Advanced Mechanical Gear Design Optimization through Multivariate Statistical Modeling: Strength Analysis and Wear Mitigation Strategies
DOI:
https://doi.org/10.56294/saludcyt20251752Keywords:
wear, modeling, multivariate, optimization, strengthAbstract
Introduction: Mechanical gears are essential components in power transmission in industrial, automotive and aerospace systems. Objective: Advanced optimization of mechanical gear design is explored using multivariate statistical modeling, with a focus on structural strength and wear mitigation. Methodology: The NSGA-II algorithm was applied to identify optimal solutions on the Pareto front, balancing wear minimization and mechanical strength maximization. Results: The results indicate that a modulus close to 5.0 and a pressure angle of 24°-25° optimize durability and gear efficiency. In addition, linear regression showed a high predictive ability (R²=0.882 for wear and R²=0.963 for strength), although limited to a single-objective approach. Conclusion: It is concluded that the combination of NSGA-II with statistical models and numerical simulation can improve the accuracy and applicability of solutions in industrial environments.
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Copyright (c) 2025 Rodrigo Rigoberto Moreno Pallares , Juan Carlos Quinchuela Paucar , Patricia Pilar Moyota Amaguaya , Luis Fernando Buenaño Moyano (Author)
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