Affiliation:
1. State Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
2. School of Mechanical Engineering, Donghua University, Shanghai 201620, China
Abstract
With the advantages of large working space, low cost and more flexibility, industrial robots have become an important carrier in intelligent manufacturing. Due to the low rigidity of robotic milling systems, cutting vibrations are inevitable and have a significant impact on surface quality and machining accuracy. To improve the machining performance of the robot, a posture optimization approach based on the dynamic response index is proposed, which combines posture-dependent dynamic characteristics with surface quality for robotic milling. First, modal tests are conducted at sampled points to estimate the posture-dependent dynamic parameters of the robotic milling system. The modal parameters at the unsampled points are further predicted using the inverse distance weighted method. By combining posture-independent modal parameters with calibrating the cutting forces, a dynamic model of a robotic milling system is established and solved with a semi-discretization method. A dynamic response index is then introduced, calculated based on the extraction of the vibration signal peaks. The optimization model is validated through milling experiments, demonstrating that optimizing redundant angles significantly enhances milling stability and quality.
Funder
China Postdoctoral Science Foundation
National Key Research and Development Program of China for Robotics Serialized Harmonic Reducer Fatigue Performance Analysis and Prediction and Life Enhancement Technology Research