Effective strategies of metamodelling of industrial metallurgical processes

Kusiak, J.; Sztangret, Łukasz; Pietrzyk, M.

doi:10.1016/j.advengsoft.2015.02.002

Cited by 16 publications

(12 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The analysis of the six examples presented in Table 2 shows that the memory size of Kriging metamodel depends squarely on the number of experimental points. Comparison of discussed techniques can also be found in [24] where the metamodelling of laminar cooling of dual-phase steel strips after hot rolling was presented and where the ANN technique gives lower error in metamodelling of real industrial process.…”

Section: Development and Evaluation Of The Metamodelmentioning

confidence: 99%

Application of Metamodels to Identification of Metallic Materials Models

Pietrzyk

Kusiak

Szeliga

et al. 2016

Advances in Materials Science and Engineering

View full text Add to dashboard Cite

Improvement of the efficiency of the inverse analysis (IA) for various material tests was the objective of the paper. Flow stress models and microstructure evolution models of various complexity of mathematical formulation were considered. Different types of experiments were performed and the results were used for the identification of models. Sensitivity analysis was performed for all the models and the importance of parameters in these models was evaluated. Metamodels based on artificial neural network were proposed to simulate experiments in the inverse solution. Performed analysis has shown that significant decrease of the computing times could be achieved when metamodels substitute finite element model in the inverse analysis, which is the case in the identification of flow stress models. Application of metamodels gave good results for flow stress models based on closed form equations accounting for an influence of temperature, strain, and strain rate (4 coefficients) and additionally for softening due to recrystallization (5 coefficients) and for softening and saturation (7 coefficients). Good accuracy and high efficiency of the IA were confirmed. On the contrary, identification of microstructure evolution models, including phase transformation models, did not give noticeable reduction of the computing time.

show abstract

Section: Development and Evaluation Of The Metamodelmentioning

confidence: 99%

Application of Metamodels to Identification of Metallic Materials Models

Pietrzyk

Kusiak

Szeliga

et al. 2016

Advances in Materials Science and Engineering

View full text Add to dashboard Cite

show abstract

“…Taking into account that the values of x * i lie within the range [−1, 1] the error seems to be high. However, as it was presented in [13], the error can be reduced by increasing the number of records in training set.…”

Section: Metamodel (Step 4-6)mentioning

confidence: 99%

“…There are no clear guidelines on how to make this decision. Some hints can be found in [13]. A metamodel training method is dedicated to each specific metamodel type.…”

Section: The General Workflowmentioning

confidence: 99%

“…Metamodels can be built using several approaches. Two of them will be considered within the scope of this work: the Artificial Neural Network method and the Kriging method [13].…”

Section: The Metamodelling Ideamentioning

confidence: 99%

“…There are several approximation methods available. The feedforward ANNs had been firstly considered, however due to the relatively small number of available training records (2071 in "casted" set, 825 in "homogenized" set and 779 in "hot-rolled" set) the Kriging method was chosen [13]. Kriging is based on the idea that a value in a given point can be estimated on the basis of an average of known values in the neighboring points, assuming that the influences of these points are proportional to the distance to the considered point.…”

Section: Metamodel (Step 4-6)mentioning

confidence: 99%

See 2 more Smart Citations

Methodology for metamodelling of microstructure evolution: precipitation kinetic case study

2018

View full text Add to dashboard Cite

Numerical modeling is an important tool assisting in the designing and optimization of the production technology. The highest predictive capabilities are offered by multiscale modeling. The most important limitation of its wide application is computational cost. One of possible solutions is application of metamodels for fine scale modeling. In this paper, a systematic approach to development of metamodels is presented. All necessary steps, analyzing the model, selecting the metamodel inputs and outputs, gathering the training and testing datasets, choosing a metamodelling technique, training and testing the metamodel are described with a scientific background and practical examples. Development of the exemplary metamodel, replacing thermodynamic modeling of precipitation kinetic is presented.

show abstract