# Prediction of Transient Temperature Distributions for Laser Welding of Dissimilar Metals
## 1 Introduction  
Titanium alloy of grade 5 (Ti6 Al4V) and AISI 316L both have some excellent individual properties which make them suitable for different industrial applications. Ti6 Al4V has low density, high specific strength, high erosion resistance, and high operating temperature [1], which make it suitable for aerospace and nuclear industries. However, AISI 316L is cost-effective and has high corrosion resistance. Dissimilar joining of Ti6 Al4V with AISI 316L can be found in different industries such as in petrochemical, aerospace, and nuclear sectors. The main challenge of joining dissimilar materials [2; 3; 4] with differences in properties is the formation of intermetallic compounds (IMCs), which are brittle. There is also a high probability of crack formation due to residual stress generation. Solid-state joining processes such as diffusion bonding and friction stir welding process can be employed to join such materials to reduce IMC formation as it happens at low temperature. The major two drawbacks of the diffusion bonding process are large heat affected area and time-consumption. Friction stir welding is suitable for joining dissimilar metals; however, it even yields satisfactory outcomes for lap joint designs. The fusion welding process increases the chance of IMC formation as it has a high operating temperature [4; 5]. It is very difficult to control the molten pool in the arc welding process and an expensive tantalum or vanadium sheet may be required to join them successfully [5]. High energy beam fusion welding using lasers can reduce HAZ and process time [5; 6]. The formation of IMCs cannot be removed fully, but it can be controlled under optimal welding parameters. A successful metal joint should be the priority for any welding process, but for realizing the process more deeply, numerical modeling is currently required. Laser welding [6; 7; 8] has become popular among other welding processes for joining both similar [9; 10] and dissimilar materials [11; 12]. The main reasons for its popularity are due to the higher precision and reduced HAZ zone during the joining process. Due to the complex nature of the laser welding process and the parameters involved in it, the numerical simulations are thereby desired for elucidating the joining process [13; 14; 15; 16]. Past studies that modeled the laser welding process have aimed to predict the effects of the laser welding process on the resulting welded joints [16; 17; 18; 19; 20]. FE (finite element) simulation software such as ABAQUS, ANSYS, FLUENT, etc., has been used for 2D or 3D analysis of welding processes in different studies [17; 18; 19; 20]. COMSOL [21; 22] is one of the comparatively newer multiphysics software that can simulate the dynamics of the welding process. COMSOL is certainly a friendly choice for combining multiple physics, chosen for modeling and simulation purposes. The main complexity of simulation of the laser welding process is that all of the temperature-related parameters change in a very short span. Using ANSYS, [PERSON] et al. [23] predicted the temperature distribution for dissimilar welding of two different sheets of steel for the arc welding process. The maximum temperature obtained was more than 2100 \({}^{\circ}\)C. A nonsymmetric weld pool shape and temperature distribution was obtained. [PERSON] et al. [24] predicted the temperature distribution in Ti-6 Al-4V welded joints using pulsed laser welding; their FE results matched their experimental data very well. Their results showed that decreasing the welding speed increased the peak value of temperature, while the width of the molten poll decreased. [PERSON] and [PERSON] [25] prepared a numerical model to predict the temperature distribution of two dissimilar steel alloys using ANSYS and validated their model against experimental temperature data collected using a K type thermocouple.