Belt grinding is a widely adopted method in processing titanium alloy materials. However, its unique grinding mechanism will greatly affect grinding efficiency. Meanwhile, in order to finish processing task, adopting the method of replacing belts frequently will further make processing time, cost, and energy consumption rise, as well as the pollution during the whole processing. Considering the problems above, first, establish a multi-objective optimization model, which takes spindle rotary speed, feed speed of workpiece, grinding depth, and axial feed as the variables and takes processing time, SEC (specific energy consumption), cost, and environmental impact as objective optimization functions. Secondly, multi-objective particle swarm optimization algorithm is applied to solve resources allocation objectives for Pareto optimal solutions. Then, analytic hierarchy process is applied to analyze the degree of environmental impact of each solution among Pareto optimal solutions and finalize the best optimization solution of belt grinding. Finally, an experiment case is performed to verify the effectiveness of the optimization model. The result shows that, compared with experience-guided parameters, the optimized grinding parameters make processing efficiency improve by 17.3% and SEC declines by 13.2% cost decline by 17.0%. The proposed optimization method of grinding parameters is effective and can be applied to engineering practice. Meanwhile, it can provide a reference to the selection of grinding processing parameters.

砂带磨削是钛合金材料加工中广泛采用的方法。但是，其独特的磨削机理将极大地影响磨削效率。同时，为了完成加工任务，采用频繁更换皮带的方法将进一步增加加工时间，成本和能耗，以及整个加工过程中的污染。考虑到上述问题，首先，建立一个多目标优化模型，该模型以主轴转速，工件进给速度，磨削深度和轴向进给为变量，并以加工时间，SEC（比能耗），成本和环境影响作为目标优化功能。其次，采用多目标粒子群算法求解帕累托最优解的资源分配目标。然后，运用层次分析法对帕累托最优方案中每种方案的环境影响程度进行分析，最终确定砂带磨削的最优方案。最后，通过一个实验案例验证了优化模型的有效性。结果表明，与经验指导的参数相比，优化的磨削参数使加工效率提高了17.3％，SEC降低了13.2％，成本降低了17.0％。提出的磨削参数优化方法是有效的，可应用于工程实践。同时可以为磨削加工参数的选择提供参考。