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This study presents a hybrid multi-objective optimization framework for the design of a two-stage helical gearbox, aiming to minimize the base area while maximizing transmission efficiency. The Non-dominated Sorting Genetic Algorithm II (NSGA-II) is employed to generate a diverse Pareto-optimal front of design solutions considering gear ratio distribution and face width coefficients. To identify the best compromise solution from the Pareto front, the Multi-Attribute Utility Theory (MAUT) method is integrated with Entropy weighting, providing a robust and objective ranking of alternatives. The proposed method allows for a comprehensive trade-off analysis between structural compactness and performance. Numerical simulations validate the effectiveness of the hybrid approach, offering practical guidelines for efficient and space-saving gearbox design.

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Design Optimization of a Two-Stage Helical Gearbox: Minimizing Base Area and Maximizing Efficiency via NSGA-II and MAUT–Entropy Integration

  • 会议论文

  • 发表日期：

    2026年

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  • Dinh Van Thanh,
  • Le Thi Phuong Thao,
  • Nguyen Ngoc Thieu,
  • Hoang Xuan Tu

摘要

This study presents a hybrid multi-objective optimization framework for the design of a two-stage helical gearbox, aiming to minimize the base area while maximizing transmission efficiency. The Non-dominated Sorting Genetic Algorithm II (NSGA-II) is employed to generate a diverse Pareto-optimal front of design solutions considering gear ratio distribution and face width coefficients. To identify the best compromise solution from the Pareto front, the Multi-Attribute Utility Theory (MAUT) method is integrated with Entropy weighting, providing a robust and objective ranking of alternatives. The proposed method allows for a comprehensive trade-off analysis between structural compactness and performance. Numerical simulations validate the effectiveness of the hybrid approach, offering practical guidelines for efficient and space-saving gearbox design.