Development of a Predictive Model to Improve the Hardness of Mild Steel Welded Joint

Structural integrity of weldment is greatly influenced by its process parameters and usually, it is expected for a welded joint to be stronger than its parent metal, but in actual fact, most failures occur at the welded joints and it is mostly due to poor combination of process parameters or inexperience of the welder. This poor combination leads to poor hardness exhibited at the welded joint. The aim of this study is to predict and improve the hardness of mild steel welded zone using the tungsten inert gas (TIG) welding process. Response Surface Methodology (RSM) was employed to analyze the welded response. 200 pieces of mild steel coupons measuring 27.5 × 10 × 10 mm were prepared and used for the experiment, the experiment was performed 20 times, using 5 specimens for each run, after which the hardness was measured and results analyzed respectively. The study produced eighteen (18) optimum results with the best selected to produce a material hardness of 299.269 N/mm 2 with desirability of 95.6%, resulting from current of 120 amp, voltages of 20 and gas flow rate of 12 L/min.


Introduction
The tendencies of any materials to resists penetration, abrasion, scratching or cutting are regarded as hardness. It is the property by which material resists permanent deformation [1]. The automobile and shipbuilding industries employ a substantial amount of mild steel in making parts, some of these parts involve bending and forming. The relative malleability and softness of mild steel materials give room to an outstanding ductility and toughness of the material [2] and 216 Engineering [3]. This also increases its machinability and weldability of the material. Welding is the most extensively used method of metal joining, in various industries like oil and gas, rig design and marine transportation, construction, automobile industries etc. [4]. Due to the quick joining process that creates a permanent waterproof bond and provides better cost saving, its applications are numerous.
Welding operations have an overall weight reduction as compared to other joining methods. The structural integrity of the weldment is greatly influenced by its process parameters and usually, it is expected for a welded joint to be stronger than its parent metal, but in actual fact, most failures occur at the welded joints and it is mostly due to poor combination of poor process parameters or inexperience of the welder [5] and [6]. Poor weld reduces the hardness and scratch resistance of weldment, it also encourages high corrosion activities [7].
It has been proven by several researchers that the choice of welding input process parameters can alter the quality of the weldment, therefore, optimizing these process parameters to obtain the best weld quality and multi-response properties cannot be over emphasized [8] and [9]. This research aims at predicting and optimizing the Hardness of mild steel weld using tungsten inert gas (TIG) welding process and design expert 11.

Materials
The key parameters considered in this work are welding current, welding voltage and gas flow rate [10]. The range of the process parameters is shown in Table 1.
The TIG welding and test were conducted at the Department of Welding and fabrication technology, Petroleum Training Institute (PTI), Warri, Delta State, Nigeria.
The selected input parameters have the upper (+) and lower limits (−). The limits of the four welding variables are shown in Table 1.

Methods
200 pieces of mild steel coupons measuring 27.5 × 10 × 10 mm were used for the experiments, the experiment was performed 20 times, using 5 specimens for each run for hardness test specimen presented in Figure 1. The hardness of the welded specimens was measured by means of Brinell hardness tester. The procedure adopted is as follows:  1) The indenter is pressed into the sample by an accurately controlled test force.
2) The force is maintained for a specific dwell time, normally 10 -15 seconds.
3) After the dwell time is complete, the indenter is removed leaving a round indent in the sample.
4) The size of the indent is determined optically by measuring two diagonals of the round indent using a portable microscope.
5) The Brinell hardness number is a function of the test force divided by the curved surface area of the indent. The indentation is considered to be spherical with a radius equal to half the diameter of the ball. The average of the two diagonals is used in the following formula to calculate the Brinell hardness.

Results
In this study, twenty (20) experimental runs were carried out, each experimental run, comprising the current, voltage and gas flow rate used to join two pieces of mild steel plates measuring 55 mm × 10 mm × 10 mm. The hardness test was measured and results were presented in Table 2.  From the plot presented in Figure 2, the predicted and actaul, have the same minimum value of 220 N/mm 2 and maximum of 320 N/mm 2 . The positive slope with minimal scattering along the slope shows a good agreement between our model and the experimental response.
The 3D surface plot presented in Figure 3, To optimize the Hardness of the material, deign expert 11 interphase for optimization was fixed to maximize in Figure 4 under hardness test.   The interface of numerical optimization is used to define the objective function (minimize or maximize), which is used to defines the lower and upper limit of the response with the level of importance indicated. In other to maximize the material hardness, the weight leans towards the higher limit of 302.162 as seen in Figure 4.
The numerical optimization from Figure 4 was employed in producing eighteen (18) optimal solutions presented in Table 6. From the results in Table 6, it was observed that current of 120 Amp, voltage of 20.00 volt and gas flow rate of 12.00 L/min produced a weld material with good hardness of 299.269 N/mm 2 . This solution obtained was selected by design expert as the optimal solution with a desirability value of 97.30%.
The contour plots showing the hardness response variable of the material against the optimized value of the input variable are presented in Figure 5. To maximize the hardness of the material, the red region on the plot in Figure 5 should be targeted. This region of optimal responses for increased hardness can be achieved by the use of the optimal process parameters of Table 6.

Discussion
The ANOVA in Table 4 shows a P-values of 0.0029 which is less than 0.0500, this indicates a significant model. It was still observed in that same   To maximum the material hardness, the upper limit of 302.162 N/mm 2 in Figure 4 was targeted. This target produced eighteen (18) optimum results in Table 6.
The contour plots in Figure 5 show the effect of voltage and current on the material hardness. It can be deduced from the plot that to maximize the material hardness, effort should be made toward the red region of Figure 5. The eighteen (18) optimum results in Table 6 are all concentrated around the red region on the contour plot. In same table, the optimum result for the material hardness was obtained as current of 120 amp, voltages of 20 and gas flow rate of 12 to produce a material hardness of 299.269 N/mm 2 with desirability of 0.956 or 95.6%. This shows that RSM was robust enough for predicting.

Conclusions
In this study, mathematical model for material hardness is presented in Equation Weld current is found to have a greater influence on the material hardness as compared to voltage and gas flow rate at a moderate level.