Modeling of CNC Machine Tool Feed Drive Systems through Position Analysis
Year: 2024
Type: article
Abstract: Methods for identifying the dynamic parameters of machine tool feed drives have been studied to develop simulation models reflecting the machine dynamics. Most studies propose identification methods that use the least squares method, mathematical calculations, and sensors. However, these methods have limitations such as difficulty in accounting for nonlinear friction at low speeds and the requirement for physical modifications. To address these issues, this study proposes a feed drive modeling method that identifies dynamic parameters based on the position differences between the simulation model and machine tool, while minimizing nonlinear friction effects. The identification approach is validated by identifying an arbitrary simulation model and evaluating its accuracy by comparing the position prediction and contour errors between the feed drive simulation model and machine tool. Experimental results confirm that the proposed method, using position, effectively identifies the dynamic parameters of the CNC machine tool feed drive while minimizing friction effects.
Authors: Myungyeon Kim, Jeongmo Kang, Sungchul Jee
Language: English
Subfield: Industrial and Manufacturing Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4405824919