构建用于滑轨剥离强度预测的代用模型的系统框架

IF 4.4 2区 工程技术 Q1 ENGINEERING, MULTIDISCIPLINARY
XingJian Dong, Qian Chen, WenBo Liu, Dong Wang, ZhiKe Peng, Guang Meng
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引用次数: 0

摘要

剥离强度可全面反映滑块轨道的安全性,在汽车座椅安全评估中至关重要。目前确定滑块剥离强度的方法主要是物理测试和数值模拟。然而,这些方法面临着成本高、耗时长等潜在挑战,尚未得到充分解决。因此,高效、低成本的代用模型成为一种很有前途的解决方案。然而,目前使用的代用模型存在数据采样效率低、复杂性高、局部模型预测缺乏稳健性、数据采样与模型预测之间相互隔离等问题。为了克服这些挑战,本文旨在建立一个系统的滑块轨道剥离强度预测框架,包括灵敏度分析、数据集采样和模型预测。具体来说,本文通过可解释线性回归来确定各种几何变量对剥离强度的敏感性。根据变量灵敏度,构建距离度量来衡量不同变量组的差异。然后,提出了稀疏性目标采样(STS),以建立具有代表性的数据集。最后,设计了顺序选择局部加权线性回归(SLWLR)来实现精确的轨道剥离强度预测。此外,利用最小相邻样本距离作为中介,提出了补充数据集的定量成本评估。实验结果验证了顺序选择和加权机制在增强定位鲁棒性方面的功效。此外,就预测性能和数据量要求而言,所提出的 SLWLR 方法超越了类似方法和其他常见的代用方法,在模拟测试数据集中实现了 3.3 kN 的平均绝对误差。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A systematic framework of constructing surrogate model for slider track peeling strength prediction

Peeling strength can comprehensively reflect slider track safety and is crucial in car seat safety assessments. Current methods for determining slider peeling strength are primarily physical testing and numerical simulation. However, these methods encounter the potential challenges of high costs and overlong time consumption which have not been adequately addressed. Therefore, the efficient and low-cost surrogate model emerges as a promising solution. Nevertheless, currently used surrogate models suffer from inefficiencies and complexity in data sampling, lack of robustness in local model predictions, and isolation between data sampling and model prediction. To overcome these challenges, this paper aims to set up a systematic framework for slider track peeling strength prediction, including sensitivity analysis, dataset sampling, and model prediction. Specifically, the interpretable linear regression is performed to identify the sensitivity of various geometric variables to peeling strength. Based on the variable sensitivity, a distance metric is constructed to measure the disparity of different variable groups. Then, the sparsity-targeted sampling (STS) is proposed to formulate a representative dataset. Finally, the sequentially selected local weighted linear regression (SLWLR) is designed to achieve accurate track peeling strength prediction. Additionally, a quantitative cost assessment of the supplementary dataset is proposed by utilizing the minimum adjacent sample distance as a mediator. Experimental results validate the efficacy of sequential selection and the weighting mechanism in enhancing localization robustness. Furthermore, the proposed SLWLR method surpasses similar approaches and other common surrogate methods in terms of prediction performance and data quantity requirements, achieving an average absolute error of 3.3 kN in the simulated test dataset.

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来源期刊
Science China Technological Sciences
Science China Technological Sciences ENGINEERING, MULTIDISCIPLINARY-MATERIALS SCIENCE, MULTIDISCIPLINARY
CiteScore
8.40
自引率
10.90%
发文量
4380
审稿时长
3.3 months
期刊介绍: Science China Technological Sciences, an academic journal cosponsored by the Chinese Academy of Sciences and the National Natural Science Foundation of China, and published by Science China Press, is committed to publishing high-quality, original results in both basic and applied research. Science China Technological Sciences is published in both print and electronic forms. It is indexed by Science Citation Index. Categories of articles: Reviews summarize representative results and achievements in a particular topic or an area, comment on the current state of research, and advise on the research directions. The author’s own opinion and related discussion is requested. Research papers report on important original results in all areas of technological sciences. Brief reports present short reports in a timely manner of the latest important results.
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