Mechanical properties prediction for hot rolled products

Herrera, A.; Barco, Jon; Carrillo, Ana; Miñambres, J.; Cagigas, M.

doi:10.1051/metal:2006123

Cited by 2 publications

(1 citation statement)

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“…With the use of computer models, the simulation of the microstructure is becoming more and more accurate. Some researches have been applied to the actual production, such as the SLIMMER for hot rolling of flat products [2], the computer program developed by VAI, VASL, and CDL to predict the mechanical properties of hot rolled plain carbon and HSLA steels [3], the semiempirical model based on the finite element technique of the cooling curves [4], the model developed by LABEIN and ACB to predict the thermal and microstructural history for the slab and the final mechanical properties [5], a comprehensive micro-macro model presented to quantify the effects of the inclusions, and their characteristics on the properties of steel [6]. However, because the structure of these models is very complex with a lot of dependencies, so the physical metallurgy models have some limitations.…”

Section: Introductionmentioning

confidence: 99%

Prediction of the mechanical properties of hot rolling products by using attribute reduction ELM

Sui

2015

Int J Adv Manuf Technol

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Aiming at the process quality parameters of hot rolling production with high-dimensional, strong coupling and redundant information and other features, a new mechanical properties prediction model is proposed. The model based on ELM algorithm is combined with the attribute reduction method. Firstly, attribute reduction method of combining information entropy with Gram-Schmidt orthogonal transformation is used to select effective process quality parameters and form the feature subset. And then, mechanical properties prediction model is built through adopting ELM algorithm as a neural network training approach. Finally, the model is proved by actual production data of two different hot rolling products from a certain iron and steel company. Compared with the traditional modeling methods, the model has the advantage of a simple structure, is less time-consuming, has high prediction accuracy, etc. The prediction results show that it is more adaptive to the complicated hot rolling process and the prediction performance is superior to the classic ELM model.

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Section: Introductionmentioning

confidence: 99%

Prediction of the mechanical properties of hot rolling products by using attribute reduction ELM

Sui

2015

Int J Adv Manuf Technol

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Optimisation of total roll power using genetic algorithms in a compact strip production plant

Marquez

Arribas

Carrillo

et al. 2013

International Journal of Materials Research

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The application of optimisation techniques to hot rolling models can lead to the more efficient use of these models. In this work, a genetic algorithm has been used in order to design new hot rolling schedules with lower energy consumption through a reduction in the total roll power. Firstly, mean flow stress has been modelled for several Nb microalloyed steels produced in a compact strip production plant taking into account recrystallisation and precipitation models. The selected mean flow stress model has been validated against the values obtained from the industrial hot rolling forces using the Sims approach. Secondly, the model has been integrated with a genetic optimisation algorithm and new reductions have been proposed in order to decrease the total rolling power, maintaining all the requirements. The reductions achieved can be up to 10%.

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Mechanical properties prediction for hot rolled products

Cited by 2 publications

References 0 publications

Prediction of the mechanical properties of hot rolling products by using attribute reduction ELM

Prediction of the mechanical properties of hot rolling products by using attribute reduction ELM

Optimisation of total roll power using genetic algorithms in a compact strip production plant

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