Adaptive edge finishing process on distorted features through robot-assisted computer vision

Abstract

Robotic deburring has emerged as a transformative solution in finishing processes, offering independence from operator influence to enhance product quality and reduce production costs. To do this, contour tracking is key to ensuring cutting tool contact and obtaining chamfers within narrow tolerances, especially on high added-value parts such as aeronautical turbomachinery. The persistent challenge involves accommodating workpiece variability, including deformations and burrs. Current probe positioning methods lack insights into burr morphology, while optical systems often require expensive scanners, resulting in time-intensive point cloud generation. Therefore, this paper presents an innovative industrial computer vision approach for adaptive deburring of functional edges. By processing the detected contour deviation signal, the proposed method generates tailored toolpaths based on real-time part conditions. The primary contribution lies in its ability to swiftly detect edges and burrs, dynamically adjusting machining toolpaths and conditions based on their dimensions, all through the rapid analysis of a simple image. This approach optimizes deburring operations, directing the cutting tool precisely where it is needed. Consequently, the default NC programming was enhanced with two new sub-programs tailored for deburring with conical tools—one for primary burr removal and another for uniform chamfering of distorted edges. The developed methodology can be easily applied in various industrial environments, offering a rapid and efficient means to improve chamfering of workpieces with variable contours. This research not only presents a practical solution for robotic deburring but also contributes to the advancement of adaptive manufacturing systems.