) and workpiece thickness (T) in altering the process response. Surface roughness and MRR for the parametric level are considered in this study, whereas the percentage contribution of workpiece thickness (T) is more than the arc current (C) and gas pressure (P) in altering burr height.

  • From the behaviour of the slopes of the response parameters vs treatment condition graphs, it is observed that treatment condition 7 is an optimum set of process parameters in altering burr height and MRR. However, for surface roughness, the optimum level of parameter obtained from the S/N ratio and average performance graph analysis is treatment condition 5.

Acknowledgments

The authors express their gratitude to AEC, Guwahati, Assam and MIT, MAHE Bengaluru Campus for providing them with facilities and financial assistance. The authors further express their gratitude for the valuable input and suggestions provided by the anonymous reviewers, guest editor, and editor-in-chief, which greatly contributed to enhancing the quality of the article.

  1. Funding information: The authors state no funding is involved.

  2. Author contributions: Dr. Mridusmita Roy Choudhury: Framing of the manuscript and development of methodology; Hrishikesh Dutta: experimentation; Utpal Deka: result analysis; Basireddy Bhavani: drafting the manuscript; Kuldeep K. Saxena: drafting the manuscript; Anil Borah: guiding throughout the process and sharing of idea and concept.

  3. Conflict of interest: The authors state no conflict of interest.

  4. Data availability statement: All data generated or analyzed during this study are included in this published article.

  5. Authorization for the use of human subjects: Not applicable.

  6. Authorization for the use of experimental animals: Not applicable.

  7. Informed consent (If applicable): Not applicable.

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Received: 2024-01-21
Revised: 2024-02-12
Accepted: 2024-02-20
Published Online: 2024-04-10

© 2024 the author(s), published by De Gruyter

This work is licensed under the Creative Commons Attribution 4.0 International License.

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https://doi.org/10.1515/htmp-2022-0329
Keywords for this article
plasma arc welding; optimization; ANOVA; Taguchi’s orthogonal array
Creative Commons
BY 4.0

