scholarly journals Geometrical Parametric Optimisation of A356 Alloy Composite in a Two-Stage Casting Process for Automobile Wheel Covers using Response Surface Methodology

2020 ◽  
Vol 7 (1) ◽  
pp. 457-478 ◽  
Author(s):  
Sunday Oke ◽  
Stephen Chidera Nwafor ◽  
Chris Abiodun Ayanladun
Keyword(s):  
Response Surface Methodology ◽  
Response Surface ◽  
Regression Models ◽  
Predictive Accuracy ◽  
A356 Alloy ◽  
Casting Process ◽  
Sand Casting ◽  
Phase Method ◽  
Technical Solution ◽  
Two Phase

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.

OPTIMISATION OF SAND CASTING PROCESS FOR IMPELLER USING TAGUCHI METHODOLOGY

2020 ◽  
Vol XVII (2) ◽  
pp. 23-33
Author(s):  
Faisal Hafeez ◽  
Salman Hussain ◽  
Wasim Ahmad ◽  
Mirza Jahanzaib
Keyword(s):  
Surface Roughness ◽  
Surface Defects ◽  
A356 Alloy ◽  
Casting Process ◽  
Sand Casting ◽  
Gate Length ◽  
Taguchi Methodology ◽  
Confirmatory Tests ◽  
Binder Ratio ◽  
Response Measures

This paper presents the study to investigate the effects of binder ratio, in-gate length and pouring height on hardness, surface roughness and casting defects of sand casting process. Taguchi methodology with L9 orthogonal array was employed to design the experimentation. Sand casting of six blade impeller using A356 alloy was performed and empirical models for all the above response measures were formulated. Confirmatory tests and analysis of variance results confirmed the accuracy of the model. Binder ratio was found to be the most significant parameter affecting casting surface defects and surface roughness. This was followed by pouring height and in-gate length.

scholarly journals Enzyme optimization to reduce the viscosity of pitanga (Eugenia uniflora L.) juice

2016 ◽  
Vol 19 (0) ◽  
Author(s):  
Ricardo Schmitz Ongaratto ◽  
Luiz Antonio Viotto
Keyword(s):  
Response Surface Methodology ◽  
Response Surface ◽  
Incubation Time ◽  
Regression Models ◽  
Incubation Temperature ◽  
Correlation Coefficients ◽  
Enzyme Concentration ◽  
Optimum Conditions ◽  
Activity Time ◽  
Independent Variables

Summary The aim of this work was to separately evaluate the effects of pectinase and cellulase on the viscosity of pitanga juice, and determine the optimum conditions for their use employing response surface methodology. The independent variables were pectinase concentration (0-2.0 mg.g–1) and cellulase concentration (0-1.0 mg.g–1), activity time (10-110 min) and incubation temperature (23.2-56.8 °C). The use of pectinase and cellulase reduced the viscosity by about 15% and 25%, respectively. The results showed that enzyme concentration was the most important factor followed by activity time, and for the application of cellulase the incubation temperature had a significant effect too. The regression models showed correlation coefficients (R2) near to 0.90. The pectinase application conditions that led to the lowest viscosity were: concentration of 1.7 mg.g–1, incubation temperature of 37.6 °C and incubation time of 80 minutes, while for cellulase the values were: concentration of 1.0 mg.g-1, temperature range of 25 °C to 35 °C and incubation time of 110 minutes.

Optimization of Aqueous Two Phase Extraction of Proteins from Litopenaeus Vannamei Waste by Response Surface Methodology Coupled Multi-Objective Genetic Algorithm

2019 ◽  
Vol 15 (1) ◽  
Author(s):  
P. Saravana Pandian ◽  
S. Sindhanai Selvan ◽  
A. Subathira ◽  
S. Saravanan
Keyword(s):  
Genetic Algorithm ◽  
Molecular Weight ◽  
Response Surface Methodology ◽  
Response Surface ◽  
Litopenaeus Vannamei ◽  
Value Added ◽  
Phase System ◽  
Two Phase ◽  
Multi Objective ◽  
Multi Objective Genetic Algorithm

Abstract Waste generated from industrial processing of seafood is an enormous source of commercially valuable proteins. One among the underutilized seafood waste is shrimp waste, which primarily consists of head and carapace. Litopenaeus vannamei (L. vannamei) is the widely cultivated shrimp in Asia and contributes to 90 % of aggregate shrimp production in the world. This work was focused on extraction as well as purification of value-added proteins from L. vannamei waste in a single step aqueous two phase system (ATPS). Polyethylene glycol (PEG) and trisodium citrate system were chosen for the ATPS owing to their adequate partitioning and less toxic nature. Response surface methodology (RSM) was implemented for the optimization of independent process variables such as PEG molecular weight (2000 to 6000), pH (6 to 8) and temperature (25 to 45 °C). The results obtained from RSM were further validated using a Multi-objective genetic algorithm (MGA). At the optimized condition of PEG molecular weight 2000, pH 8 and temperature 35 °C, maximum partition coefficient and protein yield were found to be 2.79 and 92.37 %, respectively. Thus, L. vannamei waste was proved to be rich in proteins, which could be processed industrially through cost-effective non-polluting ATPS extraction, and RSM coupled MGA could be a potential tool for such process optimization.

scholarly journals Optimisation of Casting Geometries for A356 Alloy Composites Reinforced with Organic Materials using Box-Behnken Design Methodology

2020 ◽  
Vol 7 (2) ◽  
pp. 524-553
Author(s):  
Sunday Oke ◽  
Stephen Chidera Nwafor ◽  
Chris Abiodun Ayanladun
Keyword(s):  
Regression Models ◽  
Real Life ◽  
A356 Alloy ◽  
Dimensional Accuracy ◽  
Transformation Process ◽  
Comprehensive Treatment ◽  
Two Phase ◽  
Box Behnken Design ◽  
Life Circumstance ◽  
Geometrical Features

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.

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