Prediction of the True Stress of ZE20 Magnesium Alloy at Different Temperatures and Strain Rates

Bingöl, Sedat; Misiołek, Wojciech Z.

doi:10.5545/sv-jme.2015.2785

Cited by 10 publications

(2 citation statements)

References 17 publications

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“…The estimated values were compared with the experimental results. Bingöl and Misiolek (2015) [4] conducted compression tests at temperatures of 200-400⁰C and strain rate of 0.01-15 s -1 . Then, true stress and true strain values at certain temperature and strain rate points were estimated by using ANN and Gene Expressing Programming (GEP) models and compared with the experimental results.…”

Section: Introductionmentioning

confidence: 99%

A Model to Construct and Predict Flow Curve of Materials from Compression Test Results with Machine Learning Models Using Python

Aydın¹,

Kocatürk²,

Zeren³

2022

KEM

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In order to obtain flow curves from compression test results of a cold forging material and predict flow curves of the material at intermediate temperature and strain rate values, a model was developed using Python programming language in this study. The model consists of two parts: Flow curve determination and flow curve prediction. The compression test data including Force-Stroke values was processed to determine the flow curves in the first part, and the flow curve data constructed for certain temperature and strain rate values of the material was used as input for the machine learning algorithms to predict flow curve at desired intermediate temperature and strain rate values in the second part. Moreover, Ludwik material model parameters were estimated by using curve fitting methods in order to define the material model into the simulation software. Machine learning algorithms and various regression models in Python libraries were tested to predict the flow curves. The performances of different machine learning and regression models were compared with respect to the mean squared error and coefficient of determination performance measures. Support vector regression, k-Nearest Neighbour (kNN) and artificial neural network models were used to predict flow curves of cold forging materials and kNN regression model was able to found predictions with the lowest error rate. As a result, a model that can process the compression test data to predict flow curves at intermediate temperature or strain rate values was developed.

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Section: Introductionmentioning

confidence: 99%

A Model to Construct and Predict Flow Curve of Materials from Compression Test Results with Machine Learning Models Using Python

Aydın¹,

Kocatürk²,

Zeren³

2022

KEM

View full text Add to dashboard Cite

show abstract

“…The AZ31 magnesium finds applications in areas for different purposes, especially in the automotive industry due to its high specific strength. AZ31 can be super-formed at elevated temperatures [8], hence, the process temperature was simulated at 623K which is the suitable forming temperature for AZ31 magnesium material. [20] [14] The deformation characteristics of magnesium material are improved with the relaxation of additional prismatic and pyramidal gliding planes.…”

Section: Methodsmentioning

confidence: 99%

Simulation of Helical Gear Forming of Az31 Magnesium Material

Ayer¹

2017

Adv. Sci. Technol. Res. J.

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The simulation of helical gear forming is investigated in this study. AZ31 type magnesium material was selected for workpiece material because of its high specific strength. The finite element method was used to simulate the closed die upsetting method for magnesium material of helical gear upsetting. Three dimensional finite element analyses were carried out to obtain the forming load, effective stress, effective strain and temperature change with DEFORM-3D software.

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Data-driven constitutive model of GH4169 alloy within a synergistic high strain rate and elevated temperature

et al. 2023

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Prediction of the True Stress of ZE20 Magnesium Alloy at Different Temperatures and Strain Rates

Cited by 10 publications

References 17 publications

A Model to Construct and Predict Flow Curve of Materials from Compression Test Results with Machine Learning Models Using Python

A Model to Construct and Predict Flow Curve of Materials from Compression Test Results with Machine Learning Models Using Python

Simulation of Helical Gear Forming of Az31 Magnesium Material

Data-driven constitutive model of GH4169 alloy within a synergistic high strain rate and elevated temperature

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