The classical Preston equation considers that the material removal is linearly related to time, velocity, and pressure. However, in the wheel polishing technology, it is found through experiments that there is a nonlinear relationship between the rotational speed of the polishing wheel and the amount of material removed. In order to accurately control the material removal in the polishing wheel variable speed machining strategy, it is necessary to modify the classical Preston equation. In this paper, the control variable method is used to carry out the sampling experiment: the time and pressure are set as fixed values, and the polishing wheel speed is set as a variable and the value is between 0-4rps. Then the data points were analyzed and a least squares fit was used to obtain a non-linear function between the rotational speed of the polishing wheel and the amount of material removed. Finally, the classical Preston equation is modified to obtain the removal equation suitable for the variable speed machining strategy.
In this paper, industrial robot is used as motion carrier and self-developed flexible wheel tool is used as polishing tool to realize low-cost, high-efficiency, and high-precision optical processing. Firstly, the mapping formula between the workpiece coordinates and the road point coordinates is deduced, and the position and posture data required for robot programming are obtained. Secondly, a new type of wheel polishing tool is designed, which controls the polishing pressure through a pneumatic floating structure to ensure the stability of the removal function. Finally, an off-axis paraboloid of φ345mm was processed using this technology. After three times of processing for 10 hours, the surface error converged from PV-2.111λ, RMS-0.249λ to PV-0.119λ, RMS-0.01λ. PV and RMS converged by 94% and 96%, respectively. This proves that the technology has the advantages of high efficiency and high precision, and is expected to be widely used in the field of precision optical processing.
In this paper, a new type of wheel polishing tool is designed. Through the bevel gear structure, only one motor is used to realize the revolution and rotation of the polishing wheel, which makes the structure simple, small inertia, and stable operation. Traditional polishing wheels have a three-layer structure: internal rigid hub, middle flexible rubber, and external polishing pad. It is found through experiments that the hardness of rubber has a greater influence on the tool influence function (TIF). Therefore, by optimizing the hardness of the rubber, we obtained a TIF very close to the Gaussian shape, which is conducive to the rapid convergence of the surface error. Finally, the effects of polishing wheel speed, polishing pressure and polishing time on the TIF, as well as the stability of the TIF, are studied through experiments. Experiments show that: (1) There is no linear relationship between the removal efficiency of the polishing wheel, the polishing wheel speed, and the polishing pressure, but as the parameter increases, the increase in the removal efficiency slows down; (2) There is a good linear relationship between the removal amount of the polishing wheel and time; (3) The TIF is very stable, and the stability of the TIF reaches 98%.
An ABB IRB6640 industrial robot is used as a processing platform for optical polishing. The relationships of coordinate systems are defined, the algorithm of coordinate transformation, Euler angles and quaternion are provided. M-like removal function and Gaussian-like removal function are used to simulation process an off-axis aspheric surface. The surface error after polishing by M-like removal function is 1.5 to 2.5 times bigger than Gaussian-like removal function. This proves that M-like removal function also has good convergence speed. Then, the pentagram polishing head is used to polish a Φ600mm off-axis paraboloid surface. After 15 cycles, about 120 hours processing, PV converges from 5.8μm to 0.836μm, RMS converges from 1.2μm to 0.054μm, PV and RMS respectively converge 85% and 95%. The experiment shows M-like removal function has good convergence speed.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.