The A356 alloy is widely known to exhibit an extremely superior casting, machining, mechanical, and corrosion resistance properties. Despite these, it constitutes an environmental nuisance at its improper disposal for worn-out engine blocks. Also, organic reinforcements have the potentials to reduce the environmental impacts of composites. Consequently, there exits significant research potential to fuse A356 alloy with organic materials to obtain enhanced composite properties. In the area of aluminium matrix, as melting and solidifications of materials are done the accuracy of measurements is driven by the huge array of process parameters and the geometrical aspect of cast components is important. For these reasons, we attempt to solve the problem of optimising the geometry of casts in a complicated scenario with the use of the robust Taguchi's methods. To optimise the framework, the significant process parameters are identified and their effects studied in a route using Taguchi, Taguchi-Pareto and Taguchi–ABC methods. Parameters such as the volume of the cast, length, weight, density, height, width, breadth, weight loss and the total weight of organic materials infused into the melting process were studied for parametric changes, interactions and optimisation with L27 orthogonal array. The analysis of variance for the A356 alloy cast revealed that the density parameter of cast 1 had the highest and major significant effect on the casting process with a variance of 333573, followed by weight parameter of casts 1 and 2, total weight of organic material parameters and weight loss with the variance values of 0.007, 0.005, 0.001 and 0.004, respectively. The variance of other parameters was insignificant to the A356 alloy cast.

The aspect of reinforcing re-usable A356 alloys with organic matters is a principal, success yet expanding in scope regarding new products development. Despite, the use of Taguchi-Pareto method has potentials for improvement. The purpose of this article is to examine the factor analysis- Taguchi-Pareto method at which identification of the key factors could be achieved while concurrently optimizing the factors. The point of integration is at the variance determination of the unrotated factor loadings and communality. The synergy between factor analysis and Taguchi-Pareto method provides practical assistance to foundry engineers to concurrently select key factors while optimising them. The factors were noted to be effective and responsive to the proposed method. The delta values are 0, 0.84 and 0 for LC1, WC1 and HC1, respectively while the ranks are WC1 as first while the second position is shared between LC1 and HC1. The optimal parametric settings are LC11 WC11 HC11, LC11 WC11 HC12, LC12 WC11 HC11 or LC12 WC11 HC12­. One of the optimal parametric settings is 0.280m of LC1 with 1.788kg of WC1 and 0.036m of HC1. The finding provides novel steps to the concurrent factor identification and optimisation in the choice of optimal parametric setting for the process. It is the first step towards sustainable foundry practice.

In an earlier article, the central composite design was applied to the determination of geometrical features of casts in a two-phase transformation process to produce the wheel covers of automobiles whereby the A356 alloy is reinforced with organic substances for composite property enhancement. This article reexamines the assumptions in that circumstance to revise and expand the optimisation through the response surface methodology to a new method, Box-Behnken design (BBD), to facilitate a comprehensive treatment of the sand casting product parameters. Casting geometrical optimisation can be modelled to involve lengths, breadths, widths, heights, densities of casts and weight loss, varied at three discrete levels. The parameters are translated into codes (–1,0,1) with specified actual, minimum and maximum values. The framework, validated by published literature data, indicates its feasibility in a real-life circumstance. This article assessed the effects of the casting geometry parameters on the responses. Besides, it examined the accuracy of the parameters to predict in the regression models deployed. It was concluded that the BBD and the regression models are adequate and predict correctly. The BBD can be applied by composite developers to improve casting dimensional accuracy and economics.

In recent years, novel products from out–of–use A356 alloy engine components are increasingly produced for the automobile industries. Despite being a promising method the sand casting of these products reveals an inadequately understood cast geometry phenomenon for the process. At present, there is no technical solution to the optimisation of cast geometries for A356 alloy reconfigured into composites through organic matter reinforcements. This paper models and analyse sand casting process product geometries in a two–phase method. It utilises the response surface methodology with data on inputs and outputs to create the regression. Volume and density of the first casting process and the weight loss were evaluated for the various groupings of casting process variables, including length, weight, height, width of product for the first casting, weight, length, breadth of the product for the second casting, and the total weight of organic materials. The input and output associations were established in two models of regression analysis representing the central composite design, CCD. The influences of the cast geometrical variables on the evaluated responses were analysed. Furthermore, the predictive accuracy of the two regression models was evaluated. Results revealed that the applied CCD and the regression models reveals statistical adequacy and are competent to predict accurately.