Articles in the same Issue

  1. Research Articles
  2. De-chlorination of poly(vinyl) chloride using Fe2O3 and the improvement of chlorine fixing ratio in FeCl2 by SiO2 addition
  3. Reductive behavior of nickel and iron metallization in magnesian siliceous nickel laterite ores under the action of sulfur-bearing natural gas
  4. Study on properties of CaF2–CaO–Al2O3–MgO–B2O3 electroslag remelting slag for rack plate steel
  5. The origin of {113}<361> grains and their impact on secondary recrystallization in producing ultra-thin grain-oriented electrical steel
  6. Channel parameter optimization of one-strand slab induction heating tundish with double channels
  7. Effect of rare-earth Ce on the texture of non-oriented silicon steels
  8. Performance optimization of PERC solar cells based on laser ablation forming local contact on the rear
  9. Effect of ladle-lining materials on inclusion evolution in Al-killed steel during LF refining
  10. Analysis of metallurgical defects in enamel steel castings
  11. Effect of cooling rate and Nb synergistic strengthening on microstructure and mechanical properties of high-strength rebar
  12. Effect of grain size on fatigue strength of 304 stainless steel
  13. Analysis and control of surface cracks in a B-bearing continuous casting blooms
  14. Application of laser surface detection technology in blast furnace gas flow control and optimization
  15. Preparation of MoO3 powder by hydrothermal method
  16. The comparative study of Ti-bearing oxides introduced by different methods
  17. Application of MgO/ZrO2 coating on 309 stainless steel to increase resistance to corrosion at high temperatures and oxidation by an electrochemical method
  18. Effect of applying a full oxygen blast furnace on carbon emissions based on a carbon metabolism calculation model
  19. Characterization of low-damage cutting of alfalfa stalks by self-sharpening cutters made of gradient materials
  20. Thermo-mechanical effects and microstructural evolution-coupled numerical simulation on the hot forming processes of superalloy turbine disk
  21. Endpoint prediction of BOF steelmaking based on state-of-the-art machine learning and deep learning algorithms
  22. Effect of calcium treatment on inclusions in 38CrMoAl high aluminum steel
  23. Effect of isothermal transformation temperature on the microstructure, precipitation behavior, and mechanical properties of anti-seismic rebar
  24. Evolution of residual stress and microstructure of 2205 duplex stainless steel welded joints during different post-weld heat treatment
  25. Effect of heating process on the corrosion resistance of zinc iron alloy coatings
  26. BOF steelmaking endpoint carbon content and temperature soft sensor model based on supervised weighted local structure preserving projection
  27. Innovative approaches to enhancing crack repair: Performance optimization of biopolymer-infused CXT
  28. Structural and electrochromic property control of WO3 films through fine-tuning of film-forming parameters
  29. Influence of non-linear thermal radiation on the dynamics of homogeneous and heterogeneous chemical reactions between the cone and the disk
  30. Thermodynamic modeling of stacking fault energy in Fe–Mn–C austenitic steels
  31. Research on the influence of cemented carbide micro-textured structure on tribological properties
  32. Performance evaluation of fly ash-lime-gypsum-quarry dust (FALGQ) bricks for sustainable construction
  33. First-principles study on the interfacial interactions between h-BN and Si3N4
  34. Analysis of carbon emission reduction capacity of hydrogen-rich oxygen blast furnace based on renewable energy hydrogen production
  35. Just-in-time updated DBN BOF steel-making soft sensor model based on dense connectivity of key features
  36. Effect of tempering temperature on the microstructure and mechanical properties of Q125 shale gas casing steel
  37. Review Articles
  38. A review of emerging trends in Laves phase research: Bibliometric analysis and visualization
  39. Effect of bottom stirring on bath mixing and transfer behavior during scrap melting in BOF steelmaking: A review
  40. High-temperature antioxidant silicate coating of low-density Nb–Ti–Al alloy: A review
  41. Communications
  42. Experimental investigation on the deterioration of the physical and mechanical properties of autoclaved aerated concrete at elevated temperatures
  43. Damage evaluation of the austenitic heat-resistance steel subjected to creep by using Kikuchi pattern parameters
  44. Topical Issue on Focus of Hot Deformation of Metaland High Entropy Alloys - Part II
  45. Synthesis of aluminium (Al) and alumina (Al2O3)-based graded material by gravity casting
  46. Experimental investigation into machining performance of magnesium alloy AZ91D under dry, minimum quantity lubrication, and nano minimum quantity lubrication environments
  47. Numerical simulation of temperature distribution and residual stress in TIG welding of stainless-steel single-pass flange butt joint using finite element analysis
  48. Special Issue on A Deep Dive into Machining and Welding Advancements - Part I
  49. Electro-thermal performance evaluation of a prismatic battery pack for an electric vehicle
  50. Experimental analysis and optimization of machining parameters for Nitinol alloy: A Taguchi and multi-attribute decision-making approach
  51. Experimental and numerical analysis of temperature distributions in SA 387 pressure vessel steel during submerged arc welding
  52. Optimization of process parameters in plasma arc cutting of commercial-grade aluminium plate
  53. Multi-response optimization of friction stir welding using fuzzy-grey system
  54. Mechanical and micro-structural studies of pulsed and constant current TIG weldments of super duplex stainless steels and Austenitic stainless steels
  55. Stretch-forming characteristics of austenitic material stainless steel 304 at hot working temperatures
  56. Work hardening and X-ray diffraction studies on ASS 304 at high temperatures
  57. Study of phase equilibrium of refractory high-entropy alloys using the atomic size difference concept for turbine blade applications
  58. A novel intelligent tool wear monitoring system in ball end milling of Ti6Al4V alloy using artificial neural network
  59. A hybrid approach for the machinability analysis of Incoloy 825 using the entropy-MOORA method
  60. Special Issue on Recent Developments in 3D Printed Carbon Materials - Part II
  61. Innovations for sustainable chemical manufacturing and waste minimization through green production practices
  62. Topical Issue on Conference on Materials, Manufacturing Processes and Devices - Part I
  63. Characterization of Co–Ni–TiO2 coatings prepared by combined sol-enhanced and pulse current electrodeposition methods
  64. Hot deformation behaviors and microstructure characteristics of Cr–Mo–Ni–V steel with a banded structure
  65. Effects of normalizing and tempering temperature on the bainite microstructure and properties of low alloy fire-resistant steel bars
  66. Dynamic evolution of residual stress upon manufacturing Al-based diesel engine diaphragm
  67. Study on impact resistance of steel fiber reinforced concrete after exposure to fire
  68. Bonding behaviour between steel fibre and concrete matrix after experiencing elevated temperature at various loading rates
  69. Diffusion law of sulfate ions in coral aggregate seawater concrete in the marine environment
  70. Microstructure evolution and grain refinement mechanism of 316LN steel
  71. Investigation of the interface and physical properties of a Kovar alloy/Cu composite wire processed by multi-pass drawing
  72. The investigation of peritectic solidification of high nitrogen stainless steels by in-situ observation
  73. Microstructure and mechanical properties of submerged arc welded medium-thickness Q690qE high-strength steel plate joints
  74. Experimental study on the effect of the riveting process on the bending resistance of beams composed of galvanized Q235 steel
  75. Density functional theory study of Mg–Ho intermetallic phases
  76. Investigation of electrical properties and PTCR effect in double-donor doping BaTiO3 lead-free ceramics
  77. Special Issue on Thermal Management and Heat Transfer
  78. On the thermal performance of a three-dimensional cross-ternary hybrid nanofluid over a wedge using a Bayesian regularization neural network approach
  79. Time dependent model to analyze the magnetic refrigeration performance of gadolinium near the room temperature
  80. Heat transfer characteristics in a non-Newtonian (Williamson) hybrid nanofluid with Hall and convective boundary effects
  81. Computational role of homogeneous–heterogeneous chemical reactions and a mixed convective ternary hybrid nanofluid in a vertical porous microchannel
  82. Thermal conductivity evaluation of magnetized non-Newtonian nanofluid and dusty particles with thermal radiation
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