| Autor: |
Olvera-Romero, Gerardo Daniel, Praga-Alejo, Rolando Javier, González-González, David Salvador |
| Předmět: |
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| Zdroj: |
International Journal of Industrial Engineering; 2023, Vol. 30 Issue 4, p999-1015, 17p |
| Abstrakt: |
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 R² and R²prediction 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 (R² = 0.886) outperforms the non-optimized model (R² = 0.796), linear regression (R² = 0.498), and backpropagation neural networks (R² = 0.641). Additionally, a stability analysis of the proposed model was performed using cross-validation, obtaining an R²prediction = 0.758, which indicates that the genetic algorithm-based method can be a suitable option for modeling complex processes. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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