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Multiple axis ultra-precision freeform deviation control using encoder data analysis
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AI
DOI：10.1016/j.jmapro.2023.01.079.png)
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Abstract
En 中文
Keen interest in the positioning accuracy of the slow slide servos in ultra-precision machining has allowed for complex and creative features to be machined with optical quality in recent times. Currently, block processing techniques are commonly used to control the position of the tool-workpiece interface when fabricating such intricate features. Using a point cloud to obtain a deterministic position and direction of the tool movement, the machine uses block-processing to look ahead and predict the position of multiple axes in the next few points in preparation to move at the appropriate speeds and times. However, with larger buffer sizes, inconsistencies in the forward read speeds would be directly reflected upon the surface workpiece because of varying positioning velocities. Thus, in this paper, the parameters in block processing and ideal point cloud developments are studied using complex freeform curvilinear features to understand the implications and achieve the desired effects in the actual machining process using block processing. By directly obtaining the encoder positioning data, analysis is done to obtain the machining characteristics. This allows for a model to be developed for deviation control, increasing the overall accuracy of the machined parts.
Keywords:
Ultra -precision machining
Micro features
Data analytics
Machine learning
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