<p>To synergistically improve machining accuracy, efficiency, and thermal balance in dry hobbing, this paper proposes a multi‑objective optimization method for process parameters. First, the interaction and conflict mechanisms among machining accuracy, efficiency, and thermal balance are systematically analyzed. Building upon this analysis, a mathematical model targeting the integrated optimization of these three objectives is constructed, designating the hob rotational speed and axial feed rate as decision variables while incorporating constraints, such as machine dynamic performance and cutting forces. In view of the multi‑objective and strongly conflicting nature of the model, the MOEA/D algorithm is applied to obtain a uniformly distributed set of non‑dominated solutions on the Pareto front. Furthermore, a multi‑attribute decision‑making mechanism combining the entropy weight method and TOPSIS is introduced to select the optimal combination of process parameters from the Pareto solution set. Finally, shop-floor machining experiments are conducted to compare the optimized parameters with long‑term empirical parameters used in industry. The results show that the optimized solution significantly outperforms the empirical benchmark in terms of workpiece accuracy, machining efficiency, and machine thermal stability, validating the effectiveness and superiority of the proposed method in practical engineering. This research provides a robust solution for dry hobbing process optimization that strikes a balance between theoretical rigor and engineering applicability.</p>