To address spindle thermal error in precision machining, this study proposes a hybrid modeling method. It combines a physical model for linear deformation with a GAT-LSTM network. Experiments show the hybrid model achieved RMSE/MAE of 4.6/4.0 µm under full training (12 conditions), 5.5/4.9 µm under 3 training condition and 4.8/4.3 µm under 1 training condition, substantially reducing the data requirements for thermal error modeling. The compensation experiment conducted using a high real-time surrogate-model-based architecture reduced thermal error by 78% (from 54 µm to 12 µm), demonstrating high precision and minimal data requirements suitable for real-time applications.
This paper explores the feasibility and implications of developing a privacy-preserving, data-driven cloud service for predicting the energy consumption of industrial robots. Using machine learning, we evaluated three neural network architectures—dense, LSTM, and convolutional–LS…
The prediction of energy-related time series for computer numerical control (CNC) machine tool axes is an essential enabler for the shift towards autonomous and intelligent production. In particular, a precise prediction of energy consumption is needed to determine the environmen…
Rolling bearings are crucial components in CNC machine tool spindles, and their health condition directly affects machining precision and operational reliability. To address the significant challenges of bearing fault diagnosis in industrial environments, this paper proposes an a…
The growing deployment of connected and autonomous vehicles (CAVs) requires coordinated control strategies that jointly address safety, mobility, and energy efficiency. This paper presents a novel two-stage cooperative control framework for autonomous hybrid electric vehicle (HEV…
As engines trend toward miniaturization, lightweight design, and higher power density, noise issues have become increasingly prominent, necessitating precise radiated noise prediction for effective noise control. This study develops a machine learning model based on surface vibra…
Recently, bearing fault diagnosis methods based on deep learning have achieved significant success. However, in practical engineering applications, the limited labeled data and various working conditions severely constrain the widespread application of most deep-learning-based fa…