What to expect from heavy clay?

Arsenović, Milica; Radojević, Zagorka; Stanković, Slavka; Lalić, Ž.; Pezo, Lato

doi:10.1016/j.ceramint.2012.08.009

Cited by 31 publications

(40 citation statements)

References 21 publications

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“…A multi-layer perceptron model (MLP) consisted of three layers (input, hidden and output), which is the most common, flexible and general-purpose kind of ANN was used, 17) giving the reason for choosing it in this study. The MLP neural network learns using an algorithm called "backpropagation".…”

Section: Statistical Analysesmentioning

confidence: 99%

Chemometric Approach for Mechanical Properties Prediction during the Electromagnetic Casting Process

et al. 2015

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In this study the mechanical properties (reduction of area, S 0 , tensile strength, R m , yield strength, R p , and elongation, A) of EN AW 7075 aluminum alloy obtained by electromagnetic casting were investigated at different operating parameters: frequency (V), field strength (T) and current intensity (I). The predictive mathematical models using Response Surface Methodology, with second order polynomial (SOP) regression models, and Artificial Neural Network model (ANN), were afterwards compared to obtained experimental results. Analysis of variance and posthoc Tukey's HSD test at 95% confidence limit ("honestly significant differences") have been utilised to show significant differences between various samples. SOP models showed good prediction capabilities, with high coefficients of determination (r 2 ), 0.5310.977, while ANN model performed even better prediction accuracy: 0.8000.992. The optimal samples were chosen depending on mechanical properties of the product (S 0 = 50.49 mm 2 , R m = 405.75 N mm ¹2, R p = 302.49 N mm ¹2, A = 6.86%), using optimal operating parameters (V = 30 Hz, I = 250 A, T = 18 © 10

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Section: Statistical Analysesmentioning

confidence: 99%

Chemometric Approach for Mechanical Properties Prediction during the Electromagnetic Casting Process

et al. 2015

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“…Prior to sample preparation, all the raw samples were oven-dried at 105°C and then milled following the usual practice in ceramic laboratories [4]. To simulate industrial processing conditions, the heavy clays were moistened, mixed, and milled to the particle size of 2 mm until homogeneous mass was obtained.…”

Section: Raw Materials and Samplesmentioning

confidence: 99%

“…Th ese parameters can be also indicative for fi nal products' durability and quality. Defi ning the mutual relationship among characteristics of heavy clay, processing conditions, and properties of the fi nal products is a topic that has been extensively studied in previous years [1][2][3][4][5][6][7]. Various reports have been published, but only a few of them are related to the application of the mathematical modeling for better understanding of the overall system behavior and the quality of fi nal products.…”

Section: Introductionmentioning

confidence: 99%

“…Th e heterogeneous nature of the heavy clays implied a complex material-process-product relationship. Th erefore, second order polynomial (SOP) and artifi cial neural network (ANN) models have gained momentum for modeling and control of brick processing [1][2][3][4].…”

Section: Introductionmentioning

confidence: 99%

“…Artifi cial neural network (ANN) models are recognized as a good modeling tool since they provide the solution to the problems from a set of experimental data, and are capable of handling complex systems with nonlinearities and interactions between decision variables [4]. Sensitivity analysis was used for ANN outputs testing, by changing one input variable at a time, for +2.241% or -2.241% of its nominal value, while all the other variables were fi xed to their baseline values.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Prediction and Optimization of Heavy Clay Products Quality

Arsenović

Pezo

Mančić

et al. 2014

Advanced Materials for Agriculture, Food, and Environmental Safety

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Th e eff ects of chemical composition, fi ring temperature (800-1100 °C), and several shape formats of laboratory brick samples on the fi nal product quality were investigated. Prediction of the fi nal laboratory products parameters was evaluated by second order polynomial regression models (SOPs) and artifi cial neural networks (ANNs), and aft erwards compared to experimental results. SOPs showed high r 2 values (0.897-0.913 for compressive strength models, 0.942-0.962 for water absorption, 0.928 for fi ring shrinkage, 0.988-0.991 for water loss during fi ring, and 0.941 for volume mass of cubes models). An ANN model, coupled with sensitivity analysis, was obtained with high prediction accuracy: 0.866-0.939 for compressive strength models, 0.954-0.974 for water absorption, 0.882 for fi ring shrinkage, 0.982-0.988 for water loss during fi ring, and 0.920 for volume mass of cubes models. Th e optimal samples' chemical composition and fi ring temperature were chosen depending on a fi nal usage of the raw material in heavy clay brick industry.

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