This paper focuses on modelling the relationship between flow stress and strain, strain rate and temperature using Support Vector Regression technique. Data obtained for both the regions (non-Dynamic Strain Aging and Dynamic Strain Aging) is analysed using Support Vector Machine, where a nonlinear model is learned by linear learning machine by mapping it into high dimensional kernel included feature space. A number of semi empirical models based on mathematical relationships and Artificial Intelligence techniques were reported by researchers to predict the flow stress during deformation. This work attempts to show the prowess of Support Vector Regression based modelling applied to flow stress prediction, delineating the flexibility that the user is presented with, while modelling the problem. The model is successfully trained based on the training data and employed to predict the flow stress values for the testing data, which were compared with the experimental values. It was found that the correlation coefficient between the predicted and experimental data is 0.9978 for the nonDynamic Strain Aging regime and 0.9989 for the Dynamic Strain Aging regime showcasing the excellent predictability of this model when compared with other models that are prominently used for flow stress prediction. Data is trained at different values of insensitivity loss function of the Support Vector Regression for showcasing the unique features of this technique. The results produced are encouraging to the researchers for exploring this Artificial Intelligence technique for data modelling.
Iron sand contains Fe 3 O 4 which can be produced Fe 2 O 3 and limestone contains dominant CaCO 3 . Both of these materials are very abundant in Indonesia. Iron sand is generally used as building construction materials, as well as the use of limestone, there has been no development in the management of the combination of the two materials. Because of the abundance of iron sand and limestone which have not yet been developed to the fullest, a study was carried out to manage and develop the product as a follow up to the previous Calcium Ferrite research. This research focuses on analyzing the phase of Calcium Ferrite formed using XRD and SEM-EDX tests. Tests were carried out on samples with a mass ratio of a mixture of Fe 2 O 3 and CaCO 3 of 1: 4, 1: 6, 1: 8, and 1:12. The XRD test results showed that the phases formed were dominated by the Ca 2 Fe 2 O 5 and Ca 2 Fe 9 O 13 phases. And from the SEM-EDX test results, the results indicate the formation of nano-scale Calcium Ferrite with the composition of elements Ca, Fe, O, and Si.
Synthesis of Ca-Fe-O using coprecipitation method employing CaCO3 and Fe2O3 has been conducted. Extraction of limestone as the raw material of precipitated calcium carbonate (PCC) and iron sands as that of Fe2O3 was prepared to explore various compound of Ca-Fe-O. PCC and Fe2O3 are dissolved in HCl then mixed into homogeneous and precipitated using NH4OH. Mixing is resolved by the mass ratio of PCC and Fe2O3 with a ratio of 1/4, 1/6, 1/8, 1/12. The results of synthesis are sintering at temperature of 7000C. The sintered samples were characterized by XRD and SEM-EDX. The results of XRD is indicated the formation of Ca-Fe-O phases that is dominated by Ca2Fe2O5 and Ca2Fe9O13 phases, and results of SEM/EDX indicate nanoscale particle size that is composed of Ca, Fe, O, and Si elements.
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