Evaluation of microstructural and mechanical qualities in optimised TIG-welded SDSS 2507 joints

The current research utilised a thin sheet of SDSS 2507, with the dimensions of SDSS base metal being 150 mm × 100 mm × 2 mm. The chemical composition of SDSS is presented in Table 1 and the mechanical properties of SDSS 2507 base metal are given in Table 2. Table 3 presents the operational range of variables for the TIG process. The flow chart representing the layout of the study is shown in Figure 1. Figure 2(a) depicts the GTAW experimental setup and Figure 2(b) and (c) shows both the top and bottom beads of an SDSS welded plate, respectively, produced using the TIG process. Prior to welding, the workpiece was cleaned with a brush made of wire. All welding experiments were carried out through the “FRONIUS MAGIC WAVE welder equipment” utilising 2.4 mm size non-consumable tungsten electrodes. It is an autogenous welding process, so no filler material was required. Numeric Control was used along with the welding equipment to regulate the rate of welding. Clean argon gas (99.99%) was employed for welding to improve the arc consistency and prevent the pool of welding fluid against environmental contaminants (such as oxidation). Equation (1) was used to calculate the heat input (Q) of the TIG process. (1) Q = ( η × I × V ) / S , Q=(\eta \times I\times V)/S,

Figure 1

Layout of the research.

Figure 2

(a) TIG experimental setup. SDSS plate welded by the TIG process: (b) top bead and (c) bottom bead.

where Q represents the heat input (J/mm), I is the welding current (A), ƞ is the welding efficiency, V is the arc potential, and S is the welding rate (mm/s).

Microstructural examination of SDSS base metal and welded joints was carried out. The samples were consistently abraded using silicon carbide emery papers with a precision of up to 2,000. Abrasion eliminates all surface defects, producing an even as well as equal finish. After abrasion, the specimens were then polished with alumina powder and diamond paste (1 µm) to achieve a mirror finish. Following polishing the specimens to the desired level of softness, they were etched to reveal the internal microstructure [38]. The etching method used Kalling’s solution, which is a mixture of 100 mL of ethanol, 5 g copper chloride (CuCl₂), and 100 mL of hydrochloric acid. The samples were carefully immersed in the etching solution, which targeted the grain boundaries. Finally, the etched samples were analysed under an optical microscope (LEICA-DM750) to determine their microstructures, which included grain size, phase distribution, and any possible defects. The elemental analysis of the SDSS base metal with TIG welded joints was carried out by energy-dispersive X-ray spectroscopy (EDX) using a ZEISS EVO MA15 scanning electron microscope coupled with an EDX sensor. The scanning electron microscope was operated in high-vacuum mode at a voltage gradient of 20 kV, which was tuned towards effective X-ray production and elemental analysis.

Mechanical tests were carried out for SDSS base metal as well as TIG welded joints. The tensile test was conducted with a universal testing machine (UTM) (Tinius Olsen) at a strain rate of 1 mm/min, which is illustrated in Figure 3(a), and the tensile test sample before fracture is shown in Figure 3(b). The test specimens were developed in line with the requirements of the ASTM E8/E8M to assure excellent dimension conformity [39]. The specimens were set in the UTM and produced a uniaxial tensile force till fracture. Data were obtained to create stress–strain graphs for yield strength, tensile strength, and elongation. The broken tensile test specimens were evaluated with the help of a Hitachi S-3000N scanning electron microscope for analysing the tensile test fracture surfaces and establishing the failure mode, offering details about the material’s mechanical behaviour and the quality of the weld. The microhardness of FZ and HAZ was determined using a Vickers hardness tester; the testing began in the middle of the metal that is being welded and moved on to the base metal through HAZ. Three independent locations were determined at every stage of every test area using a 500 g load with 10 s of dwell time. The final outcome was determined from the average of multiple readings to provide accurate and reliable data. For each characterisation, three samples were tested for getting consistent results.

