## 1 Introduction
The optimization of those process parameters has been so far attempted for various steels [13; 14]. Low alloy steels, such as 1.25 Cr-0.5 Mo, are highly suitable for processing components under high pressure and temperature and for further applications in fabrication [15]. The mechanical, physical and chemical properties of this low alloy steel certify a safe working atmosphere, particularly for applications that demand varied temperature cycles. At higher temperatures, the degradation processes are distinct and more severe. As a result, the welding and heat treatment methods are simulated to satisfy the desired qualities [16]. Cr-Mo steels possess good weldability; nevertheless, when exposed to 370-550 \({}^{\circ}\)C for lengthy periods, they tend to undergo temper embrittlement [17]. Gas metal arc welding is the most basic procedure carried out for fabricating solid wires [18; 19; 20]. To solve the productivity issue faced by the fabricators in solid wires, tubular cores wires are used as an alternate option [21]. The benefit of using tubular wires is that they highly increase the current density, which further increases the deposition rates. Metal and flux-cored wires are the only two desired cored wires for the process [22]. The deposition rate is higher in solid wires but the flux-cored wires improve the overall welding quality by filling a shielding flux at the core [23]. The reason why metal-cored wires are the most efficient is that they increase the deposition and current density by filling filler metal at the core. However, WAAM using metal-cored arc welding (MCAW) process has been meagrely attempted as per the author's best knowledge. To obtain the optimized welding parameters, several attempts have been made to apply the principles of meta-heuristics techniques. [PERSON] and [PERSON] [24] compared two models, namely a forward artificial neural network and a direct artificial neural network. The genetic algorithm (GA) and forward artificial neural network (FANN) model were integrated to determine their advantages over the backward artificial neural network (BANN) model. This model was formerly researched in attempts to obtain the desired height of any component and one that can be further used in surface coating and slicing AM. In order to achieve such parameters, the referred arc voltage and wire feed speed were compared to the parameters obtained by the FANN-GA model, which showed that there was minimal spatter and the bead appearance was better than the BANN model. The error involving the bead width (BW), area and bead height (BH) was found to be 3% in FANN-GA and 18% in the BANN model. [PERSON] and [PERSON] [25] conducted WAAM experiments for SS316L material. Here, the deposition rate was kept constant and the arc modes varied. The results showed that SpeedArc and SpeedPulse manufacturing processes were stable and highly efficient. Although the deposition rate of both the processes was the same, the SpeedArc WAAM process depicted a finer solidification structure in comparison with SpeedPulse WAAM structure, owing to their high cooling rate and lower heat input. It was determined that SpeedArc WAAM has higher hardness and tensile strength than SpeedPulse because of the finer solidification structure and lower heat input. More than 540 MPa of ultimate tensile strength was observed along the horizontal direction in both the processes, which helped to understand the formation of ductile fractures and, hence, their toughness value. [PERSON] and [PERSON] [26] used the WAAM technique to improve the structural integrity of aluminium parts. The technique used was pulsed gas metal arc welding, by considering the low heat input requirement for aluminium. The procedure followed during the experimentation was to optimize the input parameters and use robotic operations to predict the bead profile. The results concluded that the voltage range should be between 19.2 and 23.2 V for prismatic blocks. The experimentation would require further in-depth study to produce parts free from volumetric defects.