According to the characteristics of NC milling, an approach for optimization of milling parameters considering high efficiency and low carbon based on gravity search algorithm is proposed. Taking the carbon emission and processing time as the objectives, the cutting rate, feed per tooth, and cutting width as the optimization variables. A multi-objective optimization model of NC milling parameters is established. An non-dominated sorting gravity search algorithm (NSGSA) is used to solve the multi-objective model, and the position update backoff operation is introduced. Finally, taking NC machining process as an example, the multi-objective optimization results and the single objective optimization results are compared respectively, the actual data show that when the optimization objective is high efficiency and low carbon, the processing time and carbon emissions are 173 and 192 respectively. The comparison results show that the combination of processing parameters obtained by multi-objective optimization is the best, the optimal parameter combination obtained by NSGSA algorithm is verified by grey correlation analysis, and the grey correlation degree of the optimal solution set is 0.81, which is the largest in all solution sets. This approach can help the decision-makers flexibly select the corresponding milling parameters, and provide decision-makers with flexible selection decisions suitable for various scenarios.

中文翻译：

---

基于重力搜索算法的高效低碳制粉参数优化

针对数控铣削的特点，提出了一种基于重力搜索算法的高效低碳铣削参数优化方法。以碳排放量和加工时间为目标，切削速度、每齿进给量、切削宽度为优化变量。建立了数控铣削参数的多目标优化模型。采用非支配排序重力搜索算法（NSGSA）求解多目标模型，并引入位置更新退避操作。最后，以数控加工过程为例，分别比较了多目标优化结果和单目标优化结果，实际数据表明，当优化目标为高效低碳时，处理时间和碳排放分别为 173 和 192。对比结果表明，多目标优化得到的加工参数组合效果最好，NSGSA算法得到的最优参数组合经过灰色关联分析验证，最优解集的灰度关联度为0.81，即所有解决方案集中最大的。这种方式可以帮助决策者灵活选择相应的铣削参数，为决策者提供适合各种场景的灵活选择决策。NSGSA算法得到的最优参数组合经灰色关联分析验证，最优解集的灰色关联度为0.81，在所有解集中最大。这种方式可以帮助决策者灵活选择相应的铣削参数，为决策者提供适合各种场景的灵活选择决策。NSGSA算法得到的最优参数组合经灰色关联分析验证，最优解集的灰色关联度为0.81，在所有解集中最大。这种方式可以帮助决策者灵活选择相应的铣削参数，为决策者提供适合各种场景的灵活选择决策。