Pose-Dependent Cutting Force Identification for Robotic Milling

Author:

Hou Maxiao1,Cao Hongrui1,Luo Yang1,Guo Yanjie1

Affiliation:

1. Xi'an Jiaotong University State Key Laboratory for Manufacturing System Engineering, , Xi'an 710049 , China

Abstract

AbstractCutting force identification is critical to improving industrial robot performance and reducing machining vibration. However, most indirect identification methods of cutting force are not applicable since the modal parameters of the robotic milling system vary with the robot pose. This paper presents a novel pose-dependent method to identify the cutting force using the acceleration signal generated by robotic milling. First, the modal parameters at different machining points are employed as a training dataset to develop the Gaussian Process Regression (GPR) model. Next, the modal parameters predicted by the GPR model are employed to optimize the cutting force estimation based on the minimum variance unbiased estimate method. Then, the Kalman filter method is employed to update the covariance matrix of the cutting force identification error and the state estimation error. Lastly, the effectiveness of the proposed method is verified with robotic milling experiments, and the results show that the identification error and time are acceptable under the condition of variable robot pose.

Funder

National Natural Science Foundation of China

Publisher

ASME International

Subject

Industrial and Manufacturing Engineering,Computer Science Applications,Mechanical Engineering,Control and Systems Engineering

Reference26 articles.

1. Active Vibration Suppression in Robotic Milling Using Optimal Control;Nguyen;Int. J. Mach. Tools Manuf.,2020

2. Combined Offline Simulation and Online Adaptation Approach for the Accuracy Improvement of Milling Robots;Zaeh;CIRP Ann.,2020

3. Prediction of the Tool Displacement by Coupled Models of the Compliant Industrial Robot and the Milling Process;Abele,2008

4. The Concept and Progress of Intelligent Spindles: A Review;Cao;Int. J. Mach. Tools Manuf.,2017

5. Prediction of Cutting Forces in Five-Axis Milling Using Feed Drive Current Measurements;Aslan;IEEE/ASME Trans. Mechatron.,2018

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