The study examined two types of design of experiments (DoE) methods for injection molding of a molded part. It evaluated them using an artificial neural network (ANN) and a support vector machine (SVM) via cross-validation and holdout validation. The innovative goal is to identify the most efficient and successful ways for modeling varied DoE. The influence of four processing parameters on the volumetric shrinkage of a thin polystyrene plate sample is simulated using factorial design and orthogonal Taguchi arrays design. As measured by root mean square error (RMSE), the prediction performance revealed that DoE with eight experimental points as in \({2}^{4-1}\) for fractional factorial design and L8 for orthogonal Taguchi design is particularly efficient for this modeling simulation problem. Both design methods are beneficial and efficient because orthogonal Taguchi arrays play an essential role when the accuracy of fractional factorial designs is insufficient.
The use of optimal parameter values in an experimental investigation for minimizing surface roughness has always been the design trend of many machining operation without checking the variation levels of the process parameters if it has any measure of significant difference or not in minimizing surface roughness of the workpiece. The study carried out a comparative analysis between low, intermediate and high level of cutting speed, feed, depth of cut and tool on mild steel, stainless steel and brass using one-way ANOVA approach with the aid of statistical package for social sciences (SPSS), version 17 based on drilling operation. The findings revealed that there were no statistically significant differences between the levels for the respective workpiece in minimizing surface roughness. The result implies that the low, intermediate and high level plays equal contributing effect in minimizing surface roughness in drilling mild steel, stainless steel and brass. The study concludes that preference should not only be given to a particular level in an experimental investigation because minimum surface roughness can be achieved at any level which we might least expected.
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