Milling wear prediction using an artificial neural network model

Abstract

The minimization of milling wear from prolonged contact between a tool and a workpiece is crucial in modern manufacturing, where high precision, efficiency, and productivity are required. Therefore, many researchers have developed systems for monitoring milling wear in real-time. However, while the adoption of multiple sensor signals can enhance the training effectiveness of the model, the complex setup process, data extraction, and feature analysis make it challenging to practically apply it for wear monitoring on production lines. Therefore, this study integrates various machining parameters of machine tools and a single type of sensor signal to extract features related to tool wear. These features are utilized as training data for an artificial neural network (ANN) model. The paper proposes a tool wear prediction ANN model based on automatic hyperparameter tuning and transfer learning, incorporating the architecture of Inception, DenseNet, and Layer Normalization. The network structure enhances the training efficiency for target features by expanding and reusing features from a single sensor's data. The combination of automatic hyperparameter tuning with a general optimizer optimizes the ANN architecture to achieve the best tool wear prediction model. Lastly, the proposed model is compared with other models using metrics including Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Coefficient of Determination (R2) to evaluate the model performance. In the results, the proposed model shows the best performance among all the comparing models in all three metrics. The MAE is 0.03527, RMSE is 0.05135, and the R2 is 0.9611. Compared to the common models such as Random Forest, the MAE and RMSE can be reduced by 43.2%, and 43.5% respectively, and a 9.5% R2 performance increase.