Optimization of Redundant Degrees of Freedom in Robotic Flat-End Milling Based on Dynamic Response
Source abstract
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.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Advanced machining processes and optimization · Advanced Surface Polishing Techniques · Manufacturing Process and Optimization
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
State Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
School of Mechanical Engineering, Donghua University, Shanghai 201620, China
Funding
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
References (43)
Show source-provided references
1. A State-of-the-Art Review on Robotic Milling of Complex Parts with High Efficiency and Precision10.1016/j.rcim.2022.102436 ↗
2. Static and Dynamic Stiffness Analyses of Cable-Driven Parallel Robots with Non-Negligible Cable Mass and Elasticity10.1016/j.mechmachtheory.2014.10.010 ↗
3. High Precision and Efficiency Robotic Milling of Complex Parts: Challenges, Approaches and Trends10.1016/j.cja.2020.12.030 ↗
4. Stiffness Analysis and Optimization in Robotic Drilling Application10.1016/j.precisioneng.2017.04.001 ↗
5. Stiffness-Oriented Performance Indices Defined on Two-Dimensional Manifold for 6-DOF Industrial Robot10.1016/j.rcim.2020.102076 ↗
6. A Method for Off-Line Compensation of Cutting Force-Induced Errors in Robotic Machining by Tool Path Modification10.1007/s00170-013-5421-z ↗
7. A Real Time Machining Error Compensation Method Based on Dynamic Features for Cutting Force Induced Elastic Deformation in Flank Milling10.1080/10910344.2017.1402933 ↗
8. Jin, Y., Gu, Q., Liu, S., and Yang, C. (2023). Experimental Investigation and Modeling of Force-Induced Surface Errors for the Robot-Assisted Milling Process. Machines, 11.10.3390/machines11060655 ↗
9. Chatter in Milling with Robots with Structural Nonlinearity10.1016/j.ymssp.2021.108523 ↗
10. Robotic Milling Stability Optimization Based on Robot Functional Redundancy10.1108/IR-06-2023-0130 ↗
11. The Self-Adaptation of Weights for Joint-Limits and Singularity Avoidances of Functionally Redundant Robotic-Task10.1016/j.rcim.2010.08.004 ↗
12. Closed-Form Inverse Kinematics of 6R Milling Robot with Singularity Avoidance10.1007/s11740-010-0283-9 ↗
13. Redundancy and Optimization of a 6R Robot for Five-Axis Milling Applications: Singularity, Joint Limits and Collision10.1007/s11740-012-0362-1 ↗
14. A Local Toolpath Smoothing Method for a Five-Axis Hybrid Machining Robot10.1007/s11431-022-2315-7 ↗
15. Recurrent Neural Network for Kinematic Control of Redundant Manipulators With Periodic Input Disturbance and Physical Constraints10.1109/TCYB.2018.2859751 ↗
16. Determination of Optimum Robot Base Location Considering Discrete End-Effector Positions by Means of Hybrid Genetic Algorithm10.1016/j.rcim.2006.08.003 ↗
17. Positional Error Similarity Analysis for Error Compensation of Industrial Robots10.1016/j.rcim.2016.05.011 ↗
18. A Closed-Loop Error Compensation Method for Robotic Flank Milling10.1016/j.rcim.2019.101928 ↗
19. Path Smoothing and Feed Rate Planning for Robotic Curved Layer Additive Manufacturing10.1016/j.rcim.2020.101967 ↗
20. Posture Optimization Methodology of 6R Industrial Robots for Machining Using Performance Evaluation Indexes10.1016/j.rcim.2017.02.002 ↗
21. Improving the Pose Accuracy of the Delta Robot in Machining Operations10.1007/s00170-016-9955-8 ↗
22. Smoothness-Oriented Path Optimization for Robotic Milling Processes10.1007/s11431-019-1529-x ↗
23. Stiffness Performance Index Based Posture and Feed Orientation Optimization in Robotic Milling Process10.1016/j.rcim.2018.07.003 ↗
24. Quality Certification and Productivity Optimization in Robotic-Based Manufacturing10.3182/20130619-3-RU-3018.00184 ↗
25. Region-Based Toolpath Generation for Robotic Milling of Freeform Surfaces with Stiffness Optimization10.1016/j.rcim.2020.101953 ↗
26. Posture Optimization in Robotic Machining Based on Comprehensive Deformation Index Considering Spindle Weight and Cutting Force10.1016/j.rcim.2021.102290 ↗
27. Posture Optimization in Robotic Drilling Using a Deformation Energy Model10.1016/j.rcim.2022.102395 ↗
28. Chatter Suppression in Robotic Milling by Control of Configuration Dependent Dynamics10.1016/j.procir.2019.04.053 ↗
29. Energy-Efficient Robot Configuration for Assembly10.1115/1.4034935 ↗
30. Stability Optimization in Robotic Milling through the Control of Functional Redundancies10.1016/j.rcim.2017.09.004 ↗
31. Xiong, X., Li, Y., and Qin, H. (2018, January 14–16). Structural Dynamic Performance Evaluation of Industrial Robots Based on Vibration Tests. Proceedings of the 2018 IEEE 4th Information Technology Mechatronics Eng Conference, Chongqing, China.10.1109/ITOEC.2018.8740752 ↗
32. Pose Optimization in Robotic Machining Using Static and Dynamic Stiffness Models10.1016/j.rcim.2020.101992 ↗
33. Optimization of Redundant Degree of Freedom in Robotic Milling Considering Chatter Stability10.1007/s00170-022-09889-4 ↗
34. Pose Optimization in Robotic Milling Based on Surface Location Error10.1115/1.4057055 ↗
35. Modal Mass and Length of Mode Shapes in Structural Dynamics
36. Scaling of Mode Shapes from Operational Modal Analysis Using Harmonic Forces10.1016/j.jsv.2017.06.033 ↗
37. Posture-Dependent Stability Prediction of a Milling Industrial Robot Based on Inverse Distance Weighted Method10.1016/j.promfg.2018.10.104 ↗
38. Rapid Prediction of Posture-Dependent FRF of the Tool Tip in Robotic Milling10.1016/j.rcim.2019.101906 ↗
39. Prediction of the Posture-Dependent Tool Tip Dynamics in Robotic Milling Based on Multi-Task Gaussian Process Regressions10.1016/j.rcim.2022.102508 ↗
40. Altintas, Y. (2012). Manufacturing Automation: Metal Cutting Mechanics, Machine Tool Vibrations, and CNC Design, Cambridge University Press. [2nd ed.].10.1017/CBO9780511843723 ↗
41. Analytical Prediction of Three Dimensional Chatter Stability in Milling10.1299/jsmec.44.717 ↗
42. Updated Semi-discretization Method for Periodic Delay-differential Equations with Discrete Delay10.1002/nme.1061 ↗
43. Machine Tool Chatter and Surface Location Error in Milling Processes10.1115/1.2280634 ↗