Augmented Reality-Based System for Skill Transfer to Assist Workpiece Fixturing on a Machining Center

Machining operations require preparation work called a “setting operation,” such as workpiece fixturing. This operation affects the machining time and machining accuracy and strongly depends on the skill of the operator. Thus, to improve machining efficiency, skill transfer to an unskilled operator via extraction and generalization of the skills regarding the setting operation is desired. In contrast, to acquire the skills of a skilled operator, eye tracking is expected to determine an operator’s attention by estimating the eyeball direction. In addition, augmented reality (AR) technology, which overlays virtual images on the real world, is a promising human-computer interaction technology for skill transfer in industry. Therefore, in this study, an AR-based assist system was developed to demonstrate workpiece fixturing on a machining center. This was achieved by incorporating the skills acquired from interviews based on the cognitive task analysis and gaze data of a skilled operator. Workpiece fixturing was assumed on a machining center, and five types of jigs were prepared. The dimensions of the workpiece and target were first detected. The removal area to be machined was calculated as the difference between the workpiece and target shapes. Workpiece fixturing was determined based on the shape and dimensions of the workpiece, as well as the positional relationships between the jig and removal area, according to the acquired skills. Case studies confirmed that the system improved machining efficiency by reducing the setting operation time.