CNC Spindle Thermal Error Prediction via Virtual-Real Twin Transfer
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Solution Overview
Problem
Existing methods for predicting thermal errors in CNC machine tool spindles face challenges due to limited space for sensor arrangement and the inability to measure spindle end due to tight wrapping, leading to inaccurate prediction accuracy.
Innovation Solution
A method utilizing a virtual-real prototype twin feature transfer, involving a spindle physical prototype experiment table, temperature sensitive point screening, and autoregressive distributed lag modeling, coupled with a virtual prototype to establish a thermal error prediction model without requiring displacement sensors on the spindle.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If temperature sensors are arranged outside the spindle to screen temperature sensitive points, then temperature data can be collected for modeling, but the prediction accuracy is insufficient due to limited space and tight wrapping
Solution Approach 1:
The patent creates a virtual prototype that copies the physical spindle structure and thermal characteristics. By establishing a mapping relationship between the virtual prototype and physical spindle, temperature sensitive points are identified on the virtual model and then transferred to the physical system, eliminating the need for complex sensor arrangements on the actual spindle while maintaining high prediction accuracy
Solution Approach 2:
The virtual prototype serves as an intermediary between the physical spindle and the prediction model. It enables the transfer of temperature sensitive point information from the virtual environment to the physical system through established mapping relationships, solving the problem of difficult sensor placement on the tightly wrapped physical spindle
2Measurement precision
If displacement sensors are arranged on the spindle to measure thermal error, then direct measurement is possible, but the tight wrapping of the spindle end prevents sensor placement
Solution Approach 1:
The virtual prototype copies the spindle structure and enables virtual measurement of thermal error at any position, including the spindle end. The measured thermal error from the virtual prototype is then transferred to predict the physical spindle's thermal error, eliminating the need for physical displacement sensor installation on the tightly wrapped spindle
Solution Approach 2:
The patent replaces the mechanical displacement sensing system with a computational approach. Instead of using physical displacement sensors that require mechanical installation, the system uses thermal field simulation and data transfer from the virtual prototype to calculate and predict thermal error, substituting mechanical measurement with computational prediction
3Measurement precision
If more temperature sensors are arranged to improve prediction accuracy, then more temperature data points are available, but the limited space near the spindle prevents additional sensor placement
Solution Approach 1:
The patent moves the sensor arrangement problem from the physical three-dimensional space (where space is limited) to the virtual digital space (where space is abundant). In the virtual prototype, temperature sensitive points can be identified at any location without physical constraints, and these points are then mapped back to the physical system, effectively bypassing the limited physical space constraint
Solution Approach 2:
By creating and analyzing the virtual prototype copy of the spindle, the system can identify numerous temperature sensitive points in the virtual environment without space constraints. These identified points are then transferred to guide minimal sensor placement on the physical spindle, achieving high prediction accuracy without requiring numerous physical sensors
Data Source
AI summary
A method for predicting a thermal error of a spindle of a computer numerical control (CNC) machine tool based on twin feature transferring of a virtual-real prototype, is provided, including: first, building a spindle physical prototype experiment table, and screening temperature sensitive points outside the physical prototype spindle, so as to establish an autoregressive distributed lag model; second, determining a temperature synchronization lag point on the physical prototype spindle corresponding to the temperature sensitive point outside the physical prototype spindle, so as to construct a thermal error analysis model; thereafter, establishing a virtual prototype and transferring the twin feature of the physical prototype, and by integrating a twin coupling relationship between the physical prototype spindle and the virtual prototype spindle, realizing the thermal error prediction. The present disclosure improves the accuracy of the thermal error prediction under the condition that it is difficult to arrange sensors on the spindle.