Design of experiment analysis of elevated temperature wear of Mg-WC nano-composites

Current study explores the effect of selected process parameters i.e. wt.% of reinforcement (A), elevated temperature (B) and load (C) on wear characteristics of Mg-WC nanocomposites using Taguchi robust design concept. Ultrasonic treated stir casting is employed to synthesize nanocomposites. Three levels for every factor are taken into consideration and accordingly L27 orthogonal array (OA) is used for minimization of wear rate. Main effect plot is generated to investigate the important parameters and optimality is also predicted from the main effect plot. Optimal condition for minimum wear rate is 2wt.% of WC, 100°C temperature and 20N load (A3B1C1). Interaction plots are generated to scrutinize the interaction outcome between selected parameters. ANOVA study is executed to evaluate significant parameters and their effective handout on output. Current investigation reveals, Wt.% of WC is the most significant factor while temperature and load are moderately significant. Among the interacting parameters, interaction between wt.% of WC & temperature (A×B) has moderate significance. Wt.% of WC (A) has 43.135% contribution while temperature (B), load (C) and interaction between wt.% of WC & temperature (A×B) have 26.623%, 19.037% and 5.639% contribution respectively. Residual plots for wear rate are discussed and confirmation test finally helps to validate present experimental model. S/N ratio is improved by 4.411 dB (48.60%) than the initial condition.

Simulation of Friction Stir Spot Welding of Copper and Aluminium During Plunging Phase

Simulation is limited and remains briefly addressed in the literature of friction stir spot welding (FSSW) process in joining dissimilar copper and aluminium. Thus, this study simulated the FSSW process of copper and aluminium to investigate the peak temperature during the plunging phase produced by all possible combinations of levels for tool rotational speed, plunge rate, and plunge depth according to the full factorial design. The modeling was established by Coupled Eulerian-Lagrangian (CEL) model and ‘dynamic, temperature-displacement, explicit’ analysis. The highest peak temperature of 994.4 oC was produced by 2400 rpm rotational speed, 100 mm/min plunge rate, and 1.6 mm plunge depth. The combination was suggested to be the optimum welding parameters in joining copper to aluminium as sufficient heat input was essential to soften the area around the welding tool and adequately plasticize the material. Three sets of confirmation tests presented consistent responses with a mean peak temperature of 994.4 °C, which validated that the response produced by the suggested optimum welding parameters was reliable. The statistical result reported that the variability in the factors could explain 84.12% of the variability in the response. However, only the rotational speed and plunge depth were statistically significant. The residual plots showed that the regression line model was valid.

Factors Affecting Carbonation Depth in Foamed Concrete Bricks for Accelerate CO2 Sequestration

Foamed concrete bricks (FCB) have high levels of porosity to sequestrate atmospheric CO2 in the form of calcium carbonate CaCO3 via acceleration of carbonation depth. The effect of density and curing conditions on CO2 sequestration in FCB was investigated in this research to optimize carbonation depth. Statistical analysis using 2k factorial and response surface methodology (RSM) comprising 11 runs and eight additional runs was used to optimize the carbonation depth of FCB for 28 days (d). The main factors selected for the carbonation studies include density, temperature and CO2 concentration. The curing of the FCB was performed in the chamber. The results indicated that all factors significantly affected the carbonation depth of FCB. The optimum carbonation depth was 9.7 mm, which was determined at conditions; 1300 kg/m3, 40 °C, and 20% of CO2 concentration after 28 d. Analysis of variance (ANOVA) and residual plots demonstrated the accuracy of the regression equation with a predicted R2 of 89.43%, which confirms the reliability of the predicted model.

Optimizing Residual Plots for Likert Data

Likert-type questions are widely used in survey in social science and produce discrete and repeated data. When plotting residuals from a linear model whose dependent variable is measured by Likert-type question, researchers might have problem observing the plot which is always with parallel lines. Adding some disturbance to the dependent variable before plotting can optimize the plot and solve this problem.

