Research and application of the tool wear prediction system

Introduction to Tool Wear Prediction System

As the direct execution unit in cutting processes, the wear condition of cutting tools directly affects machining quality, efficiency, and cost. Tool wear prediction systems have emerged to address this need, enabling precise monitoring and prediction of tool wear, thus providing strong support for the machining process. Traditional tool wear monitoring methods are mostly based on thresholds or empirical models, which struggle to handle dynamic changes under complex working conditions and have limited prediction accuracy. Modern tool wear prediction systems, however, leverage advanced technologies and algorithms to predict tool wear more accurately.

The Importance of Tool Wear Prediction Systems

Accurate prediction of tool wear is crucial for improving machining quality and efficiency. During machining, tool wear leads to decreased machining accuracy and deteriorated surface quality, ultimately affecting product quality. Tool wear prediction systems can detect tool wear in a timely manner, allowing for proactive measures such as tool replacement and adjustment of machining parameters, thereby ensuring machining quality and efficiency. Furthermore, accurate tool wear prediction can reduce production costs and prevent increased scrap rates and equipment damage caused by excessive tool wear.

Technical Principles of Tool Wear Prediction System

Global attention convolutional neural network

The tool wear prediction model based on Global Attention Convolutional Neural Network (GAM-CNN) is an advanced technique. This model adds a global attention mechanism to the convolutional neural network, using both channel and spatial attention to weighted convergence of features extracted by the convolutional neural network, better highlighting important features and correlating them with the predicted values. For example, in milling experiments, this model can more accurately predict tool wear. Experimental results show that the prediction model has a MAE of 13.83 μm, an RMSE of 17.33 μm, and a MAPE of 12.58%, all outperforming the compared CNN, CAM-CNN, and PSO-SVR models without a global attention mechanism.

Improved aggregation algorithm with Tent-GWO-IBiLSTM

To address the issue of low prediction accuracy caused by the inability of a single global model with fixed parameters to effectively reflect tool degradation patterns in tool wear monitoring, an improved aggregation algorithm (Bagging algorithm) integrating Tent-GWO-IBiLSTM is proposed as a staged tool wear prediction model. First, after signal denoising, multi-channel wear features are extracted. After dimensionality reduction using Spearman coefficients, the extracted features are further reduced to a second dimension based on Friesian distance, ensuring the wear features match the tool wear degradation patterns. Then, the tool wear process is divided into three stages: early, middle, and late wear. Historical data is used to train each stage's improved Long Short-Term Memory (IBiLSTM) tool wear prediction model, and the Tent mapping-improved Grey Wolf algorithm (Tent-GWO) is introduced to optimize the prediction model parameters for each stage. Finally, sample information entropy is introduced to improve the random sampling problem of the Bagging algorithm. The improved Bagging algorithm is then used to integrate the prediction models for each stage, reducing the impact of differences in data distribution across different tools and achieving staged tool wear prediction.

Multi-channel one-dimensional convolutional neural network

To address the issue of historical model failure caused by varying data distributions in tool wear prediction modeling under the same working conditions, a dynamic tool wear prediction modeling method based on a multi-channel one-dimensional convolutional neural network is proposed. The multi-channel one-dimensional convolutional neural network trained on historical tool wear data serves as the initial historical model for tool wear prediction. The maximum mean difference (MMD) method is used to detect the similarity of different tool wear data. When the similarity difference is large, the historical model is iteratively updated, and the updated model then predicts the wear data. Milling experiments verify that this method can accurately predict the wear values ​​of different tools and has good adaptability.

Embedding physical information of symbolic regression into LSTM

A tool wear prediction method based on symbolic regression and LSTM embedding of physical information includes acquiring multi-source sensor signals during CNC machine tool machining, extracting time-domain, frequency-domain, and time-frequency-domain features, and selecting target features strongly correlated with tool wear. A symbolic regression model based on genetic programming is constructed, introducing a comprehensive fitness function including monotonicity constraints and complexity penalties to derive the tool wear distribution function as physical information. This physical information is embedded as a loss function into a stacked LSTM network incorporating an attention mechanism, constructing an Attention-Physics-LSTM model. The Grey Wolf optimization algorithm is used to optimize the hyperparameters of the symbolic regression and LSTM in stages, forming a GAP-LSTM prediction framework. This method exhibits high prediction accuracy, strong generalization ability, and good physical interpretability under complex working conditions.

Application scenarios of tool wear prediction system

Mechanical processing industry

In the machining industry, tool wear prediction systems can monitor tool wear in real time, predict tool life in advance, and avoid machining quality problems caused by tool wear. For example, in the machining of automotive parts, using a tool wear prediction system can ensure the machining accuracy and surface quality of parts, and improve production efficiency.

Aerospace

In the aerospace industry, the machining precision requirements for components are extremely high, and tool wear directly affects the performance and safety of these components. Tool wear prediction systems can help companies detect tool wear in a timely manner, ensuring machining quality and reducing accidents caused by tool problems.

Electronics manufacturing industry

In the electronics manufacturing industry, tool wear affects the machining accuracy and quality of components such as circuit boards. Tool wear prediction systems can provide companies with accurate tool wear information, helping them to rationally schedule tool changeover times and improve production efficiency and product quality.

Development trend of tool wear prediction system

Intelligent

Future tool wear prediction systems will be more intelligent, capable of automatically learning and adapting to different machining conditions and tool types. Through artificial intelligence and machine learning algorithms, the system can continuously optimize the prediction model and improve prediction accuracy.

Integration

The tool wear prediction system will be integrated with other production management systems to achieve data sharing and collaboration. For example, it can be integrated with an enterprise's ERP system to optimize tool inventory management and procurement planning.

Real-time monitoring and early warning

The system will have real-time monitoring and early warning functions, enabling it to promptly detect abnormal wear of cutting tools and issue early warning signals to remind operators to take measures.

Challenges and Solutions for Tool Wear Prediction Systems

Data Acquisition and Processing

Tool wear prediction systems require a large amount of sensor data for accurate predictions, but the data acquisition process may be affected by factors such as noise and interference. The solution is to use advanced sensor technology and data processing algorithms to improve data quality and reliability.

Model adaptability

Different tool types and machining conditions place different demands on prediction models, making model adaptability a challenge. However, the generalization ability and adaptability of a model can be improved by continuously optimizing its structure and parameters.

Cost issues

The high construction and maintenance costs of tool wear prediction systems limit their application in some enterprises. However, the system cost can be reduced and its cost-effectiveness improved by lowering hardware costs and optimizing software algorithms.

In conclusion, tool wear prediction systems play a vital role in modern manufacturing. With continuous technological advancements, these systems will be constantly improved and optimized, providing even stronger support for the development of the manufacturing industry.