CNC Machine Tool Digital Twin for Real-Time Physical Field Rendering
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Solution Overview
Problem
Existing methods for constructing digital twins of computer numerical control machine tools face challenges in real-time data processing and accuracy due to limitations in hardware calculation ability, algorithm complexity, and model complexity, particularly in scenarios with large data sets.
Innovation Solution
A method involving double order reduction is proposed, which includes establishing a finite element analysis grid model, using an improved K nearest neighbor algorithm for sparse grid model creation, and employing a radial basis function to construct a digital twin physical field proxy model. This method ensures real-time stable communication and accurate rendering of the physical field.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If finite element analysis method is used to simulate and analyze physical property state, then the spatial distribution law of physical properties can be fully reflected, but the calculation time is too long to meet real-time requirements
Solution Approach 1:
The patent creates a digital twin copy of the physical machine tool system, including virtual copies of the finite element model, sensor data, and physical field simulations. This virtual copy runs in parallel with the physical system, providing real-time spatial distribution data without requiring actual finite element calculations on the physical hardware, thus resolving the contradiction between accuracy and calculation time.
Solution Approach 2:
The patent pre-processes and pre-calculates finite element models and physical field simulations during system setup and initialization phases. By preparing the computational framework, mesh structures, and baseline simulations in advance, the system eliminates the need for time-consuming real-time finite element calculations during operation, while still maintaining the ability to provide accurate spatial distribution data when needed.
2Measurement precision
If the number of model nodes is increased to improve simulation accuracy, then the spatial distribution precision is improved, but the real-time rendering ability is limited by communication interface and software performance
Solution Approach 1:
The patent segments the physical field simulation into multiple independent components and processing stages, including separate handling of different physical fields (thermal, mechanical, vibrational), different spatial regions, and different time scales. This segmentation allows parallel processing of multiple simulation tasks simultaneously, maintaining high spatial precision while improving overall real-time rendering throughput by distributing computational load across multiple processing channels.
Solution Approach 2:
The patent transitions from traditional three-dimensional spatial modeling to a four-dimensional approach by adding the time dimension as an explicit parameter. This allows the system to pre-calculate and store spatial distribution data at multiple time points, then interpolate or select appropriate time-point data during real-time operation, thereby reducing the computational burden of continuous real-time calculations while maintaining both spatial precision and temporal responsiveness.
3Measurement precision
If the model complexity is increased to improve simulation accuracy, then the physical field representation is more precise, but the algorithm complexity and hardware calculation requirements increase
Solution Approach 1:
The patent introduces intermediate data structures and processing layers between the raw sensor inputs and the final physical field simulations. These intermediaries include pre-processed sensor data fusion modules, simplified boundary condition generators, and adaptive mesh refinement controllers that translate complex physical problems into standardized computational formats, reducing algorithmic complexity while preserving simulation accuracy.
Solution Approach 2:
The patent dynamically adjusts simulation parameters such as mesh density, element types, material property precision, and boundary condition complexity based on the specific operational context, required accuracy level, and available computational resources. This adaptive parameter adjustment allows the system to use simplified models when high precision is not critical and more complex models when accuracy is paramount, optimizing the balance between model complexity and computational efficiency.
Data Source
AI summary
A method of constructing a digital twin physical field of a computer numerical control machine tool with double order reduction is provided. A physical field finite element simulation sample data set in a working state sampling space has an order preliminarily reduced to sparse grid node data via an improved K Nearest Neighbor (KNN) algorithm, so that the number of point sets is kept within the real-time rendering capability range of graphic rendering software. A digital twin physical field proxy model is trained using a radial basis function (RBF) to realize reconstruction of the physical field. The proxy model is iteratively optimized using a genetic algorithm to solve the problem of deviation between a proxy physical field model and a real physical model. Finally, the proxy model is deployed to a digital twin system, and the physical field distribution is solved in real time.