Figure 3

(a) UTM for tensile tests and (b) tensile test sample before fracture.

The Taguchi method is a commonly used optimization technique that improves the efficiency and performance of structures, processes, and products by reducing trial duration and expenses. It optimises the response by fitting an equation with polynomials to the experimental data, and it is ideal for modelling and analysing issues with several variables influencing the outcome. This strategy employs carefully planned trials to discover the optimal conditions by calculating the impact of TIG parameters and the way these interact with each other. The methodology employs ANOVA to get the most essential parameter. It also entails building response interfaces to understand whether the factors influence the response. Using Taguchi, this research determined the optimal process parameters by altering four variables that significantly affect the caliber of the weld durability [40]. The TIG parameters used include the welding current, arc potential, feed rate, and argon gas flow. The experiments were designed with a fundamental compound structure to provide reliable and durable outcomes, and the experimental layout was carefully developed.

ANOVA was employed to make a prediction regarding the relevance of the TIG parameters. It is useful for finding out whether various parameters affect the output variable. It analyses mean squares with error from experiment predictions across different confidence levels to identify the value of the major components and their interactions. This study analyses the peak strength of the welded joints on tensile strength, elongation (%), and micro-hardness. Table 4 lists the TIG process parameters utilised in the bead on plates’ TIG process. The property of a matrix that contrasts the mean to variance with the optimal value is the S/N ratio. Equations (2)–(4) can be utilised to calculate the signal-to-noise (S/N) ratio. By assessing the mean and S/N ratio utilising the output variables – tensile strength, elongation (%), and microhardness – it is possible to ascertain the optimal joint integrity as well as the TIG process parameters that affect the welded joints’ quality. The mean and S/N ratio were calculated to assess whether each process factor affects the joints that are welded. Utilising the nominal is better (micro-hardness) and larger the better (tensile strength elongation) standards, the mean and S/N ratio were selected in this study to ascertain the response of the process parameters. These variables determine the joint strength of the welded component, and they were evaluated using MINITAB software.

Nominal is best for microhardness [41]: (2) S N ratio = − 10 log 10 1 n ∑ b = 1 n ( y b − y ′ ) 2 . \frac{S}{N}\text{ratio}=-10\hspace{.25em}\log \hspace{.25em}10\left(\frac{1}{n}\right)\mathop{\sum }\limits_{b=1}^{n}{(yb-y^{\prime} )}^{2}.

Larger the better for tensile test (UTS, elongation) [41]: (3) S N ratio = − 10 log 10 1 n ∑ b = 1 n 1 y b 2 . \frac{S}{N}\text{ratio}=-10\hspace{.25em}\log \hspace{.25em}10\left(\frac{1}{n}\right)\mathop{\sum }\limits_{b=1}^{n}\frac{1}{y{b}^{2}}.

Smaller the better for corrosion rate and power consumption [41]: (4) S N ratio = − 10 log 10 1 n ∑ b = 1 n y b 2 , \frac{S}{N}\text{ratio}=-10\hspace{.25em}\log \hspace{.25em}10\left(\frac{1}{n}\right)\mathop{\sum }\limits_{b=1}^{n}y{b}^{2}, where n is the total amount of measurements, yb is the obtained data, and y′ is the mean of the previously obtained experimental data.

The butt joints of the SDSS (thin) material sheet were produced by changing the welding process parameters, including the welding current, arc potential, feed rate, and argon gas flow rate. Table 5 provides the values of all input parameters and responses (UTS, elongation [%], and microhardness).

