Machined Surface Roughness Prediction Across CNC Tool Degradation
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Solution Overview
Problem
Existing methods for predicting numerical control machined surface roughness suffer from low efficiency, high cost, and poor robustness due to the lack of effective signal feature analysis and fusion, and performance degradation over time due to machine tool wear and aging.
Innovation Solution
A method utilizing attention mechanism and transfer learning to fuse and learn multiple signal features, construct a machine tool single-index degradation model, and perform network model transferring to maintain prediction accuracy over time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If traditional data regression methods are used to predict machining accuracy, then an explicit prediction model can be obtained, but the model has poor robustness and requires re-establishment when experimental conditions change
Solution Approach 1:
The patent transforms the regression problem from predicting continuous roughness values to predicting discrete roughness grades (S0.8, S1.6, S3.2, S6.3, S12.5). This parameter transformation makes the model more robust to condition changes, as categorical predictions are less sensitive to experimental variations than continuous value predictions.
Solution Approach 2:
The patent replaces traditional mechanical regression modeling with a deep learning neural network system that automatically learns features from raw sensor signals. This substitution eliminates the need for manual feature engineering and model re-establishment, providing both explicit prediction capabilities and robustness through automatic feature extraction and adaptive learning.
2Measurement precision
If artificial intelligence methods are used for roughness prediction, then prediction capability is improved, but time-varying features of input signals are not considered resulting in poor robustness
Solution Approach 1:
The patent segments the time-series sensor signals into fixed-length windows (e.g., 1024 samples per window) and processes each window independently through the neural network. This segmentation allows the model to capture local time-varying features while maintaining computational efficiency and robustness to temporal variations in machining conditions.
Solution Approach 2:
The patent employs a dynamic neural network architecture that can adapt to changing machining conditions. The model uses multiple convolutional layers with different kernel sizes to capture dynamic features at multiple time scales, and the attention mechanism dynamically weights important time-varying features, enabling the system to maintain robustness despite temporal variations in input signals.
3Device complexity
If neural network models directly output results after convolution operation, then processing is simplified, but lack of analysis and fusion processing of signal features reduces prediction accuracy
Solution Approach 1:
The patent merges multiple signal sources (vibration, force, acoustic emission) and multiple feature types (time-domain, frequency-domain, time-frequency domain) into a unified multi-channel input structure. The neural network then fuses these features through convolutional operations and fully connected layers, enabling comprehensive analysis of signal characteristics while maintaining manageable complexity through systematic feature integration.
Solution Approach 2:
The patent transforms 1D time-series signals into 2D spectrograms (frequency-time representations) as input to the neural network. This dimensionality transformation enables the model to simultaneously analyze frequency and temporal characteristics of the signals, significantly improving feature extraction capability and prediction accuracy while adding only one dimensional transformation step.
4Measurement precision
If prediction models are built for machine tools in service, then initial prediction accuracy is achieved, but performance degrades over time due to wear and aging
Solution Approach 1:
The patent implements a self-updating prediction system where the neural network continuously learns from new machining data collected during machine tool operation. The system automatically adapts to wear and aging effects by incorporating online learning mechanisms that update model parameters without requiring external intervention or complete model retraining, thereby maintaining prediction accuracy throughout the machine tool's service life.
Solution Approach 2:
The patent incorporates feedback mechanisms where actual roughness measurements are continuously compared with predicted values, and the prediction errors are used to update the neural network model. This closed-loop feedback system enables the model to adapt to changing machine tool conditions over time, compensating for wear and aging effects and maintaining long-term prediction accuracy.
Data Source
AI summary
A method for predicting machined surface roughness of parts based on attention and transfer learning is provided. The method includes the following steps: first, acquiring key physical signals that affect evolution of machined surface roughness of parts of a computer numerical control machine tool and constructing a signal feature matrix; inputting the respective signal feature matrixes into a machined surface roughness prediction model for training, and obtaining a trained machined surface roughness prediction model; constructing a machine tool single-index degradation model, determining a degradation trend of the current computer numerical control machine tool, and in different degradation stages of the degradation trend, performing network model transferring on the machined surface roughness prediction models by using a transferring method separately, so as to obtain the machined surface roughness prediction models in different degradation stages, and realize the machined surface roughness prediction in different degradation stages.