Development of an Analyzing and Tuning Methodology for the CNC Parameters Based on Machining Performance
Source abstract
Abstract
This paper proposes the development of a tuning methodology which can set the proper values of the Computer Numerical Control (CNC) parameters to achieve the required machining performance. For the conventional operators of machine tools, the CNC parameters were hard to be adjusted to optimal settings, which was a complicated and time-consuming task. To save time in finding optimal CNC parameters, the objective of this research was to develop a practical methodology to tune the CNC parameters effectively for easy implementation in the commercial CNC controller. Firstly, the effect of the CNC parameters in the CNC controller on the tool-path planning was analyzed via experiments. The machining performance was defined in the high-speed (HS) mode, the high-accuracy (HP) mode, and the high-surface-quality (HQ) mode, according to the dynamic errors of several specified paths. Due to the CNC parameters that have a particularly critical effect on the dynamic errors, the relationship between the CNC parameters and the dynamic errors was validated by the measured data. Finally, the tuning procedure defined the anticipated dynamic errors for the three machining modes with the actual machine. The CNC parameters will correspond with anticipated dynamics errors based on several specified paths. The experimental results showed that the HS mode was the fastest to complete the path, and the completion time of the HP and HQ modes were increased by 37% and 6%, respectively. The HP mode had the smallest dynamic errors than other modes, and the dynamic errors of the HS and HQ modes are increased by 66% and 16%. In the HQ mode, the motion oscillation was reduce significantly, and the tracking error of the HS and HP modes were increased by 85% and 28%. The advantage of the methodology is that it simplifies set-up steps of the CNC parameters, making it suitable for practical machine applications.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Advanced machining processes and optimization · Iterative Learning Control Systems · Advanced Numerical Analysis Techniques
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Mechanical Engineering, National Chung Hsing University, 40227 Taichung, Taiwan
References (28)
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