Adsorption-Desorption Mechanism of Synthesized Benzimidazole Based Fungicide 2-(3’-Pyridyl) on Selected Soil Minerals

The adsorption and desorption phenomenon of synthesized Benzimidazole based fungicide, 2-(3’-pyridyl)benzimidazole (PyBlm), was investigated by batch equilibrium method. Four soil minerals were utilized for thesorption studies including; alumina, silica, muscovite and montmorillonite. Highest value of adsorption coefficient(Kd(ads)), obtained for montmorillonite mineral (Mx (Al, Fe, Mg4) Si8O20 (OH4)) was 2779 µg ml-1. Highest rate ofadsorption is attributable to its considerably large surface area of 628 m2g-1 and highest inter-lattice d-spacing, 10 Å.Highest desorption (Kd(des)) was also observed in montmorillonite mineral (21.45 µg ml-1). Montmorillonite thusdisplayed increased sorption capacity for PyBlm among all tested minerals. Hysteresis coefficient ranged from 0.58 to3. The results were statistically evaluated by using one-way analysis of variance (ANOVA). Furthermore, statisticalevaluation done with the help of Minitab 17 expressed the good fitting of the obtained results, which was shown bymeans of residual plots. Current research which suggests the variable adsorption and desorption of PyBlm expresses theprofound dependence of PyBlm interaction on the physicochemical characteristics of the selected minerals. Allminerals except montmorillonite expressing poor adsorption signifying the percolation of PyBlm through them towardsthe lower soil profiles. Results obtained in the present research show of that montmorillonite in firmly interacting withthe PyBlm molecules and thus alleviating the possibility of PyBlm percolation to lower soil profiles.

Adsorption-Desorption Mechanism of Synthesized Benzimidazole Based Fungicide 2-(3’-Pyridyl) on Selected Soil Minerals

The adsorption and desorption phenomenon of synthesized Benzimidazole based fungicide, 2-(3’-pyridyl)benzimidazole (PyBlm), was investigated by batch equilibrium method. Four soil minerals were utilized for thesorption studies including; alumina, silica, muscovite and montmorillonite. Highest value of adsorption coefficient(Kd(ads)), obtained for montmorillonite mineral (Mx (Al, Fe, Mg4) Si8O20 (OH4)) was 2779 µg ml-1. Highest rate ofadsorption is attributable to its considerably large surface area of 628 m2g-1 and highest inter-lattice d-spacing, 10 Å.Highest desorption (Kd(des)) was also observed in montmorillonite mineral (21.45 µg ml-1). Montmorillonite thusdisplayed increased sorption capacity for PyBlm among all tested minerals. Hysteresis coefficient ranged from 0.58 to3. The results were statistically evaluated by using one-way analysis of variance (ANOVA). Furthermore, statisticalevaluation done with the help of Minitab 17 expressed the good fitting of the obtained results, which was shown bymeans of residual plots. Current research which suggests the variable adsorption and desorption of PyBlm expresses theprofound dependence of PyBlm interaction on the physicochemical characteristics of the selected minerals. Allminerals except montmorillonite expressing poor adsorption signifying the percolation of PyBlm through them towardsthe lower soil profiles. Results obtained in the present research show of that montmorillonite in firmly interacting withthe PyBlm molecules and thus alleviating the possibility of PyBlm percolation to lower soil profiles.

Predicting the performance of the players in NBA Players by divided regression analysis

A divided regression model is built to predict the performance of the players in the National Basketball Association (NBA) from year 1997 until year 2017. The whole data set is divided into five groups of sub data sets and multiple linear regression model is employed to model each of the sub data set. In addition, the relationships among independent variables are checked by using variance inflation factor (VIF) to identify the risk of having multicollinearity in the data. Moreover, non-linearity of regression model, non-constancy of error variance and non-normality of error terms are investigated by plotting residual plots and quantile-quantile plots. Finally, a divided regression model is built by combining the results obtained from the sub data sets and the performance of the divided regression model is verified.

Assessing Student Ability to Interpret Regression Diagnostic Plots

Simple linear regression analysis is an important component of introductory statistics courses, and testing the validity of its assumptions is an important part of regression modeling and analysis. The traditional approach in introductory statistics courses is to use residual plots to assess possible departures from the key assumptions. Confirmatory statistics are typically not used. But do undergraduate students in these introductory courses have the ability to assess residual plots correctly? This paper presents the results of an Institutional Review Board (IRB) approved study to address this question. The results are interesting and have implications for the teaching of regression in introductory courses as well as for textbook authors who cater to these courses.