Analysis of the effects of processing parameters on mechanical properties and formability of cold rolled low carbon steel sheets using neural networks

Monajati, H.; Asefi, D.; Parsapour, Amir; Abbasi, Sh.

doi:10.1016/j.commatsci.2010.06.040

Cited by 27 publications

(16 citation statements)

References 14 publications

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“…Amount of coil in batch annealing furnace slight effect the difference temperature between hot point and cold point [4] In the annealing stage, heating rate and soaking time obviously have higher intrinsic effects on formability properties. The coil position in the batch annealing furnace has a slight effect on n-value but more effect on r-value [5]. A low carbon steel sheet with 0.035% Cr responded positively to continuous annealing exceed to 700 °C with fully recrystallized grains [6].…”

Section: %Elongationmentioning

confidence: 99%

Factors Affecting the Mechanical Properties Variation after Annealing Of Cold Rolled Steel Sheet

Wichienrak

Puajindanetr

2019

E3S Web Conf.

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Cold rolled steel industry in type of batch annealing furnace, the mechanical properties of steel sheet have variation by each position. The parameters of annealing temperature and time were analysed to work out the source of mechanical properties variation. This experiment is using low-carbon steel sheet that were cold rolled at the same reduction ratio. Then annealed applying by different annealing temperature and soaking time in laboratory furnace. The mechanical properties which were examined. Yield strength, Tensile strength, %Elongation and Hardness. The result showed that (1) Increasing the annealing temperature could remarkably decrease the yield strength, tensile strength and hardness, whereas the %Elongation could be increased. (2) Increasing the soaking time could slightly effect on mechanical properties. (3) The annealing temperature of 650°C with soaking time of 2 hr should be applied to provide the mechanical properties close to target value (4) Grain size of the workpieces trended to be grown from the annealing temperature of 610°C.The experiment it can be concluded that annealing temperature and soaking time have significant effect on the mechanical properties variation in batch annealing.

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Section: %Elongationmentioning

confidence: 99%

Factors Affecting the Mechanical Properties Variation after Annealing Of Cold Rolled Steel Sheet

Wichienrak

Puajindanetr

2019

E3S Web Conf.

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“…Uma rede neural que analisava os parâmetros do processo de produção de chapas de aço de baixo carbono, tais como composição química, laminação a frio ou a quente e as condições de tratamento térmico, foi utilizada em [5] O anisotrópico. Após validação dos resultados, foi possível concluir que a rede neural proposta é capaz de predizer parâmetros importantes do processo de fabricação e conformação das chapas de aço utilizadas no trabalho, o que possibilita a melhoria do processo no chão de fábrica.…”

Section: Introductionunclassified

Simulation via Artificial Neural Networks and Mechanic Behavior Analysis of Steel AISI 430

Presoti

Martins

Wanner

et al. 2016

IEEE Latin Am. Trans.

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The tensile test is one of the main techniques of mechanical characterization of materials. It is a destructive test and, considering the preparation of the proof corps, it has high cost and is time consuming. This work intends to use Artificial Neural Networks to simulate the behavior of the tensile testing machine at different annealing temperatures of ferritic stainless steel AISI 430, stabilized with niobium. The samples were annealed at 14 different temperature values for one hour and cooled in the furnace. The results showed that the network correctly simulated the stressstrain curves generated during the tensile tests getting low average squared errors and hardening rates with even minor errors. This characterization indicates the possibility of refining the microstructure in the samples annealed below 700°C, this temperature recrystallization and secondary recrystallization at 900°C.

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“…Uma rede neural que analisava os parâmetros do processo de produção de chapas de aço de baixo carbono, tais como composição química, laminação a frio ou a quente e as condições de tratamento térmico, foi utilizada em [5]. A rede neural foi usada para encontrar os parâmetrosótimos que maximizam o valor do coeficiente de encruamento anisotrópico.…”

Section: Introductionunclassified

Perfil da Curva de Índice de Encruamento do Aço AISI 430 E: simulação via Redes Neurais Artificiais

Presoti¹,

Nascimento²,

Lopes³

et al. 2015

Anais Do 12. Congresso Brasileiro De Inteligência Computacional

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Analysis of the effects of processing parameters on mechanical properties and formability of cold rolled low carbon steel sheets using neural networks

Cited by 27 publications

References 14 publications

Factors Affecting the Mechanical Properties Variation after Annealing Of Cold Rolled Steel Sheet

Factors Affecting the Mechanical Properties Variation after Annealing Of Cold Rolled Steel Sheet

Simulation via Artificial Neural Networks and Mechanic Behavior Analysis of Steel AISI 430

Perfil da Curva de Índice de Encruamento do Aço AISI 430 E: simulação via Redes Neurais Artificiais

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