Stochastic Optimization of Surface Roughness Using Monte Carlo Algorithms

Abstract In this paper, we investigate the application of Monte Carlo algorithms for the optimization of surface roughness in production processes. Using stochastic methods, a mathematical model was developed that accurately predicts surface roughness based on key processing parameters. Simulations were performed on samples of different materials, where the effects of changes in input parameters on the final roughness were analyzed. The results show that Monte Carlo algorithms can significantly improve the accuracy of process prediction and optimization, enabling a better control of the quality of the final processing. Algorithms are implemented using MATLAB and Python, which enables flexibility and efficiency in data analysis. The results show that Monte Carlo algorithms can significantly improve the accuracy of process prediction and optimization, enabling better control of the quality of finishing. In addition, this approach reduces the need for an experimental approach, resulting in reduced costs and processing time.