## 1 Introduction
In order to address some of the limitations associated with ANN and ANFIS, algorithms such as genetic algorithm (GA) and particle swarm optimization (PSO) are used to train ANN and ANFIS models. GA and PSO are optimization methods that use a fitness function to assess the population arbitrarily generated. They all use homogenous population subgrouping to enhance the quality of the solution by retarding the early clustering of the solution. GA is an approach to addressing the restricted and unrestricted optimization issues found in a biologically inspired natural process. The model alters a population of candidate solutions on a regular basis. GA arbitrarily picks people from the present population to serve as parents for the following generation's offspring during every stage. The population mutates toward an ideal solution throughout generations. PSO, on the other hand, is a computing approach to solving problems by iteratively attempting to improve a candidate solution (particle) in terms of a quality metric. It addresses the problem by generating a population (swarm) of potential solutions, referred to as particles, and shifting them about in the search space using a simple equation based on their position and velocity. PSO particles auto-update using their internal velocity rather than crossover and mutation genetics. Despite the ANN's high success rate in handling complicated problems, [PERSON] et al. [26] pointed out the model's flaws, such as its sluggish learning rate. The study revealed that the PSO algorithm improves the ANN model's performance. As a result, the PSO-ANN hybrid model was developed to estimate the slope's safety factor using data obtained from 85 natural slope sections. [PERSON] et al. [27] used a predictive optimization algorithm combining ANN and GA. The experimental process was carried out with a silicon carbide grinding wheel on a cylindrical grinding machine. Three machining variables were chosen with three distinct ranges of speed, feed, and depth of cut. The authors used both a 3-5-1 ANN-type stand-alone and a hybrid ANN-GA model to forecast the surface roughness in a grinding operation. The results show that the ANN-GA hybrid model performs better than the ANN stand-alone model. The findings from this study demonstrate that the proposed technique might predict the grinding parameters and optimize them. ANN and GA have been employed by [PERSON] [28] to predict the surface roughness of a mill-machined mild steel alloy. For GA, the author randomly chose the population sizes of 40, 60, and 80 at 60 iterations with a crossover percentage of 0.75 and mutation rate of 0.01. As for ANN, the author selected 2 and 10 as the numbers of hidden layers and neurons, respectively. The author also used Levenberg-Marquardt as a training function. After exploring and studying much literature, a point to note is that researchers frequently employ soft computing models for modeling purposes in a variety of fields, including machining, but there are currently no established guidelines that may be used as a foundation for this study to create the ideal model. In the present study, several models, ANN, ANFIS, ANN-PSO, ANN-GA, ANFIS-PSO, and ANFIS-GA, were developed to analyze the prediction of the surface roughness of aluminum Al6061. This paper proposes an approach for the prediction of surface roughness from machining parameters. Parametric analysis has been performed to fine-tune the variables within each model. Variables include the population size, the number of neurons, the membership functions, and the acceleration factors. The choice of these hyperparameters in the next section is based on their recurrence and recommendations in more pieces of literature, as described above, as well as on trial and error. A common practice in many predictions shows that they can be modified to improve the model's performance. [PERSON] and [PERSON].