Applying Machine Learning to the Phenomenological Flow Stress Modeling of TNM-B1

Stendal, Johan Andreas; Bambach�, Markus; Eisentraut, Mark; Sizova, Irina; Weiß, Sabine

doi:10.3390/met9020220

Cited by 19 publications

(5 citation statements)

References 25 publications

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“…They pointed out that the ANN model can forecast material flow behavior including the metallurgical phenomenon, and also stated that the model has a capability to capture complex behavior even outside of the test conditions. Ashtiani et al [27] and Stendal et al [28] employed both phenomenological and ANN models to predict high-temperature deformation behavior in AlCuMgPb alloy and titanium aluminide alloy (TNM-B1) materials. They also identified that the well trained ANN model can be able to make accurate predictions than the tested phenomenological equations.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Machine Learning Optimization Approach to Predict Hot Deformation Behavior of Medium Carbon Steel Material

Murugesan

Sajjad

Jung

2019

Metals

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The isothermal tensile test of medium carbon steel material was conducted at deformation temperatures varying from 650 to 950 ∘ C with an interval of 100 ∘ C and strain rates ranging from 0.05 to 1.0 s − 1 . In addition, the scanning electron microscopy (SEM) procedures were exploited to study about the surface morphology of medium carbon steel material. Using the experimental data, the artificial neural network (ANN) model with a back-propagation (BP) algorithm was proposed to predict the hot deformation behavior of medium carbon steel material. For model training and testing purpose, the variables such as deformation temperature, strain rate, and strain data were considered as inputs and the flow stress data were used as targets. Before running the neural network, the test data were normalized to effectively run the problem and after solving the problem, the obtained results were again converted in order to achieve the actual data. According to the predicted results, the coefficient of determination ( R 2 ) and the average absolute relative error between the predicted flow stress and the experimental data were determined as 0.999 and 1.335%, respectively. For improving the model predictability, the constrained nonlinear function based optimization procedures was adopted to obtain the best candidate selections of weights and biases. By evaluating each test conditions, it was found that the average absolute relative error based on the optimized ANN-BP model varied from 0.728% to 1.775%. Overall, the trained ANN-BP models proved to be much more efficient and accurate by means of flow stress prediction against the experimental data for all the tested conditions. These optimized results displayed that an ANN-BP model is more accurate for flow stress prediction than that of the conventional flow stress models.

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Section: Introductionmentioning

confidence: 99%

Hybrid Machine Learning Optimization Approach to Predict Hot Deformation Behavior of Medium Carbon Steel Material

Murugesan

Sajjad

Jung

2019

Metals

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“…Xiao et al [26] conducted a comparative study about the Arrhenius-type constitutive equation and BP-ANN model in a prediction of 12Cr3WV steel deformation behavior and discussed that 12Cr3WV steel flow behavior can be more accurately captured by the optimized BP-ANN model than the Arrhenius-type constitutive model. Likewise, the constitutive relationships were proposed for various materials using the BP-ANN models by researchers Li et al [27], Stendal et al [28], WD et al [29], Thakur et al [30], and Murugesan et al [31] and stated that the neural network model could be an impressive tool to examine the deformation behavior and to suggest the constitutive equation of test materials. However, the BP-ANN model can yield different results at each run due to random outcomes of weights and bias in the neural network and is often termed a multi-restart problem [31].…”

Section: Introductionmentioning

confidence: 99%

Hybrid Artificial Neural Network-Based Models to Investigate Deformation Behavior of AZ31B Magnesium Alloy at Warm Tensile Deformation

Murugesan

Chung

et al. 2023

Materials

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The uniaxial warm tensile experiments were carried out in deformation temperatures (50–250 °C) and strain rates (0.005 to 0.0167 s−1) to investigate the material workability and to predict flow stress of AZ31B magnesium alloy. The back–propagation artificial neural network (BP–ANN) model, a hybrid models with a genetic algorithm (GABP–ANN), and a constrained nonlinear function (CFBP–ANN) were investigated. In order to train the exploited machine learning models, the process parameters such as strain, strain rate, and temperature were accounted as inputs and flow stress was considered as output; moreover, the experimental flow stress values were also normalized to constructively run the neural networks and to achieve better generalization and stabilization in the trained network. Additionally, the proposed model’s closeness and validness were quantified by coefficient of determination (R2), relative mean square error (RMSE), and average absolute relative error (AARE) metrics. The computed statistical outcomes disclose that the flow stress predicted by both GABP–ANN and CFBP–ANN models exhibited better closeness with the experimental data. Moreover, compared with the GABP–ANN model outcomes, the CFBP–ANN model has a relatively higher predictability. Thus, the outcomes confirm that the proposed CFBP–ANN model can result in the accurate description of AZ31 magnesium alloy deformation behavior, showing potential for the purpose of practicing finite element analysis.

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“…Results were tested by using cross validation and estimating the flow curves that are not included in the datasets. Machine learning algorithms were applied to phenomenological flow curve estimation by Stendal et al (2019) [6]. In the study, flow curve estimation was made using a previously developed phenomenological model and machine learning separately and together.…”

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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Applying Machine Learning to the Phenomenological Flow Stress Modeling of TNM-B1

Cited by 19 publications

References 25 publications

Hybrid Machine Learning Optimization Approach to Predict Hot Deformation Behavior of Medium Carbon Steel Material

Hybrid Machine Learning Optimization Approach to Predict Hot Deformation Behavior of Medium Carbon Steel Material

Hybrid Artificial Neural Network-Based Models to Investigate Deformation Behavior of AZ31B Magnesium Alloy at Warm Tensile Deformation

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

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