Development of a Machine Learning Based Fast Running Model to Determine Rapidly the Process Conditions in Drawing Process

  • Donghyuk Cho
  • Youngseog LeeEmail author


This study proposes a fast running model that interconnects input and output data for a single-pass cold bar drawing process through the use of Artificial Neural Network (ANN) and automatically generated a large volume of elastic-plastic finite element (FE) analysis results. The prediction accuracy of the FE analysis was verified by comparing the FE analysis with measurements from a drawing experiment. A Python-based script that automatically controls ABAQUS was coded to sequentially produce output data that varies according to the input data, which is a combination of 18 grades of steel and 1,000 process conditions. The ANN was trained using input and output data, and then a nine-dimensional fast running model was developed. The fast running model predicted the values of output variables (drawing force, strain at the center, strain on the surface, accumulated damage at the center, contact pressure, and the fracture (or non-fracture) of the material) in 0.1 second no matter how the mechanical properties of the steels and process conditions change. With this fast running model, engineers in the drawing industry can easily determine or modify the process conditions to improve productivity and product quality even when a grade of steel that has never been employed before is drawn.

Key words

Machine learning ANN Fast running model Elastic-plastic FE simulation Laboratory bar drawing test 



drawing force, kgf


strain at the center of material


strain on the surface of material


contact pressure at the interface of material and die, MPa


reduction ratio, %


die semi-angle, °


coulomb friction coefficient


drawing speed, mm/min


material temperature, °C


initial diameter of the material, mm


strength coefficient, MPa


strain hardening exponent


reduction of area in a tensile test, %


yield strength, MPa


uniform elongation, %


ultimate tensile strength, MPa


accumulative damage parameter


accumulated damage at the center of material


critical damage value


maximum principal stress, MPa


equivalent stress, MPa


mean stress, MPa


stress triaxiality

\({\overline \varepsilon_{{\rm{dr}}}}\)

equivalent strain in the material during drawing

\(\overline \varepsilon\)

equivalent strain


number of samples


FE calculation for the ith sample


FR model prediction for the ith sample


ductile fracture


mean average percentage error, %


cross-entropy error


mean square error


artificial neural network

FR model

fast running model


values of each input variable


values of each output variable

GUI program

graphic user interface program



This work was supported by a grant (NRF-2016 R1D1A1B03935327) from the National Research Foundation of Korea funded by the Korean government (Ministry of Science, ICT & Future Planning, MSIP).


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Copyright information

© KSAE/112-03 2019

Authors and Affiliations

  1. 1.Department of Mechanical EngineeringChung-Ang UniversitySeoulKorea

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