Simulation models of machine tool feed drive are utilized for process planning and optimization. In order to improve the estimation performance of such models, sophisticated parameter identification methods have been applied to capture the dynamic characteristics of feed drive precisely. However, the identification process, which comprises data gathering, data processing, and parameter estimation, is time-consuming and complex. Therefore, this paper presents a new rapid parameter identification algorithm based on the recursive least squares (RLS) method to identify feed drive mass and sliding friction. The proposed method simplifies the identification process by utilizing the measured data directly without data processing. A two-step identification technique is proposed for addressing the nonlinearity of the feed drive model, thus allowing for the application of the RLS method. The identification accuracy and fast convergence rate of the proposed algorithm were validated via simulations and experiments using a ball screw–driven feed drive testbed. The friction behavior and control characteristics estimated by the feed drive model were compared with experimental results to evaluate the estimation accuracy.
|Number of pages||14|
|Journal||International Journal of Advanced Manufacturing Technology|
|Publication status||Published - 2020 Aug 1|
Bibliographical noteFunding Information:
This work was supported by the Development of Core Industrial Technology Program (20000285, Development of a machine tool intelligence system based on virtual models of the machine structure, control system, and cutting process) funded by the Ministry of Trade, Industry & Energy (MOTIE), Korea. CYL and SHH were partially supported by Korea Institute for Advancement of Technology (KIAT) grant (P0012744, The Competency Development Program for Industry Specialist) funded by MOTIE, Korea.
© 2020, Springer-Verlag London Ltd., part of Springer Nature.
All Science Journal Classification (ASJC) codes
- Control and Systems Engineering
- Mechanical Engineering
- Computer Science Applications
- Industrial and Manufacturing Engineering