Application of Multiple Regression Analysis for Wire Electrical Discharge Machining of Stainless Steel 304

Thejasree, P.; Manikandan, N.; Binoj, J. S.; Krishnamachary, P. C.; Arulkirubakaran, D.; Reddy, J. Janardhana; Reddy, V. Indra Karan; Saikiran, K.; Vardhan, C. Vishnu

doi:10.1007/978-981-19-0244-4_18

Cited by 3 publications

(1 citation statement)

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“…Linear regression is a standard method to study the quantitative relationship between the independent variable and the dependent variable, which is called multiple linear regression when there is more than one independent variable, and is widely used in data analysis in a variety of scenarios [20][21][22]. Its general expression is as follows:…”

Section: Multiple Regression Methodsmentioning

confidence: 99%

A Quantitative and Optimization Model for Microstructure Uniformity of Sinter Based on Multiple Regression-NSGA2

Fang,

Li,

Liu

et al. 2024

Metals

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The degree of homogeneity of the sintered ore phase structure directly determines its quality index. A sinter ore quality evaluation method based on the quantification of the homogeneity of the mineral phase structure is proposed. First, the magnetite particle size characteristics in the ore phase structures with different degrees of homogeneity were summarized under a polarized light microscope, and a criterion for evaluating the uniformity of the sintered ore phase structure based on the magnetite content of different particle size grades was determined. Second, a multiple regression model was established for the raw material composition ratio of magnetite with varying particle size grades. Finally, the multiple regression model was optimized using the second-generation non-dominated sorting genetic algorithm (NSGA2). The results show that mineral phase structure analysis categorized the magnetite particle sizes into <30 μm, 30~60 μm, and >60 μm. The adjusted R2 of the multiple regression model of the chemical composition of raw materials and the proportion of magnetite of each particle size grade were all greater than 0.95, and the p values were all <0.05, indicating a high degree of model fitting. Using model analysis, the single factor and the interaction between the multiple factors that significantly influence the proportion of magnetite in the three particle size grades were determined. The multivariate regression model was optimized using the NSGA2 algorithm to determine the ratios of Al2O3 mass% = 1.82, MgO mass% = 1.50, and R(CaO mass%/SiO2 mass%) = 1.84 for the highest degree of uniformity of the sintered ores. Under this sintering condition, the micro-mineral phase structure became more homogeneous, confirming the model’s reliability.

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Section: Multiple Regression Methodsmentioning

confidence: 99%