Tables 6–8 present the response data for S/N ratios (bigger is better) of UTS and elongation (%), as well as the response chart for S/N ratios (smaller is better) of micro-hardness. The effect of welding current greatly impacts micro-hardness, suggesting that welding current is a predominant factor making the welded joints. The elevated cooling rate at increased thermal energy input may be the root cause of this occurrence. The elevated cooling rate resulted in a finer grain structure, hence improving the mechanical characteristics of the welds, such as UTS and elongation (%). Therefore, enhanced weld reinforcing was achieved by increased weld penetration at lower feed rates and elevated welding current. Consequently, higher welding currents led to increased UTS and elongation. ANOVA was conducted using Minitab software at 95% confidence level (α = 0.05) to identify the significant controllable factor and its corresponding contribution to the response variables. The optimal settings were a welding current of 75 A, an arc potential of 15 V, a feed rate of 120 mm/min, and an argon gas flow rate of 12 L/min. ANOVA findings are provided in Table 9, showing that welding current contributes the most at 46.95%, followed by argon gas flow rate at 24.56%, feed rate at 13.26%, and arc potential at 10.25%. Figure 4(a)–(c) shows the impact of each parameter on UTS, elongation, and microhardness, respectively.

Figure 4

Contribution of each parameter: (a) UTS, (b) elongation, and (c) microhardness.

The formula for regression correlates the input values of process variables (welding current, arc potential, feed rate, and argon gas flow rate) with the response outcomes (UTS, elongation, and microhardness). The results of this research used Taguchi’s L25 orthogonal arrays; a total of 625 trials and 25 experiments have been conducted. The objective of regression modelling in the present investigation is to determine the responses for middle tests that exclude welded joints. Regression equations are presented in equations (5)–(7). (5) UTS = 0.24445 + 1.24153 welding current – ( − 0.3524622 ) arc potential + 4.132543 feed rate + 0.01564 Argon gas flow, \text{UTS}=0.24445+1.24153\hspace{.25em}\text{welding}\hspace{.25em}\text{current}\hspace{.25em}\mbox{--}\hspace{.25em}(-0.3524622)\text{arc}\hspace{.25em}\text{potential}+4.132543\hspace{.25em}\text{feed}\hspace{.25em}\text{rate}+0.01564\hspace{.25em}\text{Argon}\hspace{.25em}\text{gas}\hspace{.25em}\text{flow,} (6) Elongation = 3.11 − 0.35245 welding current + 1.7845 arc potential + 4.32625 welding speed + 0.035264 Argon gas flow, \text{Elongation}=3.11-0.35245\hspace{.5em}\text{welding}\hspace{.5em}\text{current}+1.7845\hspace{.5em}\text{arc}\hspace{.5em}\text{potential}+4.32625\hspace{.5em}\text{welding}\hspace{.5em}\text{speed}+0.035264\hspace{.5em}\text{Argon}\hspace{.5em}\text{gas}\hspace{.5em}\text{flow,} (7) Hardness = − 19.3275 + 1.39285 welding current − 0.625684 arc potential + 5.05 welding speed + 0.31254 Argon gas flow . \text{Hardness}=-19.3275+1.39285\hspace{.5em}\text{welding}\hspace{.5em}\text{current}-0.625684\hspace{.5em}\text{arc}\hspace{.5em}\text{potential}+5.05\hspace{.5em}\text{welding}\hspace{.5em}\text{speed}+0.31254\hspace{.5em}\text{Argon}\hspace{.5em}\text{gas}\hspace{.5em}\text{flow}\text{.}

The confirmatory test was performed at appropriate circumstances to check the welded joint’s characteristics of quality. Optimal conditions for the confirmation test were 75 A welding current, 15 V arc potential, 120 mm/min feed rate, and 12 L/min argon gas flow rate. The optimised parameters produced an UTS of 789 MPa, an elongation of 24.7%, and a hardness of 325 BHN. The next sections provide a detailed investigation of microstructural analysis, UTS, % of elongation, and microhardness analysis.

