MODELING AND OPTIMIZATION OF AN INTERVAL TYPE 2 FUZZY LOGIC SYSTEM FOR A CERAMIC COATING PROCESS
DOI:
https://doi.org/10.23055/ijietap.2023.30.4.8973Abstract
Process control is essential in Industry 4.0, and process modeling is an effective way to achieve it. For complex processes with high variability and uncertainty, Interval Type 2 Fuzzy Logic Systems are an efficient alternative, but they lack an appropriate methodology for selecting the Footprint of Uncertainty width. This work proposes a method that uses a genetic algorithm to optimize the Footprint of Uncertainty width and evaluates various Type-Reduction methods. ANOVA and R^2 and R_prediction^2 statistics are used to verify the model, which is applied to a manufacturing process that adjusts the density of a ceramic coating. The results indicate that the optimized model (R^2=0.886) outperforms the non-optimized model (R^2=0.796), linear regression (R^2=0.498), and backpropagation neural networks (R^2=0.641). Additionally, a stability analysis of the proposed model was performed using cross-validation, obtaining an R_prediction^2=0.758, which indicates that the genetic algorithm-based method can be a suitable option for modeling complex processes.
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