基于迭代式局部加权线性回归的汽车座椅滑轨剥离强度预测

doi:10.3901/JME.2025.17.193

机械工程学报 ›› 2025, Vol. 61 ›› Issue (17): 193-204.doi: 10.3901/JME.2025.17.193

扫码分享

基于迭代式局部加权线性回归的汽车座椅滑轨剥离强度预测

陈钱^1,2, 董兴建^1,2, 陈康康^1,2, 刘文博³, 袁顺³, 吴培桂³, 倪洪斌³

1. 上海交通大学机械系统与振动国家重点实验室上海 200240;
2. 上海交通大学振动、冲击、噪声研究所上海 200240;
3. 恺博(常熟)座椅机械部件有限公司苏州 215500

收稿日期:2024-08-29 修回日期:2025-03-25 出版日期:2025-09-05 发布日期:2025-10-24
作者简介:陈钱，男，1999年出生，博士研究生。主要研究方向为旋转机械智能诊断的可解释性研究。E-mail：chenqian2020@sjtu.edu.cn;董兴建(通信作者)，男，1977年出生，博士，副教授，博士研究生导师。主要研究方向为振动分析与控制、振动超材料和结构疲劳分析。E-mail：donxij@sjtu.edu.cn
基金资助:
国家自然科学基金资助项目(12272219)。

Peeling Force Prediction of Automobile Seat Slide Rail Based on Iterative Local Weighted Linear Regression

CHEN Qian^1,2, DONG Xingjian^1,2, CHEN Kangkang^1,2, LIU Wenbo³, YUAN Shun³, WU Peigui³, NI Hongbin³

1. State Key Laboratory of Mechanical System and Vibration, Shanghai Jiao Tong University, Shanghai 200240;
2. Institute of Vibration, Shock and Noise, Shanghai Jiao Tong University, Shanghai 200240;
3. KEIPER Seating Mechanisms Co., Ltd., Suzhou 215500

Received:2024-08-29 Revised:2025-03-25 Online:2025-09-05 Published:2025-10-24

摘要/Abstract

摘要： 汽车座椅滑轨的剥离强度能够全面反映滑轨的安全强度，是汽车座椅安全评估的重要一环。现有的滑轨剥离强度获取方式主要为实物试验法和数值仿真法，但却存在成本高、耗时长、评估不全面等问题。因此，将数值仿真和统计回归相结合，建立包含敏感性分析、数据集构建、回归模型预测的完整汽车座椅滑轨剥离强度预测框架，是解决上述难题的可行途径。具体而言，首先借助可解释的线性回归获取滑轨各几何变量的敏感性；然后以变量敏感性为基础构建评估工况差异程度的距离度量，提出基于稀疏原则的数据集合理构建方案；最后建立迭代式局部加权线性回归实现对滑轨剥离强度的精准预测，并以最近相邻样本距离为桥梁实现数据集补充代价的定量评估。实验结果表明，迭代式工况选取策略和距离加权策略有效降低了局部模型对近邻样本数量的敏感性，所提迭代式局部加权线性回归方法的预测表现优于同类局部回归方法和其他统计学习方法。同时，考虑数据获取成本，以间接对比的方式，验证了所提稀疏原则采样方法在数据集构建中的优越性。最终，所提剥离强度预测框架在150个仿真测试工况的平均误差为3.3 kN、平均误差率为4.3%，在3个实物测试工况的误差在2 kN以内，误差率在4%以内，预测结果较为优异。

