A Robotic grinding station based on an industrial manipulator and vision system

Abstract

Due to ever increasing precision and automation demands in robotic grinding, the automatic and robust robotic grinding workstation has become a research hot-spot. This work proposes a grinding workstation constituting of machine vision and an industrial manipulator to solve the difficulty of positioning rough metal cast objects and automatic grinding. Faced with the complex characteristics of industrial environment, such as weak contrast, light nonuniformity and scarcity, a coarse-to-fine two-step localization strategy was used for obtaining the object position. The deep neural network and template matching method were employed for determining the object position precisely in the presence of ambient light. Subsequently, edge extraction and contour fitting techniques were used to measure the position of the contour of the object and to locate the main burr on its surface after eliminating the influence of burr. The grid method was employed for detecting the main burrs, and the offline grinding trajectory of the industrial manipulator was planned with the guidance of the coordinate transformation method. The system greatly improves the automaticity through the entire process of loading, grinding and unloading. It can determine the object position and target the robotic grinding trajectory by the shape of the burr on the surface of an object. The measurements indicate that this system can work stably and efficiently, and the experimental results demonstrate the high accuracy and high efficiency of the proposed method. Meanwhile, it could well overcome the influence of the materials of grinding work pieces, scratch and rust.

Industrial Robots in Mechanical Machining: Perspectives and Limitations

“…Different shape parts, vision system, PC workstation (CPU: i7, GPU: RTX 2070S, RAM: 32G) [26] Robotic grinding is assumed to be an alternative approach in complex component machining because of its flexibility, intelligence, and cost-effectiveness [22]. The paper presents a systematic review of robotic grinding, focusing on accuracy, compliance, and cooperative control.…”

“…A robotic grinding workstation designed for precision and automation in grinding rough metal cast objects is presented in [26]. The system employs machine vision and an industrial manipulator to address challenges in positioning and automatic grinding.…”

A 6DOF pose measurement method for metal casts object based on stereo vision sensor

“…Reflecting light is a common phenomenon in rough machining of metal casts. Limited by materials and process conditions, when measuring metal casts with 2D or 3D vision systems, the sampling information of the vision system is often lost due to the reflection of the object surface, leading to vision system measuring failure (Wan et al , 2021). Due to the change of ambient light and mass production in the measurement, there are obvious differences in the local features of metal casts (especially rough machined metal casts), which obtains poor measurement accuracy and recognition rate, caused by tradition pose measurement algorithms.…”

Automatic Aluminum Alloy Surface Grinding Trajectory Planning of Industrial Robot Based on Weld Seam Recognition and Positioning

“…Wang [7] proposed an automatic grinding system with a fast and accurate robot grinding path planning method for automatic removal of free-form surface irregular weldments. Aiming at complex features such as weak contrast, uneven light, and scarcity in industrial environments, Wan [8] proposed a grinding workstation composed of machine vision and industrial manipulators to solve the problems of positioning and automatic grinding of rough metal castings. The system has high accuracy and efficiency and can well overcome the influence of grinding workpiece material, scratches, rust, and other factors.…”