Improved K-medoids algorithm-based clustering analysis for handle driving force in automotive manual sliding door closing process

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.