Artificial neural networks application to predict the ultimate tensile strength of X70 pipeline steels

Khalaj, Gholamreza; Azimzadegan, Tohid; Khoeini, Mahdi; Etaat, Moslem

doi:10.1007/s00521-012-1182-0

Cited by 40 publications

(19 citation statements)

References 37 publications

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“…However, there has not been a generalized analytical function representing both the temperature dependency and the strain rate dependency of the flow stress. It has been recently demonstrated that the artificial neural network (ANN) [36][37][38][39][40] was successfully applied in the prediction of the flow stress, which depends on temperature, strain rate and strain because the ANN-based regression methods can fit complex mathematical relationships [41]. Although the feasibility of applying the ANN in modeling the flow stress is demonstrated, a comparative study is still needed to clarify the difference between the ANN method and the conventional method to support selecting a flow stress model in a simulation for FSW.…”

Section: Introductionmentioning

confidence: 99%

Flow Stress of 6061 Aluminum Alloy at Typical Temperatures during Friction Stir Welding Based on Hot Compression Tests

Ding¹,

Shi

Chen

2021

Metals

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The purpose of this paper is to report quantitative data and models for the flow stress for the computer simulation of friction stir welding (FSW). In this paper, the flow stresses of the commercial 6061 aluminum alloy at the typical temperatures in FSW are investigated quantitatively by using hot compression tests. The typical temperatures during FSW are determined by reviewing the literature data. The measured data of flow stress, strain rate and temperature during hot compression tests are fitted to a Sellars–Tegart equation. An artificial neural network is trained to implement an accurate model for predicting the flow stress as a function of temperature and strain rate. Two models, i.e., the Sellars–Tegart equation and artificial neural network, for predicting the flow stress are compared. It is found that the root-mean-squared error (RMSE) between the measured and the predicted values are found to be 3.43 MPa for the model based on the Sellars–Tegart equation and 1.68 MPa for the model based on an artificial neural network. It is indicated that the artificial neural network has better flexibility than the Sellars–Tegart equation in predicting the flow stress at typical temperatures during FSW.

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Section: Introductionmentioning

confidence: 99%

Flow Stress of 6061 Aluminum Alloy at Typical Temperatures during Friction Stir Welding Based on Hot Compression Tests

Ding¹,

Shi

Chen

2021

Metals

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“…The algorithm stopped, when the validation error increased for six iterations, which occurred at iteration 482. LM is often the fastest available back-propagation algorithm and highly recommended as the first choice supervised algorithm, although it requires more memory than other algorithms as informed by KHALAJ et al (2013). In our study LMBP method was used because of mean-squared error (MSE) values for LM method were lower compared with those Bayesian regulation in the training stages, the LM method was preferred in the modelling of the experimental data.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Evaluation of models for the dew point temperature determination

Górnicki¹,

Winiczenko²,

Kaleta³

et al. 2017

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The accuracy of the available from the literature models for the dew point temperature determination was compared. The proposal of the modelling using artificial neural networks was also given. The experimental data were taken from the psychrometric tables. The accuracies of the models were measured using the mean bias error MBE, root mean square error RMSE, correlation coefficient R, and reduced chi-square χ2. Model M3, especially with constants A=237, B=7.5, gave the best results in determining the dew point temperature (MBE: -0.0229 – 0.0038 K, RMSE: 0.1259 – 0.1286 K, R=0.9999, χ2: 0.0159 – 0.0166 K2). Model M1 with constants A=243.5, B=17.67 and A=243.3, B=17.269 can be also considered as appropriate (MBE=-0.0062 and -0.0078 K, RMSE=0.1277 and 0.1261 K, R=0.9999, χ2=0.0163 and 0.0159 K2). Proposed ANN model gave the good results in determining the dew point temperature (MBE=-0.0038 K, RMSE=0.1373 K, R=0.9999, χ2=0.0189 K2).

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“…The atoms of Cr, Mo and Ni in the weld metal can be remelted at a high temperature, which can replace Fe atoms in the lattice and disturb the original lattice arrangement, and can also make dislocation movement difficult and strengthen the joint. As in HSLA steel, the addition of Mn and other alloying elements, such as copper (Cu), titanium (TI) and vanadium (V), both provide strengthening and an obtain ideal microstructure [26,27];…”

Section: Charpy V-notch Impact Testsmentioning

confidence: 99%

Investigation on Microstructure and Properties of Duplex Stainless Steel Welds by Underwater Laser Welding with Different Shielding Gas

et al. 2021

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Taking S32101 duplex stainless steel as the research object, underwater laser wire filling welding technology was used for U-groove filling welding. The influence of different shielding gas compositions on the ferrite content, microstructure, mechanical properties and pitting corrosion resistance was studied by simulating a water depth of 15 m in the hyperbaric chamber. The results show that, under the same process parameters, the size and proportion of austenite in the weld when using pure nitrogen as the shielding gas are larger than those protected by other shielding gases. In a mixed shielding gas, the increase in nitrogen content has little effect on the strength and toughness of the weld. Regardless of the shielding gas used, the base metal was the weakest part of the weld. At the same time, intermetallic inclusions have an adverse effect on the impact toughness of the weld. The pitting corrosion resistance of the welds depends on the Cr2N content in the heat-affected zone. The precipitation and enrichment of Cr2N causes local chromium deficiency, which is the main factor for the weak pitting corrosion ability of the heat-affected zone. Pure nitrogen protection has a better corrosion resistance than other gas protection.

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Artificial neural networks application to predict the ultimate tensile strength of X70 pipeline steels

Cited by 40 publications

References 37 publications

Flow Stress of 6061 Aluminum Alloy at Typical Temperatures during Friction Stir Welding Based on Hot Compression Tests

Flow Stress of 6061 Aluminum Alloy at Typical Temperatures during Friction Stir Welding Based on Hot Compression Tests

Evaluation of models for the dew point temperature determination

Investigation on Microstructure and Properties of Duplex Stainless Steel Welds by Underwater Laser Welding with Different Shielding Gas

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