4.5 Article

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

Journal

METALS
Volume 11, Issue 5, Pages -

Publisher

MDPI
DOI: 10.3390/met11050833

Keywords

ring rolling; process energy estimation; metal forming; thermo-mechanical FEM analysis; machine learning; artificial neural network

Funding

  1. Basic Science Research Program through the National Research Foundation of Korea (NRF) - Ministry of Education [2019R1I1A1A 01062323]
  2. National Research Foundation of Korea (NRF) - Korea government (MSIT) [2019R1F1A1060567]
  3. National Research Foundation of Korea [2019R1F1A1060567] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)

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This research uses finite element simulations and machine learning models to estimate the energy consumption in metal forming processes, with Gradient Boosting (GB) identified as the most reliable method for predicting force integral changes during forming processes. The trained ML models demonstrate high accuracy in various experimental cases, proving their reliability in estimating energy consumption.
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 ring rolling mill geometries with the resulting energy consumption, measured in terms of the force integral over the processing time (FIOT), FEM simulations have been implemented in the commercial SW Simufact Forming 15. A total of 380 finite element simulations with rings ranging from 650 mm < DF < 2000 mm have been implemented and constitute the bulk of the training and validation datasets. Both finite element simulation settings (input), as well as the FI (output), have been utilized for the training of eight machine learning models, implemented with Python scripts. The results allow defining that the Gradient Boosting (GB) method is the most reliable for the FIOT prediction in forming processes, being its maximum and average errors equal to 9.03% and 3.18%, respectively. The trained ML models have been also applied to own and literature experimental cases, showing a maximum and average error equal to 8.00% and 5.70%, respectively, thus proving once again its reliability.

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