Before and after optimization design variables.

Titanium alloy is known for its low thermal conductivity, small elastic modulus, and propensity for work hardening, posing challenges in predicting surface quality post high-speed milling. Since surface quality significantly influences wear resistance, fatigue strength, and corrosion resistance of parts, optimizing milling parameters becomes crucial for enhancing service performance. This paper proposes a milling parameter optimization method utilizing the snake algorithm with multi-strategy fusion to improve surface quality. The optimization objective is surface roughness. Initially, a prediction model for titanium alloy milling surface roughness is established using the response surface method to ensure continuous prediction. Subsequently, the snake algorithm with multi-strategy fusion is introduced. Population initialization employs an orthogonal matrix strategy, enhancing population diversity and distribution. A dynamic adaptive mechanism replaces the original static mechanism for optimizing food quantity and temperature, accelerating convergence. Joint reverse strategy aids in selecting and generating individuals with higher fitness, fortifying the algorithm against local optima. Experimental results across five benchmarks employing various optimization algorithms demonstrate the superiority of the MSSO algorithm in convergence speed and accuracy. Finally, the multi-strategy snake algorithm optimizes the objective equation, with milling parameter experiments revealing a 55.7 percent increase in surface roughness of Ti64 compared to pre-optimization levels. This highlights the effectiveness of the proposed method in enhancing surface quality.钛合金具有低热导率、小弹性模量且易发生加工硬化，这一特性使得高速铣削后的表面质量预测面临诸多挑战。由于表面质量显著影响零件的耐磨性、疲劳强度与耐腐蚀性能，因此优化铣削参数对提升零件服役性能至关重要。本文提出一种基于多策略融合蛇形算法的铣削参数优化方法，以改善表面质量，其优化目标为表面粗糙度。首先，采用响应面法（response surface method）构建钛合金铣削表面粗糙度预测模型，以实现连续预测。随后，引入多策略融合蛇形算法：种群初始化阶段采用正交矩阵策略，以提升种群多样性与分布均匀性；将原有的静态食物量与温度优化机制替换为动态自适应机制，以加快收敛速度；联合反向策略（Joint reverse strategy）用于筛选并生成适应度更高的个体，增强算法跳出局部最优的能力。通过在5个基准测试集上开展与多种优化算法的对比实验，结果表明所提出的MSSO算法在收敛速度与精度上均具有优越性。最后，利用多策略融合蛇形算法对目标方程进行优化，铣削参数实验结果显示，Ti64钛合金的表面粗糙度较优化前提升了55.7%，这充分验证了所提方法在改善表面质量方面的有效性。