This paper will critique the article on Multiple regression testing, to statistically analyze indefinite chilled cast–iron rolls manufacturing using multiple regression testing, by authors Vdovin K N, Zavalishchin A N, Gorlenko D A and Feoktistov N A (2016), Kiss (2008), Krawczyk (2011), Schröder (2002) and Kiss I, Alexa V, Ratiu S A and Cioata V G (2014). This critique will explain the research design used by the authors. The analysis will also explain the reason why the authors use multiple regression testing to display the data and the meaningful effect size reported by the authors. Besides, this critique will identify the research problem, purpose, framework, questions, method of data collection, tools used to collect the data, data point and, data source and data analysis.
The research design used by the authors was mainly, the statistical fundamental methods that allow to reach conclusions, observed values of metallurgical processes, the repartition of the frequencies of various variables, the verification of the validity of various premises and the research of the dependencies of different parameters and their interactions (Kiss, et al., 2010). The realization of the optimum chemical compositions of the cast-iron provides a technical and efficient way to test the exploitation properties, the materials used by the rolling mills for manufacturing. The authors also use the main aspects of the roll material present to analyze the influences of the chemical composition on the mechanical properties of the indefinite cast iron rolls (Krawczyk, 2011). The authors used multiple regression to present the mathematical correlations and graphical interpretation between the hardness and the chemical composition of the materials. The optimal values of the hardness of the indefinite cast iron rolls were obtained and the results and the evidence from the actual experiment were presented using multiple regression equations, correlation coefficients, and graphical representations.
The multiple regression was used to test indefinite chilled cast–iron rolls manufacturing material properties. The reason why authors used multiple regression testing is that it enables them to determine the relative influence of one or more variables to the criterion value and identify the outliers and anomalies in the results (Krawczyk, 2011). The multiple regression testing is an appropriate method since it helps in analyzing the cast roll material characteristics by generating several regression surfaces and matrices of the level curves, which can be represented and interpreted by the roll makers using correlation and regression charts between the analyzed variables.
The authors displayed various data which includes the average values of the three variables ([C], [Si], [Ni]) and the hardness (HS), necessary for the calculation of the optimal form of modeling and the variables hardness variation limits as shown in the tables below.
Table 1: The average values of variables (Vdovin et al., 2016).
Table 2: The average variation domains of variables (Vdovin et al., 2016).
The optimal form of multiple regression modeling equations was presented by the authors (Kiss, et al., 2010) as shown below.
The authors represent the results of the regression surfaces, belonging to the three–dimensional space graphically with matrices of level curves, which can be interpreted by roll makers as correlation charts. In this case, independent variables ([C], [Si], [Ni]) and dependent variables HS (body) and HS (necks) were obtained between the requested limits for the roll’s body surface and the roll’s necks and the core. The results from multiple regression did not stand alone, to ensure the accuracy and certainty of the results obtained MATLAB software was used.
The authors reported the effect sizes for the optimal form of modelling for dependent variables HS (body) and HS (necks). The optimal form of modeling in the case of HS(body) = f([C], [Si], [Ni]) is given by the equation (1), where the correlation coefficient is rfHS(body) = f([C], [Si], [Ni]) = 0.6462 and the average square aberration from the regression surface is sfHS(body) = f([C], [Si], [Ni]) = 3.0771 (Vdovin et al., 2016). The optimal form of modeling in the case of HS (necks) = f([C], [Si], [Ni]) is given by the equation (2), where the correlation coefficient is rfHS(necks) = f([C], [Si], [Ni]) = 0.6499 and the average square aberration from the regression surface is sfHS(necks) = f([C], [Si], [Ni]) = 2.9500 (Vdovin et al., 2016). The effect sizes are meaningful sizes since the range between the effect sizes of HS (body) and HS (necks) correlation coefficient (0.6499-0.6462=0.0037) and the average square aberration from the regression surface (3.0771-2.9500=0.1271) is small which indicates that the relationship between HS (body) and HS (necks) are statistically significant.
The research design alignment table below shows article critique summary on the research problem, purpose, framework, questions, method of data collection, tools used to collect the data, data point and, data source and data analysis.
|Research problem, purpose and framework||Research questions, method, and design||Data collection tools and data sources||Data points||Data analysis|
Assumption of properties of the cast-iron material based on their microstructure formed during solidification in the casting mould and under the influence of cooling rate.
To analyze the properties of the material rolling mills rolls are manufacturing using metallurgical processes and statistically analyze the results using multiple regression (Kiss, et al., 2010).
The statistical fundamental methods that allow reaching conclusions, observed values of metallurgical processes, the repartition of the frequencies of various variables, the verification of the validity of various premises and the research of the dependencies of different parameters and their interactions.
Is metallurgical processes the optimum method of analyzing the properties of the material rolling mills rolls are manufacturing?
The optimal values of the hardness of the indefinite cast iron rolls were obtained using metallurgical processes and the results and the evidence from the actual experiment were presented using multiple regression equations, correlation coefficients, and graphical representations.
|The main data collection tools involved in this research study is a statistical experiment of cast-iron material. The data were collected within the framework of research and the parts of the rolls (body and necks) (Krawczyk, 2011).||Planning and research on the industrial data were obtained from the rolls manufacturing area.||As results of the applied mathematical modeling, several multicomponent were described. Using multiple regression- equations, the dimensions spaces were determined. Also, several regression surfaces and matrices of the level curves were represented using multiple regression equations, correlation coefficients, and graphical representations.
From the table above, the logical progression from the research problem to the purpose of the study indicates clearly that the purpose of the study which is to solve the research problem. The framework provides the required outline to be used in solving the research problems and answering the research questions. The research question used in this research design relates to the purpose of the study, research problem and the framework since the research question addresses the research problem to achieve the purpose of the research. Additionally, the data points help in answering the research question and completing the data analysis of the data collected. Data analysis will help us to come up with the conclusion of the research question.
Vdovin, K. N., Zavalishchin, A. N., Gorlenko, D. A., & Feoktistov, N. A. (2016). Structure and properties of cast iron designated for the working layer of rolls. Journal of Materials Science Research, 5(1), 77-88.
Krawczyk, J. (2011). Microstructure and tribological properties of mottled cast iron with different chemical composition. Archives of Materials Science and Engineering, 51(1), 5-15.
Kiss, I. M. R. E., Cioata, V. G., & Alexa, V. A. S. I. L. E. (2010). Increasing the rolling–mill rolls quality in some multidisciplinary research. Acta Technica Corviniensis–Bulletin of Engineering, 31-36.
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