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

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Abstract

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.

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Abbreviations

F d :

drawing force, kgf

ε c :

strain at the center of material

ε s :

strain on the surface of material

C p :

contact pressure at the interface of material and die, MPa

r :

reduction ratio, %

α :

die semi-angle, °

μ :

coulomb friction coefficient

v :

drawing speed, mm/min

T m :

material temperature, °C

d i :

initial diameter of the material, mm

K :

strength coefficient, MPa

n :

strain hardening exponent

RA:

reduction of area in a tensile test, %

YS:

yield strength, MPa

EL:

uniform elongation, %

UTS:

ultimate tensile strength, MPa

ω :

accumulative damage parameter

ω c :

accumulated damage at the center of material

ω critical :

critical damage value

σ 1 :

maximum principal stress, MPa

σ eq :

equivalent stress, MPa

σ m :

mean stress, MPa

η :

stress triaxiality

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

equivalent strain in the material during drawing

\(\overline \varepsilon\) :

equivalent strain

nos :

number of samples

t i :

FE calculation for the ith sample

o i :

FR model prediction for the ith sample

DF:

ductile fracture

MAPE:

mean average percentage error, %

CEE:

cross-entropy error

MSE:

mean square error

ANN:

artificial neural network

FR model:

fast running model

VIV :

values of each input variable

VOV :

values of each output variable

GUI program:

graphic user interface program

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Acknowledgement

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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Authors and Affiliations

  1. Department of Mechanical Engineering, Chung-Ang University, Seoul, 06974, Korea

    Donghyuk Cho & Youngseog Lee

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  1. Donghyuk Cho
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  2. Youngseog Lee
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Correspondence to Youngseog Lee.

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This paper was significantly extended and modified from the original paper presented in Asia-Pacific Symposium on Engineering Plasticity and its Applications 2018, and recommended by the Scientific & Technical Committee for journal publication.

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Cho, D., Lee, Y. Development of a Machine Learning Based Fast Running Model to Determine Rapidly the Process Conditions in Drawing Process. Int.J Automot. Technol. 20 (Suppl 1), 9–17 (2019). https://doi.org/10.1007/s12239-019-0123-7

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  • DOI: https://doi.org/10.1007/s12239-019-0123-7

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