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

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

Journal of ZheJiang University (Engineering Science)

2025, Vol. 59

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

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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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)

Received: 27 October 2024 Published: 30 October 2025

CLC:

TG 62

Fund: 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).

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Cite this article:

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.

URL:

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

丝杠旋铣预测建模与自适应优化方法

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)

Fig.1 Schematic diagram of whirling milling processing

Fig.2 Cutting force and vibration acquisition process

Fig.3 Roughness and residual stress measuring device

Tab.1 Plan and result of single-factor experiment

Tab.2 Interpolated parameter and result based on single-factor experiment

Tab.3 Several common chaotic mappings

Fig.4 Comparison of SSA improvement strategies

Fig.5 Effect of population size on ISSA-BP model

Fig.6 Model prediction performance for percentage of various searchers and scouters

Fig.7 Violin plots comparing RMSE of five algorithms across four indicators

Fig.8 Performance comparison of five algorithmic models under four performance evaluation standards

Fig.9 Comparison of R² under five algorithms

Tab.4 Performance comparison of five algorithm models across four indicators

Fig.10 Flowchart of ISSA-BP-NSGA-III analysis

Fig.11 Pareto optimization frontier graph

Tab.5 Pareto solution


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