Review of Industrial Robot Stiffness Identification and Modelling
Abstract
:Featured Application
Abstract
1. Introduction
- To improve the stiffness of a robot, manufacturers typically increase the cross-sectional area of the arm, which increases the weight of the robot. In addition, large-part production requires the use of large robots. Due to the gravity of the connecting link and the high load on the EE, each joint of the robot has a large deflection, and the running trajectory of the EE has a large deviation [27,29,30].
- High temperatures usually cause thermal deformation and expansion in robot parts. Model parameter errors can occur during the stiffness identification process [28].
2. Methods for Improving Robot Processing Accuracy
- Online error compensation is difficult to implement, has high hardware requirements, high sensor costs, and a long operating time.
- Sensors have some inherent limitations in the application process due to sensor data noise, communication delays between the sensor and the robot controller, and the interference suppression bandwidth of the robot EE.
- The measurement system is susceptible to external factors, which greatly reduce the measurement accuracy. For example, a chip generated by cutting hinders the real-time measurement of the optical measurement system.
3. FEA
- A well-designed robot CAD model can improve the accuracy and calculation efficiency of the stiffness model. First, a 3D geometric model of each manipulator is established. Then, the transmission components, such as the bearings, motors, and gears, are removed. Mass units are used to replace the matching in each robot joint. This process simplifies the robot joint fit and reduces the calculation. Additionally, parts and features that have little influence on the stiffness of the robot, such as the pinhole and fillet of the non-bearing parts and rounded corners, are simplified.
- The material properties, including the material of the mechanical arm and the type of heat-treatment process, are specific to the CAD model parts of the robot.
- The parameter settings of the simulation model affect the complexity, calculation speed, and accuracy of the system modelling. Generally, reasonable mesh cell type, mesh density, and mesh quality settings not only save calculation costs but also maintain calculation accuracy.
4. MSA
5. VJM
5.1. Single DOF
5.1.1. Linear Stiffness
5.1.2. Nonlinear Stiffness
5.2. Multiple DOFs
5.2.1. Three DOFs
5.2.2. Six DOFs
6. Deflection Measurement
6.1. Laser Tracker
6.2. Optical CMM
- A black and white ring is used as the target for position recognition, which is shown as markers in Figure 15. These markers are normally adopted by 3D camera systems. In addition to the stereo cameras, illuminations are provided to offer appropriate light for marker recognition. According to the measurement volume, the measurement accuracy is up to 6 µm [45].
- An infrared light-emitting diode (LED) is used as the target. As shown in Figure 16, three infrared line scan cameras are used to estimate the 3D position of the LEDs. The product specification of the Nikon Metrology K610 (Nikon, Tokyo, Japan) states a volumetric precision of 60 µm, depending on the measurement situation (distance and inclination towards the camera, ambient light conditions) [44].
7. Conclusions and Outlook
- Simplifying the stiffness identification process while maintaining accuracy.
- In the process of robot processing, there are possible delays from measured wrenches due to the calculated error deformation and the feedback position compensation of the system.
- Position deviations are caused by external wrench impacts when a robot enters and exits the machining process. The dynamic characteristics of the stiffness compensation should be considered.
- Standardization of stiffness modelling. At present, there is no standard procedure for establishing a stiffness model. It is difficult to select and apply one type of modelling method conveniently and quickly. With in-depth research on stiffness modelling methods, a modelling process with standard modelling principles, evaluation indicators, and measuring techniques could be developed.
- Automating measuring and modelling. In terms of actual measurements and stiffness modelling, the workload of manual participation can be reduced, and a set of automated systems for measuring and modelling various scenarios can be developed.
- Application of machine learning (ML) methods for stiffness modelling [43,126,127]. A high-precision stiffness model can be obtained after processing the experimental data with various artificial neural networks (ANNs). A self-learning process for stiffness modelling can also be developed and automated based on ML.
Funding
Conflicts of Interest
References
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Wu, K.; Li, J.; Zhao, H.; Zhong, Y. Review of Industrial Robot Stiffness Identification and Modelling. Appl. Sci. 2022, 12, 8719. https://doi.org/10.3390/app12178719
Wu K, Li J, Zhao H, Zhong Y. Review of Industrial Robot Stiffness Identification and Modelling. Applied Sciences. 2022; 12(17):8719. https://doi.org/10.3390/app12178719
Chicago/Turabian StyleWu, Kai, Jiaquan Li, Huan Zhao, and Yong Zhong. 2022. "Review of Industrial Robot Stiffness Identification and Modelling" Applied Sciences 12, no. 17: 8719. https://doi.org/10.3390/app12178719