Optimization of ceramic process parameters supported by ant colony optimization algorithm

Ceramic firing process parameters can directly affect mechanical properties and surface quality of ceramics. To improve the quality of ceramics, the ant colony optimization algorithm is applied. Parameters such as firing temperature, heating rate, and cooling rate are selected. Each ant represents a parameter combination. The initial pheromone is set to a constant. Simulation is carried out by random selection, and the fitness is calculated. Excellent combinations can accumulate more information, which makes subsequent ants tend to choose them and gradually converge to the optimal combination. When the fitness function value is stable, the algorithm terminates and outputs the optimal parameters. Whale optimization algorithm, Bayesian optimization algorithm, and genetic algorithm are used for comparison in the experiment. The results show that the ant colony optimization algorithm is more efficient in finding the optimal solution, reducing the number of iterations by 9% to 23% and saving 5% to 13% in time cost. The optimal solution of the ant colony optimization algorithm is used for firing, with the highest and most stable yield rate reaching 100%. The ant colony optimization algorithm can effectively optimize process parameters, improve ceramic quality, and reduce defects.