The macrostructures of the weld top and root regions are shown in Figure 5. In the welding operation, the weld root sections were melted. The increased grain size absorption occurred within the HAZ and FZ, as shown in Figure 6(a) and (b). It occurs from re-crystallisation of the ferrite framework and participation of heat input and cooling rates. Consequently, acicular secondary austenite develops at the root, while main austenite develops inside the grains. In contrast to the weld top region, the root region produces higher austenite. Three forms of austenite, that is, grain boundary, intra-granular, and Widmänstten austenite occur inside a ferrite matrix which makes up the weld top area, as shown in Figure 6(c). The higher heat input (≥0.778 kJ/mm) produced bigger grains, greater austenite proportion, and slower cooling rate in SDSS welds. The finer grains, less austenite concentration, and more rapid cooling had been the outcomes of the reduced heat input (<0.778 kJ/mm) in SDSS welds. Austenite was produced here within and between grains. Table 10 presents the ferrite level present in the welded joints. In the FZ and the HAZ, a higher ferrite content was noted in the weld top compared to the root region. It is evident that the amount of ferrite volume percentage in the HAZ exceeds that of the FZ. One austenitic stabiliser was nitrogen. The ferrite content of the FZ and HAZ welded by the TIG process was reduced by the addition of nitrogen content (Ar + 2% N). The right austenite–ferrite ratio was obtained, and improved mechanical characteristics were enhanced by increasing the nitrogen content from 2 to 5%. In comparison to 5% nitrogen, 2% nitrogen is more affordable and readily available on the market while still providing the required phase stability. Consequently, based on the results, a gas with 2% nitrogen is adequate to support the equilibrium state.

Figure 5

Macrostructure of the TIG welded joint of the SDSS sheet.

Figure 6

Microstructure of (a) base metal and HAZ, (b) FZ, and (c) enlarged view of FZ.

The SEM micrograph of the TIG-welded component of the SDSS shows a microstructure that is highly characterised. The base metal SEM image shown in Figure 7(a) reveals elongated, acicular ferrite, and austenite phases spread out in the matrix. The welded joint’s SEM study shows clear phase boundaries and areas with needle-like structures related to the Widmanstätten austenite formed during the welding process, as shown in Figure 7(b). The ferrite phase is converted to long grains of Widmanstätten austenite, as indicated in Figure 7(c); the results show that mechanical strength is increased and the margin is decreased in corrosion resistance. Ferrite is resistant to rust, and austenite makes things tougher. The comparative EDX analysis of the SDSS base metal and SDSS welded joint shows slight but substantial elemental composition changes due to TIG welding. Chromium (Cr) must be present to generate the shielding oxide layer, which decreases from 25.50% in the base metal to 23.40% in the welded joint. Nickel (Ni) increases the toughness and ductility, and the austenite phase may become unstable as its Ni percentage decreases from 6.80 to 5.10%. The content of manganese (Mn) increases from 2.47% in the base metal to 3.72% in the weld due to the thermal cycle that assists diffusion, as shown in Figure 8. Mn is a deoxidiser and stabilises weld pools and FZ. The element molybdenum (Mo) improves the welded area pitting resistance by increasing it from 1.55 to 1.59%, which is beneficial in chloride-rich situations. Silicon (Si) decreases from 1.06 to 0.93%, possibly owing to welding intake decreasing the oxidation resistance.

Figure 7

SEM images of (a) base metal and of welded joint’s (b) HAZ and (c) FZ.

Figure 8

Elemental mapping of the base metal and welded joint.

