An Improved Algorithm to Predict the Pose-dependent Cutting Stability in Robot Milling

Chattering is one of the most important factors affecting productivity of robot machining. This paper investigates the pose-dependent cutting stability of a 5-DOF hybrid robot. By merging the complete robot structural dynamics with the cutting force at TCP, an effective approach for stability analysis of the robot milling process is proposed using the full-discretization technique. The proposed method enables the computational efficiency to be significantly improved because the system transition matrix can be simply generated using a sparse matrix multiplication. Both simulation and experimental results on a full size prototype machine show that the stability lobes are highly pose-dependent and primarily dominated by the lower-order structural modes.

Auto-Calibration for Vision-Based 6-D Sensing System to Support Monitoring and Health Management for Industrial Robots

Industrial robots play important roles in manufacturing automation for smart manufacturing. Some high-precision applications, for example, robot drilling, robot machining, robot high-precision assembly, and robot inspection, require higher robot accuracy compared with traditional part handling operations. The monitoring and assessment of robot accuracy degradation become critical for these applications. A novel vision-based sensing system for 6-D measurement (six-dimensional x, y, z, yaw, pitch, and roll) is developed at the National Institute of Standards and Technology (NIST) to measure the dynamic high accuracy movement of a robot arm. The measured 6-D information is used for robot accuracy degradation assessment and improvement. This paper presents an automatic calibration method for a vision-based 6-D sensing system. The stereo calibration is separated from the distortion calibration to speed up the on-site adjustment. Optimization algorithms are developed to achieve high calibration accuracy. The vision-based 6-D sensing system is used on a Universal Robots (UR5) to demonstrate the feasibility of using the system to assess the robot’s accuracy degradation.

Natural Frequency Prediction Method for 6R Machining Industrial Robot

The industrial robot machining performance is highly dependent on dynamic behavior of the robot, especially the natural frequency. This paper aims at introducing a method to predict the natural frequency of a 6R industrial robot at random configuration, for improving dynamic performance during robot machining. A prediction model of natural frequency which expresses the mathematical relation between natural frequency and configuration is constructed for a 6R robot. Joint angles are used as input variables to represent the configurations in the model. The quantity and range of variables are limited for efficiency and practicability. Then sample configurations are selected by central composite design method due to its capacity of disposing nonlinear effects, and natural frequency data is acquired through experimental modal test. The model, which is in form of regression equation, is fitted and optimized with sample data through partial least square (PLS) method. The proposed model is verified with random configurations and compared with the original model and a model fitted by least square method. Prediction results indicate that the model fitted and optimized by PLS method has the best prediction ability. The universality of the proposed method is validated through implementation onto a similar 6R robot.