Machine Learning-Based Models for the Estimation of the Energy Consumption in Metal Forming Processes

Machine Learning-Based Models for the Estimation of the Energy Consumption in Metal Forming Processes

This research provides an insight on the performances of machine learning (ML)-based algorithms for the estimation of the energy consumption in metal forming processes and is applied to the radial-axial ring rolling process. To define the mutual influence between ring geometry, process settings, and...

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Bibliographic Details
Main Authors: Irene Mirandola, Guido A. Berti, Roberto Caracciolo, Seungro Lee, Naksoo Kim, Luca Quagliato
Format: Article
Language:English
Published: MDPI AG 2021-05-01
Series:Metals
Subjects:
ring rolling
process energy estimation
metal forming
thermo-mechanical FEM analysis
machine learning
artificial neural network
Online Access:https://www.mdpi.com/2075-4701/11/5/833

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