Optimizing Two-Stage Gear Design using NSGA-II with MATLAB: Multi-Objective Approach on Mass and Efficiency Trade-Off - Vu, Ngoc Pi; Vu, Duc Binh; Dinh, Van Thanh; Vu, Duong; Huy, Trieu Quy (Engineering, Technology & Applied Science Research, 2025)
This paper presents a comprehensive methodology for the multi-objective optimization of a two-stage spur gear reducer, aiming to minimize mass while maximizing efficiency. A physically grounded mathematical model is constructed to express gearbox mass and efficiency as functions of critical design parameters, including the gear ratio of the first stage (u1) and the face width coefficients of both stages (Xba1, Xba2). The Non-dominated Sorting Genetic Algorithm II (NSGA-II) is implemented in MATLAB to solve the optimization problem across a range of total transmission ratios (uh ∈ [5, 35]). For each , the algorithm produces a Pareto front, which reflects the trade-offs between objectives. Results reveal consistent design trends, with increasing overall transmission ratio leading to reduced mass and improved efficiency. The proposed methodology provides a decision-support tool for gearbox engineers and paves the way for intelligent design frameworks integrating simulation and optimization.