关键词: 座椅滑轨, 剥离强度, 数据驱动, 敏感性分析, 局部加权线性回归

Abstract: The peeling force (PF) of the car seat slide rail, an important part of the safety assessment, can comprehensively reflect the safety of the slide rail. The existing PF measuring methods are mainly based on physical tests or numerical simulations, with the problems of high cost, time consumption, incomplete evaluation, and so on. Therefore, we combine numerical simulation and statistical regression to propose a complete PF prediction framework for the automobile seat slide including sensitivity analysis, dataset construction, and force prediction. Firstly, the sensitivity of each geometric variable of the slide rail is obtained by the interpretable linear regression. Secondly, the distance metric is constructed based on the variable sensitivity to evaluate the mutual difference between different working conditions. Thirdly, a reasonable construction scheme of the dataset based on the principle of sparseness is proposed. Finally, iterative local weighted linear regression (ILWLR) is established to achieve an accurate prediction of the slide PF, and the nearest adjacent sample distance is used to quantitatively evaluate the cost of the dataset supplement. Experimental results show that the iterative selection strategy and the weighted distance effectively reduce the model sensitivity to the number of nearest neighbor samples, and the proposed ILWLR method achieves better prediction performance than other local regression methods and statistical learning methods. Besides, considering the cost of data acquisition, an indirect comparison is conducted to verify the superiority of the proposed sparse principle sampling over random sampling in dataset construction. Finally, the mean absolute error (MAE) of the proposed PF prediction framework is 3.3 kN the mean relative error (MRE) is 4.3% in 150 simulated test conditions, and the absolute error is within 2 kN and the relative error is 4% in three physical test conditions, which is excellent for PF prediction task.

Key words: slide rail, peel strength, data driven, sensitivity analysis, locally weighted linear regression

中图分类号:

TH122

陈钱, 董兴建, 陈康康, 刘文博, 袁顺, 吴培桂, 倪洪斌. 基于迭代式局部加权线性回归的汽车座椅滑轨剥离强度预测[J]. 机械工程学报, 2025, 61(17): 193-204.

CHEN Qian, DONG Xingjian, CHEN Kangkang, LIU Wenbo, YUAN Shun, WU Peigui, NI Hongbin. Peeling Force Prediction of Automobile Seat Slide Rail Based on Iterative Local Weighted Linear Regression[J]. Journal of Mechanical Engineering, 2025, 61(17): 193-204.

