A comparative study on phenomenological and artificial neural network models for high temperature flow behavior prediction in Ti6Al4V alloy

Uz, Murat Mert; Yoruç, Afife Binnaz Hazar; Cokgunlu, Okan; Aydoğan, Cahit Sertaç; Yapıcı, Güney Güven

doi:10.1016/j.mtcomm.2022.104933

Cited by 8 publications

(5 citation statements)

References 48 publications

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“…The testing of four different feed-forward architectures, primarily trained with constitutive flow equations for a wide parameter spectrum, are consequently assessed with an experimental dataset of flow curves. The application of new techniques for calibration or identification of mathematical models of the mechanical behavior of materials remains a relevant subject in mechanical and civil engineering research, as has been demonstrated in previous research [16,17,20,[28][29][30][31][32], with particular focus on the simplicity, accuracy, and robustness.…”

Section: Dynamic and Static Flow Stress With Thermal Softening Of Ti64mentioning

confidence: 99%

“…Inspired by the homologous behavior of biological neurons, ANNs learn and train themselves rather than being explicitly programmed. In the study of the mechanical behavior of materials, ANNs can be applied to directly predict behavior [15][16][17] or to optimize and assist in finding the parameters of material models [15,18,19]. When an ANN-based identification method is well-trained on various cases, it can be expected to be more robust and versatile than classical direct and inverse techniques, which will always require a skilled human supervision.…”

Section: Introductionmentioning

confidence: 99%

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Assessing Feed-Forward Backpropagation Artificial Neural Networks for Strain-Rate-Sensitive Mechanical Modeling

Tuninetti,

Forcael,

Valenzuela

et al. 2024

Materials

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The manufacturing processes and design of metal and alloy products can be performed over a wide range of strain rates and temperatures. To design and optimize these processes using computational mechanics tools, the selection and calibration of the constitutive models is critical. In the case of hazardous and explosive impact loads, it is not always possible to test material properties. For this purpose, this paper assesses the efficiency and the accuracy of different architectures of ANNs for the identification of the Johnson–Cook material model parameters. The implemented computational tool of an ANN-based parameter identification strategy provides adequate results in a range of strain rates required for general manufacturing and product design applications. Four ANN architectures are studied to find the most suitable configuration for a reduced amount of experimental data, particularly for cases where high-impact testing is constrained. The different ANN structures are evaluated based on the model’s predictive capability, revealing that the perceptron-based network of 66 inputs and one hidden layer of 30 neurons provides the highest prediction accuracy of the effective flow stress–strain behavior of Ti64 alloy and three virtual materials.

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Section: Dynamic and Static Flow Stress With Thermal Softening Of Ti64mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Assessing Feed-Forward Backpropagation Artificial Neural Networks for Strain-Rate-Sensitive Mechanical Modeling

Tuninetti,

Forcael,

Valenzuela

et al. 2024

Materials

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show abstract

“…The upper limit of the weight and threshold is set to 5, and the lower limit is set to −5. The location of the discoverer can be calculated by Formula (18).…”

Section: Prediction Model Of Titanium Alloy Overlapping Heating Defor...mentioning

confidence: 99%

“…Asmael M et al [17] researched the shear properties of friction-welded joints of titanium alloy and established a prediction model based on a machine learning algorithm, which can predict the shear properties of friction-welded joints of titanium alloy. Uz M M et al [18] predicted the mechanical flow behavior of titanium alloy in a certain temperature range by establishing an artificial neural network model. Bautista-Monsalve F et al [19] established a single-point incremental prediction model of titanium alloy based on a machine learning algorithm, which can predict the surface quality of titanium alloy formed by single-point incremental forming.…”

Section: Introductionmentioning

confidence: 99%

Deformation Intelligent Prediction of Titanium Alloy Plate Forming Based on BP Neural Network and Sparrow Search Algorithm

Wang,

et al. 2024

JMSE

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The application of titanium alloy in shipbuilding can reduce ship weight and carbon emissions. To solve the problem of titanium alloy forming, the deformation prediction of titanium alloy line heating based on a backpropagation (BP) neural network and sparrow search algorithm (SSA) was researched. Based on the thermal–elastic–plastic finite element method, the numerical calculation model of TA5 titanium alloy overlapping heating forming was established. The feasibility of the model was verified by comparing it with the numerical calculation and experiment of low-carbon steel. Considering the characteristics of the titanium alloy-forming process, 73 groups of titanium alloy-forming schemes were obtained by the Latin hypercube sampling method. The deformation data of the samples were obtained by using the numerical calculation model of titanium alloy forming. The prediction methods of titanium alloy-forming deformation based on BP, genetic algorithm–backpropagation (GA-BP), and SSA-BP were proposed. The accuracy of different neural network prediction models was analyzed. The mean absolute percentage errors (MAPEs) of BP, GA-BP, and SSA-BP in shrinkage prediction were 7.45%, 4.08%, and 2.96%, respectively. The MAPEs of BP, GA-BP, and SSA-BP in deflection prediction were 8.44%, 4.73%, and 2.64%, respectively. The goodness of fit (R2) of SSA-BP is closest to 1 among the three models. The calculation results show that SSA-BP is better than BP and GA-BP in predicting the forming deformation of titanium alloy. The maximum prediction error of SSA-BP is 4.95%, which is within the allowable range of engineering error. The SSA-BP prediction model is suitable for the rapid and accurate prediction of the deformation of titanium alloy line heating forming. The intelligent prediction model provides data support for intelligent decisions for titanium alloy forming.

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“…With the application of artificial intelligence in the field of materials, machine learning has been utilized to construct constitutive models and has gained wide application in recent years [21,22]. For example, artificial neural networks (ANNs) have been widely used to accurately predict the hot deformation behaviors of nickel-based superalloys [23][24][25][26][27][28][29].…”

Section: Introductionmentioning

confidence: 99%

Prediction of Tensile Properties in Inconel 625 Superalloy Fabricated by Wire Arc Additive Manufacturing Using Improved Artificial Neural Network

Xu,

Lu,

Chen

et al. 2024

Applied Sciences

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This study exhibits the possibility of using an artificial neural network (ANN) to model the mechanical behavior of wire arc additive manufacturing (WAAM) for Inconel 625. For this reason, tensile tests of Inconel 625 superalloy as-built (AB) samples and samples after heat treatment at 1200 °C (HT-1200) by WAAM were performed. For the HT-1200 samples, the yield stress decreased, and the elongation increased significantly due to grain refinement and the formation of annealed twins. A new hybrid model combining a swarm intelligence optimization algorithm with a back propagation neural network (BPNN) was developed to simulate the flow behavior of the superalloy. Compared with other hybrid BPNN models that have been reported, the proposed BPNN model is in better agreement with the experimental data and provides a better description of the flow stress of the Inconel 625 superalloy. The excellent predictive ability of the model may be attributed to the optimization of the weights and thresholds of the BPNN, which obtains the optimal global solution in the search space more efficiently.

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A comparative study on phenomenological and artificial neural network models for high temperature flow behavior prediction in Ti6Al4V alloy

Cited by 8 publications

References 48 publications

Assessing Feed-Forward Backpropagation Artificial Neural Networks for Strain-Rate-Sensitive Mechanical Modeling

Assessing Feed-Forward Backpropagation Artificial Neural Networks for Strain-Rate-Sensitive Mechanical Modeling

Deformation Intelligent Prediction of Titanium Alloy Plate Forming Based on BP Neural Network and Sparrow Search Algorithm

Prediction of Tensile Properties in Inconel 625 Superalloy Fabricated by Wire Arc Additive Manufacturing Using Improved Artificial Neural Network

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