丝杠旋铣预测建模与自适应优化方法

doi:10.3785/j.issn.1008-973X.2025.11.004

浙江大学学报(工学版)

2025, Vol. 59

Issue (11): 2259-2268 DOI: 10.3785/j.issn.1008-973X.2025.11.004

机械工程、能源工程

丝杠旋铣预测建模与自适应优化方法

刘超1,2,3(

),丁浩1,郑娟娟1,4,黄绍服1,3,罗祖青1,沈刚1

1. 安徽理工大学机电工程学院，安徽淮南 232000
2. 重庆大学高端装备机械传动全国重点实验室，重庆 400030
3. 安徽理工大学环境友好材料与职业健康研究院，安徽芜湖 241003
4. 重庆大学数学与统计学院，重庆 400030

Predictive modeling and adaptive optimization method for ball screw whirling milling process

Chao LIU1,2,3(

),Hao DING1,Juanjuan ZHENG1,4,Shaofu HUANG1,3,Zuqing LUO1,Gang SHEN1

1. School of Mechanical and Electrical Engineering, Anhui University of Science and Technology, Huainan 232000, China
2. State Key Laboratory of Mechanical Transmission for Advanced Equipment, Chongqing University, Chongqing 400030, China
3. Institute ofEnvironment-friendly Materials and Occupational Health, Anhui University of Science and Technology, Wuhu 241003, China
4. College of Mathematics and Statistics, Chongqing University, Chongqing 400030, China

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摘要：

针对丝杠旋铣加工参数与各指标之间的高度非线性问题，提出融合改进麻雀搜索算法优化反向传播（ISSA-BP）和非支配排序遗传算法（NSGA-III）的自适应动态优化混合模型. 对比5种改良策略、种群规模及搜索者与警戒者比例对麻雀搜索算法的影响，确定适宜的网络结构，建立4个指标的ISSA-BP预测模型. 通过与其他4种算法的预测性能对比可知，提出的ISSA-BP模型对4个指标的预测相对误差均低于2%，验证了模型的优越性. 将ISSA-BP模型封装嵌入NSGA-III作为适应度预测函数，求解得到帕累托最优解集，为丝杠旋铣加工在提升加工稳定性、保障加工质量方面提供指导.

关键词： 旋风铣削; 预测建模; 多目标优化; 非支配排序遗传算法(NSGA-III)

Abstract:

An adaptive dynamic optimization hybrid model that combined improved sparrow search algorithm for optimizing backpropagation (ISSA-BP) and non-dominated sorting genetic algorithm--III (NSGA-III) was proposed to address the highly nonlinear problem between machining parameters and various indicators in whirling milling. The effect of five improvement strategies, population size, and the ratio of searchers to vigilantes on the sparrow search algorithm was compared, and an appropriate network structure was determined. Then an ISSA-BP prediction model with four indicators was established. The predictive performance of the proposed ISSA-BP model was compared with four other algorithms. The relative prediction errors for all four indicators were less than 2%, verifying the superiority of the model. The ISSA-BP model was encapsulated and embedded into NSGA-III as a fitness prediction function, and the Pareto optimal solution set was solved, which provided guidance for improving machining stability and ensuring machining quality in screw whirling milling.

Key words: whirling milling predictive modeling multi-objective optimization non-dominated sorting genetic algorithm-III(NSGA-III)

收稿日期: 2024-10-27 出版日期: 2025-10-30

TG 62

基金资助: 国家自然科学基金资助项目(52205321, 52275228, U21A20125)；安徽理工大学环境友好材料与职业健康研究院 (芜湖) 资助项目(ALW2021YF06); 安徽理工大学研究生创新基金资助项目(2023CX2077).

作者简介: 刘超（1988—），男，副教授，硕导，从事切削加工动力学的研究. orcid.org/0000-0002-4749-5499. E-mail：liuchaomech@163.com

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引用本文:

刘超,丁浩,郑娟娟,黄绍服,罗祖青,沈刚. 丝杠旋铣预测建模与自适应优化方法[J]. 浙江大学学报(工学版), 2025, 59(11): 2259-2268.

Chao LIU,Hao DING,Juanjuan ZHENG,Shaofu HUANG,Zuqing LUO,Gang SHEN. Predictive modeling and adaptive optimization method for ball screw whirling milling process. Journal of ZheJiang University (Engineering Science), 2025, 59(11): 2259-2268.

链接本文:

https://www.zjujournals.com/eng/CN/10.3785/j.issn.1008-973X.2025.11.004 或 https://www.zjujournals.com/eng/CN/Y2025/V59/I11/2259

图 1 旋风铣削加工的示意图

图 2 切削力和振动采集过程

图 3 粗糙度和残余应力测量装置

表 1 单因素实验方案和结果

表 2 基于单因素实验的样条插值参数及结果

表 3 几种常见的混沌映射

图 4 SSA改进策略的对比

图 5 种群大小对ISSA-BP的影响

图 6 各种搜索者与警戒者比例的模型预测性能

图 7 5种算法在4个指标上的RMSE对比小提琴图

图 8 5种算法模型在4项性能评估标准下的性能比较

图 9 5种算法下R2的比较

表 4 5种算法模型在4个指标上的性能对比

图 10 ISSA-BP-NSGA-III分析的流程图

图 11 Pareto优化前沿图

表 5 Pareto解集

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