参考文献

[1] 工业和信息化部, 中国国家标准化管理委员会. GB 15083-2019汽车座椅、座椅固定装置及头枕强度要求和试验方法[S]. 北京: 中国标准出版社, 2019. Ministry of Industry and Information Technology, Standardization Administration of the People`s Republic of China. GB 15083-2019 Strength requirement and test method of automobile seats, their anchorages and any head restraints[S]. Beijing: Standards Press of China, 2019.
[2] 中国汽车工业协会. T/CAAMTB 46-2021 乘用车座椅用长滑轨技术要求和试验方法[S]. 北京: 中国标准出版社, 2021. China Association of Automobile Manufacturers. T/CAAMTB 46-2021 Technical requirements and test methods of long slide for passenger car seat[S]. Beijing: Standards Press of China, 2021.
[3] XIANG X, ZHANG H, ZHANG F, et al. Failure analysis of the conductive slide support rod of section insulator applied in urban rail transport system[J]. Engineering Failure Analysis, 2023, 148: 107217.
[4] OH M, LEE M K, KIM N. Robust design of roll-formed slide rail using response surface method[J]. Journal of Mechanical Science and Technology, 2010, 24(12): 2545-2553.
[5] LIU F P, SHANG J Z, LUO Z R, et al. Design of space sliding rails and mechanical analysis[J]. Applied Mechanics and Materials, 2014, 624: 207-212.
[6] 马雅丽, 叶志明, 孙守林, 等. 基于机床刚度的滑轨联接面性能分析[J]. 组合机床与自动化加工技术, 2013(11): 1-4. MA Yali, YE Zhiming, SUN Shoulin, et al. Research on slideways characteristics based on machine tool stiffness[J]. Modular Machine Tool & Automatic Manufacturing Technique, 2013(11): 1-4.
[7] BAN H, ZHANG Y, FENG S. A data-driven approach for real-time prediction of thermal gradient in engineered structures[J]. Journal of Mechanical Science and Technology, 2022, 36(3): 1243-1249.
[8] WANG L, LIU G, ZHANG C, et al. FEM simulation-based adversarial domain adaptation for fatigue crack detection using lamb wave[J]. Sensors, 2023, 23(4): 1943.
[9] LIU Y, ZHAO Y, LIN Q, et al. DeviationGAN: A generative end-to-end approach for the deviation prediction of sheet metal assembly[J]. Mechanical Systems and Signal Processing, 2023, 204: 110822.
[10] 吴少杰, 刘怀举, 张仁华, 等. 基于正交实验和数据驱动的喷丸表面完整性参数预测[J]. 表面技术, 2021, 50(4): 86-95. WU Shaojie, LIU Huaiju, ZHANG Renhua, et al. Prediction of surface integrity parameters of shot peening based on orthogonal experiment and data-driven[J]. Surface Technology, 2021, 50(4): 86-95.
[11] 陈豪龙, 柳占立. 基于数据驱动模型求解热传导反问题[J]. 计算力学学报, 2021, 38(3): 272-279. CHEN Haolong, LIU Zhanli. Solving inverse heat conduction problems based on data-driven models[J]. Chinese Journal of Computational Mechanics, 2021, 38(3): 272-279.
[12] CHEN J, LI Y, LIU X, et al. A data-driven minimum stiffness prediction method for machining regions of aircraft structural parts[J]. The International Journal of Advanced Manufacturing Technology, 2022, 120(5-6): 3609-3623.
[13] HUI Y, BAI X, YANG Y, et al. A data-driven CUF-based beam model based on the tree-search algorithm[J]. Composite Structures, 2022, 300: 116123.
[14] 蒋茂飞, 许可, 刘亚龙, 等. 一种改进的局部线性回归估计器及其在雷达高度计海况偏差估计中的应用[J]. 电子与信息学报, 2016, 38(9): 2314-2320. JIANG Maofei, XU Ke, LIU Yalong, et al. Improved local linear regression estimator and its application to estimation for radar altimeter sea state bias[J]. Journal of Electronics & Information Technology, 2016, 38(9): 2314-2320.
[15] LIANG J, XIAO K, POINTER M R, et al. Spectra estimation from raw camera responses based on adaptive local-weighted linear regression[J]. Optics Express, 2019, 27(4): 5165-5180.
[16] KISI O, OZKAN C. A new approach for modeling sediment-discharge relationship: Local weighted linear regression[J]. Water Resources Management, 2017, 31(1): 1-23.
[17] GELER Z, KURBALIJA V, RADOVANOVIĆ M, et al. Comparison of different weighting schemes for the kNN classifier on time-series data[J]. Knowledge and Information Systems, 2016, 48(2): 331-378.
[18] HUANG M, LIN R, HUANG S, et al. A novel approach for precipitation forecast via improved K-nearest neighbor algorithm[J]. Advanced Engineering Informatics, 2017, 33: 89-95.
[19] ZHANG S, LI X, ZONG M, et al. Learning k for kNN classification[J]. ACM Transactions on Intelligent Systems and Technology, 2017, 8(3): 1-19.
[20] ZHANG S, LI X, ZONG M, et al. Efficient kNN classification with different numbers of nearest neighbors[J]. IEEE Transactions on Neural Networks and Learning Systems, 2018, 29(5): 1774-1785.
[21] RODRIGUES É O. Combining Minkowski and Chebyshev: New distance proposal and survey of distance metrics using k-nearest neighbours classifier[J]. Pattern Recognition Letters, 2018, 110: 66-71.
[22] YE H J, ZHAN D C, JIANG Y. Fast generalization rates for distance metric learning: Improved theoretical analysis for smooth strongly convex distance metric learning[J]. Machine Learning, 2019, 108(2): 267-295.
[23] 杨德. 试验设计与分析[M]. 北京: 中国农业出版社, 2002. YANG De. Experimental design and analysis[M]. Beijing: China Agriculture Press, 2002.
[24] XU G, WEI H, WANG J, et al. A local weighted linear regression (LWLR) ensemble of surrogate models based on stacking strategy: Application to hydrodynamic response prediction for submerged floating tunnel (SFT)[J]. Applied Ocean Research, 2022, 125: 103228.
[25] CHEN W, XIE X, WANG J, et al. A comparative study of logistic model tree, random forest, and classification and regression tree models for spatial prediction of landslide susceptibility[J]. Catena, 2017, 151: 147-160.
[26] YILMAZ I, KAYNAR O. Multiple regression, ANN (RBF, MLP) and ANFIS models for prediction of swell potential of clayey soils[J]. Expert Systems with Applications, 2011, 38(5): 5958-5966.

