… machine learning (ML)-based algorithms for the estimation of the energy consumption in
metal forming … mill geometries with the resulting energy consumption, measured in terms of the …

… machine learning (ML)-based algorithms for the estimation of the energy consumption in
metal forming … mill geometries with the resulting energy consumption, measured in terms of the …

… This work explores and discusses the use of ML techniques regarding … metal forming is
called parameter identification. The aim is to estimate material parameters for constitutive models, …

… used for modelling using deep learning. Results indicate that deep learning based on DLeSSL
performs better in energy … to establish an energy consumption model based on the data …

… Machine learning models were used to determine energy consumption in the metal forming
process and are applied to the radial-axial ring rolling process (Mirandola et al., 2021). For …

… But especially sensorial acquired data in sheet metal forming process show a … knowledge
from sheet metal forming processes can be generated by the systematic procedure model KDT-…

… Details of the specific SBMLM and IBMLM representative models used in this study are …
machine learning models used in this study are discussed. The dataset of a cold metal forming …

… [20] developed an ANN predictor to model power consumption in … into the ANN model for
predicting energy consumed in high-… Cambria, “Recent trends in deep learning based natural …

… that metal products are consistent and of good quality, as well as optimizing the rolling
process to reduce unnecessary energy use and wear on machines. Zhang et al. [13] used a …

… , ie, regression, neural network, and deep learning-based model. The machine learning
based models established in this work can enable a prior prediction on power consumption for a …