Long time series prediction of milling force via a hybrid multi neuro-network-based algorithm

The application of machine learning and deep learning has significantly improved the accuracy and efficiency of cutting force prediction in machining processes. However, challenges such as short prediction period, degradation in accuracy over time, and the risk of overfitting remains. These limitations collectively hinder the reliability and generalizability of artificial intelligence-based force prediction models. To address these issues, this study proposed a novel hybrid multi-neural-network algorithm that integrates convolutional neural networks, long short-time memory, and residual networks to enhance both the accuracy and duration of cutting force prediction. Prior to model training, raw force signals are pre-processed using particle swarm optimization-based variational mode decomposition to effectively eliminate noise and reduce uncertainty. The training and testing datasets are derived from milling experiments conducted under varying cutting parameters, tool types, and sensor configurations to better emulate real-world industrial conditions. Experimental results demonstrate that the hybrid model model can accurately predict cutting forces over a duration exceeding 1 s. The model's higher mean absolute error under varying test conditions suggests good robustness. The proposed data pre-processing phase contributes to a 6.38 % improvement in prediction accuracy. Furthermore, increasing the hyperparameter “timestep” helps mitigate overfitting, with only a minor trade-off in accuracy (less than 5 %). These findings demonstrate the effectiveness of the hybrid algorithm in addressing key limitations of existing models and highlight its potential for robust and generalizable prediction using AI in manufacturing applications.