Adaptive thermal displacement compensation method based on deep learning

Multistep prediction of dynamic uncertainty under limited data

“…NNs are composed of multiple layers, allowing them to learn complex nonlinear relationships. Bayesian deep learning (BDL) and variations thereof have been widely applied to forecast future events given existing data and update when presented with new data [29][30][31][32][33][34][35][36][37]. Deep learning models are only as accurate as the data they are trained on and, as such, typically require large datasets with defined trends over time [37].…”

Thermal error prediction for heavy-duty CNC machines enabled by long short-term memory networks and fog-cloud architecture

“…Temperature measurement points were clustered by a SOM neural network, and an analysis was conducted to explore the correlation between the thermal sensitive points and the thermal error. Fujishima et al (2019) developed a novel thermal displacement compensation method using a deep learning algorithm. In the algorithm, reliability of thermal displacement prediction was evaluated and compensation weights were adjusted adaptively.…”

Recent Advances on Machine Learning Applications in Machining Processes

“…Wang et al [68] used DL applications for thermal deformation modeling using data mining based on RST and reducing the thermal error of~99%. Fujishima et al [69] focused on thermal displacement prediction, applying a DNN with Bayesian Dropout and considering the sensor failures to test the robustness of the model. In Reference [70], the research aimed to predict the thermal drift based on four different working conditions; the authors applied pretrained coefficients for the CNN initialization, obtaining a so-called CNN-FT (fine-tuning).…”