This paper presents a critical analysis of the optimization of surface roughness in resin printed parts using metaheuristic algorithms. Surface roughness is a crucial factor affecting the quality and functionality of 3D printed objects. Traditional optimization methods often fail to provide satisfactory results due to the complex nature of the printing process and the numerous parameters involved.

Metaheuristic algorithms, on the other hand, offer a promising approach to tackle this optimization problem. These algorithms are inspired by natural phenomena or social behavior and are known for their ability to find near-optimal solutions in complex and dynamic environments. In this study, three widely used metaheuristic algorithms, namely Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Ant Colony Optimization (ACO), are employed to optimize the surface roughness of resin printed parts.

The experimental setup involves the selection of appropriate input parameters, such as layer thickness, exposure time, and printing speed, which significantly influence the surface roughness. A comprehensive analysis is conducted to evaluate the performance of each algorithm in terms of convergence speed, solution quality, and robustness. The results are compared with those obtained from traditional optimization methods, including gradient-based algorithms and simulated annealing.

The findings reveal that metaheuristic algorithms outperform traditional methods in terms of both solution quality and convergence speed. Among the three metaheuristic algorithms, GA demonstrates the best performance, followed closely by PSO and ACO. Furthermore, sensitivity analysis is conducted to identify the most influential parameters affecting surface roughness, providing valuable insights for process optimization.

Overall, this study highlights the potential of metaheuristic algorithms in optimizing the surface roughness of resin printed parts. The results contribute to the advancement of additive manufacturing techniques by offering an effective and efficient approach to enhance the quality of 3D printed objects. Further research can focus on the application of these algorithms to optimize other important aspects of the printing process, such as mechanical properties and dimensional accuracy.