Classification and Comparative Analysis of Soldering Motions Using Unsupervised Clustering Considering Physical Characteristics

Abstract

Soldering tasks are still often performed manually, and variations in product quality due to physical characteristics and skill levels of operators remain a challenge. In this study, we applied K-means unsupervised clustering to the right-hand second joint angle data computed using MediaPipe to extract patterns in grip styles. By comparing cluster distributions and analyzing cosine similarity, we evaluated differences based on physical characteristics. The results suggest that soldering grip styles may differ depending on physical traits, with the dominant eye showing particularly significant influence. In the future, we aim to utilize the obtained grip vectors to support beginner training and skill acquisition.