Figure 9(a) presents the fractured tensile test sample of base metal, while Figure 9(b) shows the enlarged view of broken tensile test samples. Figure 9(c) shows the welded tensile test fractured sample, and Figure 9(d) shows the enlarged view of broken welded tensile test samples. The fact that the HAZ represents the weak portion of welds. The UTS of TIG welded joint’s is 789 MPa, as observed by the tensile test. According to the stress–strain curves in Figure 10, the UTS of the base metal typically ranges from 730 to 800 MPa, which is slightly higher than the welded joint. The TIG process lowers the tensile strength through the application of heat input, potentially resulting in microstructural changes, such as coarse grain formation and phase imbalance inside the FZ. The yield strength of the welds is 550 MPa, rendering it consistent with SDSS. The yield strength of the welded joint matches up with that of the base metal, indicating the effectiveness of the TIG process in keeping the material’s mechanical strength. The elongation of the welded joint was recorded at 20%, coming within the usual limit for SDSS. The elongation % indicates that the welded connection has sufficient ductility, making it appropriate for applications requiring some degree of deformation under load. The retention of high yield strength and good elongation suggest that the welding process was well-controlled, with no negative effects on the material’s mechanical properties. The Joint efficiency [(UTS_welds)/(UTS_{base metal}) × 100] achieved for the TIG process was 95.8%. The comparative study of the base metal and welded joint’s UTS, elongation (%), yield strength, and weld efficiency are shown in Figure 11(a)–(d), respectively. The findings of Figure 11(a)–(d) indicate that while tensile and yield strength are slightly lower in the weld than in the base metal, elongation remains similar. Weld efficiency surpasses 95%, indicating satisfactory mechanical reliability. Error bars show variation in data across many samples.

Figure 9

Tensile test samples: (a) base metal, (b) broken base metal, (c) TIG welded, and (d) broken TIG welded joint.

Figure 10

Stress–strain diagram for the SDSS base metal and welded joint tensile test sample.

Figure 11

Bar graph of comparative study of base metal’s and welded joint’s (a) UTS, (b) elongation, (c) yield strength, and (d) weld efficiency.

Figure 12(a) and (c) depicts SEM images of the base metals and the fractured samples from the welded tensile test, respectively. Fracture samples usually have a fibrous and coarse surface marked by numerous small dimples, as illustrated in Figure 12(b) and (d). The formation of these dimples results from nucleation, and the growth and coalescence of tiny voids occur during substantial plastic deformation before fracture. A ductile fracture surface indicates that the material or weld may absorb considerable energy prior to failure.

Figure 12

Fracture surfaces for the tensile test specimens with the (a) base metal, (b) a close-up view of the base metal fracture surface, (c) welded joint, and (d) a close-up view of the welded joint fracture surface.

Figure 13(a) depicts micro-hardness distribution patterns at the junction between joints of the SDSS base metal, HAZ, and FZ. The micro-hardness distribution patterns over the joint interface show that the harness values of the HAZ and FZ of the welded joint exceed that of the base metal. In addition, the hardness value near the FZ boundary increases as a result of growth of hard elements and elemental shift during the TIG process. The fine equiaxed dendritic morphology and enhanced concentration of Ni and Cr in the composition are thought to be responsible for the FZ elevated hardness value. In addition to Cr, Ni, Mo, and Fe components, the fluctuation in the amount of carbon in SDSS welded joints is the key factor contributing to the greatest hardness values in the HAZ and FZ. The HAZ values are around 330 HV, higher than that of the welded joint, in accordance with the observed results in the literature [1]. The SEM image of the indented part is shown in Figure 13(b). The increased inter-pass temperatures increased the hardness values of the FZ and HAZ. One possible reason for this phenomenon is that greater inter-pass temperatures can cause the development of hard and unstable intermetallic phases as a result of slow cooling. Figure 14 shows that as the TIG heat input increases, the welded joint toughness drops. The toughness of the welded joint decreases as the TIG heat input increases. The phase transition and cooling rate variations cause hardness to decrease as the heat input increases. A decrease in hardness is produced by an increase in austenite production, and it occurs when the cooling rate slows due to increased heat input. This results in a drop in ferrite content in the welded joint. The cooling rate is much higher at low heat input because the ferrite–austenite transition is slower than at a high heat input.

Figure 13

Micro-hardness values in the (a) base metal, HAZ, and FZ and (b) SEM image of indentation on the base metal.

Figure 14

Micro-hardness variation with heat input.