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USE OF REGRESSION ANALYSIS AND SIMULATION TO OPTIMIZE THE COMPOSITION OF THE CAST IRON CHARGE
Abstract
Cast iron GG30 (EN-GJL-300C) has excellent mechanical properties and good machinability. It can be used in many industries such as hydraulics, engineering, automotive, oil and gas mining, and processing. It is suitable for the production of compressor and pump components. Return material, technological residue, iron scrap, fractional cast iron, liquid metal, and alloying components can be used as input raw materials in the production of cast iron, the first three were taken into account in the analysis. In practice, it is possible to choose their different ratio. At the same time, their chemical composition is somewhat variable. The aim of the paper is to compare two models, LINEST and DoE, which, with the help of regression analysis, determine the influence of these input raw materials (factors) on the mechanical properties: ultimate strength Rm, hardness HB of the final cast iron. The equations obtained will enable the calculation of the appropriate ratio of these three input raw materials to ensure the required mechanical properties of the final product. The equations obtained using DoE correspond more closely to the measured values than the equations obtained by LINEST, their calculation is more complex and requires the use of software. Based on these equations, taking into account the variability of the input data (for example, the fluctuation of the chemical composition of the iron scrap used), it is possible to estimate the proportion of castings with the required mechanical properties. For example, with a charge containing 40 % of technological residue, 22 % of returnable material, and 38 % of iron scrap with a variability of - 10 %, it will meet the requirements of the standard for tensile strength Rm of 99.85 % of castings and hardness HB of 81.88 % of castings.
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References14
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