基于迭代式局部加权线性回归的汽车座椅滑轨剥离强度预测

Peeling Force Prediction of Automobile Seat Slide Rail Based on Iterative Local Weighted Linear Regression

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

本文评价

[1]	江晟达, 何霁, 郭聪, 李淑慧, 钱昌明. 数据驱动的自洽聚类分析方法及塑性成形高效跨尺度模拟[J]. 机械工程学报, 2025, 61(4): 74-85.
[2]	范志明, 甘磊, 淦志强, 王谙斌, 苏永辉, 吴昊. 基于特征迁移的小样本增材制造疲劳寿命预测方法[J]. 机械工程学报, 2025, 61(17): 255-265.
[3]	刘湘楠, 杨宇鑫, 石伟, 何宽芳. 物理-数据融合驱动的天然橡胶疲劳寿命预测建模方法[J]. 机械工程学报, 2025, 61(13): 293-301.
[4]	吴吉展, 魏沛堂, 吴少杰, 刘怀举, 朱才朝. 航空齿轮钢滚动接触疲劳性能预测与表面完整性优化[J]. 机械工程学报, 2024, 60(8): 81-93.
[5]	魏永峭, 张晋, 王少江, 漆小虎, 郭瑞, 杨海江. 变双曲圆弧齿线圆柱齿轮传动界面磨损规律及敏感性分析[J]. 机械工程学报, 2024, 60(5): 81-94.
[6]	张大禹, 王震坡, 刘鹏, 林倪, 张照生. 新能源汽车动力电池衰退机制与健康状态估计研究概述[J]. 机械工程学报, 2024, 60(22): 241-256.
[7]	朱海华, 王健杰, 李霏, 刘长春, 蔡祺祥. 基于特征选择和SSA-LSTM的车间订单剩余完工时间预测[J]. 机械工程学报, 2024, 60(20): 388-402.
[8]	吕志军, 褚铭, 周亚勤, 肖雷, 李宏亮. 物理模拟与数据驱动混合的Z型立柱片剪切刚度认知预测方法[J]. 机械工程学报, 2024, 60(2): 50-61.
[9]	谭志朴, 秦国华, 娄维达, 吴竹溪. 基于伯努利分布与混合驱动模型的铣削稳定性高精判断方法[J]. 机械工程学报, 2024, 60(11): 318-331.
[10]	邵海东, 肖一鸣, 颜深. 仿真数据驱动的改进无监督域适应轴承故障诊断[J]. 机械工程学报, 2023, 59(3): 76-85.
[11]	张涵寓, 宋周洲, 刘钊, 朱平. 碳纤维复合材料与铝合金胶铆混合连接力学性能可靠性研究[J]. 机械工程学报, 2023, 59(24): 334-343.
[12]	庞晓青, 李佳承, 刘文学, 邓忠伟, 胡晓松. 模型与数据融合的大尺寸快充锂电池分布式温度估计[J]. 机械工程学报, 2023, 59(22): 20-32.
[13]	谷平维, 段彬, 康永哲, 张承慧, 杜春水. 基于随机充电数据的锂离子电池容量在线估计[J]. 机械工程学报, 2023, 59(22): 100-110.
[14]	李敏, 陈涛涛, 鲁森, 杨开明, 朱煜, 胡楚雄. 光刻机超精密运动台数据驱动MIMO定结构前馈控制[J]. 机械工程学报, 2023, 59(21): 99-109.
[15]	胡炳涛, 冯毅雄, 刘继红, 郭伟, 谭建荣. 面向“互联网+”定制产品的智能适应性设计研究[J]. 机械工程学报, 2023, 59(12): 109-125.