High-Precision machining energy consumption prediction based on multi-sensor data fusion and Ns-Transformer network

Abstract

Achieving energy efficiency and cost-effectiveness in machining relies on accurate predictions of energy consumption. Despite the advancements in deep learning for predictive applications, precise energy modeling through multi-sensor data integration remains challenging, particularly due to the computational demands of large datasets. To address this, an Ns-Transformer-based strategy leveraging multi-sensor data fusion for high-precision energy consumption prediction is proposed. The methodology begins with data preprocessing, incorporating Lagrange interpolation, Butterworth filtering, principal component analysis, and correlation analysis to identify critical features. Key time-series features are then fused with energy consumption data to create an enriched feature space. The fused features are subjected to feature learning through a dual-layer Ns-Transformer network, followed by the application of linear regression to map the energy consumption state, thereby ensuring prediction accuracy. The framework employs distinct models for training and prediction, sharing parameters to reduce computational overhead. Experimental results demonstrate significant accuracy improvements, with mean squared error reductions exceeding 76% for carbon fiber and surpassing 83.2% for plastics, aluminum, and steel.