CNC Tool Remaining Life Prediction With Hybrid Neural Features
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for predicting the remaining life of tools in numerical control machine tools face challenges in accurately handling sparse features, which are crucial for efficient industrial manufacturing and reducing production costs.
Innovation Solution
A hybrid neural model combining a convolutional neural network, a long short-term memory network, and an NFM neural network is constructed to predict tool remaining life, utilizing PLC working condition signal data and vibration and current signals. This model generates feature vectors through convolutional operations, LSTM layers, and Bi-Interaction Pooling layers, with optimization techniques like Dropout and Batch Normalization to improve prediction accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional machine learning methods are used for tool life prediction, then manual feature extraction can be performed, but the prediction accuracy is limited due to inability to capture complex time-space relationships
Solution Approach 1:
The patent combines CNN for spatial feature extraction, LSTM for temporal dependency modeling, and attention mechanisms for feature weighting into a unified hybrid neural network model. This integration allows the system to capture both spatial and temporal characteristics of tool wear signals, significantly improving prediction accuracy while managing model complexity through modular architecture design.
Solution Approach 2:
The patent introduces an attention mechanism as an intermediary component that weights and prioritizes different features and time steps before feeding them to the prediction layer. This attention module acts as a mediator that enhances the relevance of critical features while filtering out noise, thereby improving prediction accuracy without requiring substantial increases in overall model complexity.
2Measurement precision
If deep learning models are used to automatically extract features, then prediction accuracy improves, but the handling of sparse features remains insufficient
Solution Approach 1:
The patent applies preliminary data preprocessing and feature augmentation techniques before feeding data into the deep learning model. This includes handling missing values, interpolating sparse data points, and augmenting feature representations to ensure that the model receives sufficiently dense and reliable input data, thereby improving both prediction accuracy and reliability.
Solution Approach 2:
The patent dynamically adjusts model parameters such as learning rates, batch sizes, and network depth based on the density and quality of input features. For sparse feature conditions, the model adapts by modifying regularization strength and activation functions to prevent overfitting, thereby maintaining reliable predictions even when input data is incomplete or sparse.
3Measurement precision
If complex hybrid neural models are constructed, then prediction accuracy and generalization improve, but computational complexity and training time increase
Solution Approach 1:
The patent segments the hybrid neural network into distinct functional modules: CNN layers for spatial feature extraction, LSTM layers for temporal modeling, and attention modules for feature weighting. Each module is trained separately using preprocessed data, and then the modules are combined and fine-tuned together. This segmented training approach reduces overall training time compared to training a monolithic model from scratch, while maintaining high prediction accuracy.
Solution Approach 2:
The patent performs preliminary feature extraction and data preprocessing using lighter computational models before training the full hybrid network. This pre-processing step reduces the dimensionality and complexity of the input data, allowing the complex hybrid model to train faster and converge more efficiently, thereby reducing training time without sacrificing prediction accuracy.
Data Source
AI summary
Disclosed is a prediction method for tool remaining life of a numerical control machine tool based on a hybrid neural model, including: constructing a hybrid neural network model, specifically including the following steps: constructing sample data according to the sampling frequency of tool data; obtaining a first feature vector representing the tool life by utilizing a convolutional neural network and a long short-term memory network; generating working condition signals of sampling points into a second feature vector representing the tool life by utilizing an NFM neural network; and inputting a current working time of a tool and the acquired feature vectors into a multi-layer perceptron for fusion to predict the tool life.