Handle driving forces are the input of the automotive sliding door dynamic system and play an important role for ensuring a smooth closing process during manual sliding door mechanism design. It is important to provide a reliable and accurate input for the manual sliding door mechanism during the design and analysis stage. This paper aims to present an improved K-medoids clustering algorithm to investigate the characteristic of handle driving forces in manually closing an automotive sliding door based on experimental data. The improved K-medoids clustering algorithm includes two stages: observation-based clustering stage and traditional K-medoids clustering stage. In all, 134 subjects have been recruited to manually close the sliding door in the lab and the handle driving force data are collected and processed. The handle driving forces are described in the sliding door coordinate system (XYZ) fixed on the door. This study mainly focuses on the X direction force component clustering analysis. The first stage of the improved algorithm classifies the X direction force components into three clusters based on force curve shapes. Then, each of the above identified three clusters is clustered with the traditional K-medoids clustering algorithm. Results show that the X direction force component has three different shapes: Shape 1—only one crest in the curve, Shape 2—two crests in the curve, and Shape 3—one crest and one trough in the curve. The forces with three different shapes are finally divided into six clusters and the amplitude and time duration are similar for X direction forces within the same cluster and are different in the different clusters. The medoids of these clusters are the mined representative prototypes. Compared to the pure traditional K-medoids algorithm, the improved algorithm can provide much better results that give insights on subjects’ door closing behaviors.

中文翻译：

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基于改进K-medoids算法的汽车手动推拉门关门过程手柄驱动力聚类分析

拉手驱动力是汽车推拉门动力系统的输入，在手动推拉门机构设计中对保证关闭过程的顺利进行起着重要作用。在设计和分析阶段为手动滑动门机构提供可靠和准确的输入非常重要。本文旨在提出一种改进的 K-medoids 聚类算法，以基于实验数据研究手动关闭汽车滑动门时的手柄驱动力特性。改进的K-medoids聚类算法包括两个阶段：基于观测的聚类阶段和传统的K-medoids聚类阶段。总共招募了 134 名受试者在实验室中手动关闭推拉门，并收集和处理手柄驱动力数据。把手驱动力在固定在门上的滑动门坐标系 (XYZ) 中描述。本研究主要针对X方向力分量聚类分析。改进算法的第一阶段根据力曲线形状将 X 方向的力分量分为三个簇。然后，上面确定的三个聚类中的每一个都用传统的 K-medoids 聚类算法进行聚类。结果表明，X方向力分量具有三种不同的形状：形状1——曲线上只有一个波峰；形状2——曲线上有两个波峰；形状3——曲线中的一个波峰和一个波谷。三种不同形状的力最终分为六个簇，同一簇内X方向力的幅度和持续时间相似，不同簇内的力不同。这些集群的中心点是挖掘出的代表性原型。与纯粹的传统 K-medoids 算法相比，改进后的算法可以提供更好的结果，可以深入了解受试者的关门行为。