Personal peculiarity classification of flat finishing motion for skill training by using expanding self-organizing maps

Abstract

The paper proposes an unsupervised classification method for peculiarities of flat finishing motion with an iron file, measured by a 3D stylus. The classified personal peculiarities are used to correct learner’s finishing motions effectively for skill training. In the case of such skill training, the number of classes of peculiarity is unknown. An expanding Self-Organizing Maps is effectively used to classify such unknown number of classes of peculiarity patterns. Experimental results of the classification with measured data of an expert and sixteen learners show effectiveness of the proposed method.