## 1 Introduction
Most of them produce cubes and tensile samples with varying SLM parameters, then choose the ones that ultimately result in the maximum density, as determined by Archimedes' density technique. [PERSON] et al. [12] used this process for HY100 steel. [PERSON] et al. [5] also utilized the processes using AlSi10 Mg, and [PERSON] et al. [13] used tungsten. This procedure takes a lot of time and materials to make and test all these cubes; thus, this process is inefficient. Furthermore, it is possible that ideal parameters will not be shown in the end. On the other hand, it has been demonstrated that design of experiment (DOE) methods like the response surface method (RSM) and statistical analysis employing the analysis of variance (ANOVA) are helpful ways to investigate the impact of numerous aspects in material processing applications. The importance of the selective laser sintering (SLS) process factors on surface roughness has been investigated using the response surface design of experiment and ANOVA approach [14]. Similarly, [PERSON] [15] optimized SLM for CMSX-486 Ni-superalloy using the response surface approach and ANOVA methodologies, examining the effects of process parameters (laser power, scan speed, scan spacing, and island size) on crack density and porosity percentage. Optimizing system performance and process yield while keeping costs down are critical goals. The standard procedure for identifying the ideal operating conditions while maintaining the others at a steady level involves a parameter adjustment. The term \"one-variable-at-a-time technique\" describes this. This technique's main drawback is that it excludes the impacts of interactions between the variables and, therefore, fails to show all the effects that the parameters have on the process. Response surface methodology can be utilized in optimization studies to solve this issue. According to [PERSON] et al. [16], RSM is a sort of statistical and mathematical method suitable for the analysis and modeling of problems in which an interest response is affected by various parameters, and the goal is to optimize this response. Ti6 Al4V, Invar36 and SS316L, iron-based powders, managing steel, AlSi12, and other materials were subjected to this process. Using DOE has the advantages of potentially showing the optimal parameters and requiring less time than the previous method. The DOE approach is commonly streamlined using the single scan track (SST) method, which preliminary screens the process parameters. SST refers to a laser track being scanned on a single powder layer that has been previously dispersed over a substrate. This method relies on the concept that powder bed fusion-laser beam/metal (PBF-LB/M) parts are composed of overlapping SSTs; as a result, the geometry of each SST and their interactions with one another determine the part's attributes. When producing the SSTs, only laser power (P) and scanning speed (v) can be changed, but, because of their quick production and analysis, a large variety of their combinations can be investigated. While a DOE of bulk samples is still necessary for full optimization with this methodology, [PERSON] et al. [17] and [PERSON] et al. [18] provided a further simplification of the process by creating a way to determine the best hd value taken straight from SSTs. The optimal hd value is normally obtained from calculating the width of the SSTs and reflecting on the suitable overlapping among them. According to research conducted by [PERSON] et al. [19], the SST technique cannot properly forecast events related to the layer-by-layer scanning typical of PBF-LB/M manufacturing, even while it excludes parameters that might cause scan track problems. This issue is more noticeable in aluminum-based alloys that are prone to cracking. Consequently, it seems that the problem of optimizing the PBF-LB/M process parameters without resulting in huge DOEs of bulk samples remains unsolved.