Statistical Analysis and Optimization of the Experimental Results on Performance of Green Aluminum-7075 Hybrid Composites

Abstract

The present study assessed the potential of engaging response surface analysis in the experimental design, modeling, and optimization of the strength performance of aluminum-7075 green composite. The design of the experiment was carried out via the Box–Behnken method and the independent variables are rice husk ash (RHA) at 3–12 wt.%, glass powder (GP) at 2–10 wt.%, and stirring temperature (ST) at 600–800 °C. Responses examined are yield, ultimate tensile, flexural, and impact strengths, as well as microhardness and compressive strength. ANOVA analysis revealed that the input factors had consequential contributions to each response, eventually presenting regression models statistically fit to represent the experimental data, further affirmed by the diagnostic plots. The result of the optimization envisaged an optimal combination at 7.2% RHA, 6.2 GP, and 695 °C with a desirability of 0.910. A comparison between the predicted values for the responses and the values of the validation experiment revealed an error of <5% for each response. Consequently, the models are certified adequate for response predictions at 95% confidence, and the optimum combination is adequate for the design of the composite.

Modeling and optimization of green-Al 6061 prepared from environmentally source materials

“…10 presents the residual vs predicted plot and it is noted that the plots are located randomly around the zero line for all responses. There is, therefore, no correlation between the data further depicting good models [ 24 ].

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“…As previously reported [ 24 ], optimization was done to determine the optimum combination of the input variables in realizing the balance performance of the composite [ 44 ]. The constraints for the optimization procedure are indicated in Table 9 .…”

“…[ 23 ]. Similar to previous studies [ 16 , 24 ], response surface method was utilized in the composite combination, modeling of the property parameters, and derivation of optimization input parameters toward achieving balanced performance. The input variables are PKA powders, RHA proportion, and stirring temperature.…”

Optimization on hardening process parameters of alumnium alloy 7075 based composites using gra approach

“…The contribution percentages of heating temperature, heating duration, and cooling environment are 42.08%, 37.05%, and 20.21%, respectively. The experiment's reliability is high, as indicated by its high adjusted R 2 value, as shown in table 7 [33]. The presence of these precipitates within the crystal lattice significantly increases the material's micro hardness and wear resistance.…”