2010
DOI: 10.4236/jmmce.2010.910066
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The Development of Mathematical Model for the Prediction of Ageing Behaviour for Al-Cu-Mg/Bagasse Ash Particulate Composites

Abstract: The thermal ageing behaviour model of Al-Cu-Mg/Bagasse ash particulate composites with 2-10wt% bagasse ash particles produced by double stir-casting method was developed in terms of weight fraction of bagasse ash, ageing temperature and time. Hardness values measurement was used in determining the ageing behaviour, after solution and age-hardened heat-treatment. The experimental results demonstrate that the bagasse ash was the major parameter in the ageing behaviour, followed by ageing temperature. The hardnes… Show more

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Cited by 21 publications
(17 citation statements)
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“…The model selected includes the effects of main variables first order and second-order interactions of all variables. Hence the general model is written as [11,15] …”
Section: Development Of Mathematical Modelmentioning
confidence: 99%
“…The model selected includes the effects of main variables first order and second-order interactions of all variables. Hence the general model is written as [11,15] …”
Section: Development Of Mathematical Modelmentioning
confidence: 99%
“…This is aimed at providing solution to problems posed by high cost and limited availability of conventional ceramic reinforcements [14][15][16][17]. Industrial wastes and agro waste derivatives are some of the alternative reinforcing materials that have been investigated [15,17,18]. The results obtained from the investigations carried out on these alternative reinforcements have been promising as they show significant improvement in the properties of the composites developed over the unreinforced alloy.…”
Section: Introductionmentioning
confidence: 99%
“…Four methods were considered for response surface modelling: Box-Behnken design (BBD), central composite design (CCD), full factorial design (FFD), and D-optimal design (DOD). Thus, model equations (4), (5), (6), and (7) are associated with fractional factorial method (FRFDRSMs), model equations (8), (9), (10), and (11) are associated with Placket-Burman method (PBDRSMs), and the 2 feasible response model equations (12) and (13) are associated with screening using one variable at-a-time (OVAATRSMs). Tables 6, 7, and 8 show the constants in all the equations:…”
Section: Development Of Response Surfacesmentioning
confidence: 99%
“…The application of 3-level full factorial is limited only to four factors or lower due to the accompanied large number of runs for large number of uncertainties. However, 2-level factorial designs (fractional designs) have also been used where 3-level full factorial is infeasible for response surface construction [10].…”
Section: Introductionmentioning
confidence: 99%