Digitalization of Human Operations in the Age of Cyber Manufacturing: Sensorimotor Analysis of Manual Grinding Performance

Abstract

This paper presents new techniques to analyze and understand the sensorimotor characteristics of manual operations such as grinding, and links their influence on process performance. A grinding task, though simple, requires the practitioner to combine elements from the large repertoire of his or her skillset. Based on the joint gaze, force, and velocity data collected from a series of manual grinding experiments, we have compared operators with different levels of experience and quantitatively described characteristics of human manual skill and their effects on manufacturing process parameters such as cutting energy, surface finish, and material removal rate (MRR). For instance, we find that an experienced subject performs the task in a precise manner by moving the tool in complex paths, with lower applied forces and velocities, and short fixations compared to a novice. A detailed understanding of gaze-motor behavior broadens our knowledge of how a manual task is executed. Our results help to provide this extra insight, and impact future efforts in workforce training as well as the digitalization of manual expertise, thereby facilitating the transformation of raw data into product-specific knowledge.