Linear and non-linear statistical modelling of green sand mould system

Parappagoudar, Mahesh B.; Pratihar, Dilip Kumar; Datta, G. L.

doi:10.1179/136404607x184952

Cited by 48 publications

(29 citation statements)

References 3 publications

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“…During the modeling process, percentage of bentonite, water and curing time were treated as the inputs and bulk density, compactability, permeability and compression strength were considered as the outputs of the system. Parappagoudar et al performed the linear and nonlinear modeling of green sand mould system [17] and cement bonded sand mould system [18] using statistical regression analysis. The parameters, such as AFS grain fineness number, percentage water, clay, number of strokes and permeability, green compression strength, mould hardness, bulk density were considered as inputs and outputs, respectively for the green sand mould system.…”

Section: Introductionmentioning

confidence: 99%

Modeling and Analysis of Resin Bonded Sand Mould System Using Design of Experiments and Central Composite Design

Surekha

Rao²,

Rao

et al. 2012

Journal for Manufacturing Science and Production

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In this paper an attempt has been made for linear and non linear modeling of resin bonded sand mould system using full factorial design of experiments and response surface methodology, respectively. It is important to note that the quality of castings produced using the resin bonded sand mould system depends largely on properties of moulds, which are influenced by the characteristics of sand, like type of sand, grain fineness number, grain size distribution and quantity and type of resin, catalyst, curing time etc. In the present study, percentage of resin, percentage of hardener, number of strokes and curing time are considered as input parameters and the mould properties, such as compression strength, shear strength, tensile strength and permeability are treated as responses. In the present work, phenol formaldehyde is used as the resin whereas tetrahydrophthalic anhydride as the hardener. A two level full factorial and three level central composite designs are utilized to develop input-output relationships. Surface plots and main effects plots are used to study the effects of amount of resign, amount of hardener, number of strokes and curing time on the responses, namely, compression strength, tensile strength, shear strength and permeability. Moreover, the adequacies of the developed models have been tested using analysis of variance. The prediction accuracy of the developed models have been tested with the help of twenty test cases and found reasonably good accuracy.

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Section: Introductionmentioning

confidence: 99%

Modeling and Analysis of Resin Bonded Sand Mould System Using Design of Experiments and Central Composite Design

Surekha

Rao²,

Rao

et al. 2012

Journal for Manufacturing Science and Production

Self Cite

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“…Resistance method is the first electrical measuring method used in green sand, and it includes DC resistance method and AC impedance method [8]. The measurement basis is that the resistance or impedance differs when the moisture content is different.…”

Section: A Resistance Methodsmentioning

confidence: 99%

Review on the Methods for Measuring the Moisture Content of Green Sand

Gao¹,

Shi²,

Dong³

2012

Proceedings of the 1st International Conference on Mechanical Engineering and Material Science

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Abstract-The moisture of green sand mainly refers to the water that can be got rid of at the temperature of 105℃-110℃, and it is affecting sand quality, one of the most important and sensitive parameters that affect the quality of green sand. At present, the methods for measuring the moisture content can be divided into two categories of direct and indirect methods. The precision of the direct method is high while the measurement period is long, so it does not suits to online measurement, and is often used to calibrate the indirect methods. In the indirect methods, the moisture content can be online obtained by measuring the physical parameters or performance indexes related to the moisture. They mainly include the resistance method, capacitance method, inductive method, microwave method, infrared spectrometry method, and nuclear magnetic resonance method.

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“…Not much work has been reported yet with the use of non linear regression models for the applications in squeeze casting process. However linear and non-linear regression models are successfully implemented to develop the input-output relationship of various casting processes namely cement bonded moulding [62,63], green sand moulding [64,65], resin bonded sand mould system [66], sodium silicate-bonded moulding system [67], die casting [68][69][70] and evaporative casting process [71]. The following key observations drawn from the linear and non-linear regression models such as, 1) Majority of the authors studied the input-output relationships of the process using main effect and the surface plots.…”

Section: Linear and Non-linear Regression Modelsmentioning

confidence: 99%

Modelling in Squeeze Casting Process-Present State and Future Perspectives

Patel¹,

MB²

2015

Adv Automob Eng

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The growing demand in today's competitive manufacturing environment has encouraged the researchers to develop and apply modelling tools. The development and application of modelling tools help the casting industries to considerably increase productivity and casting quality. Till date there is no universal standard available to model and optimize any of the manufacturing processes. However the present work discusses the advantages and limitations of some conventional and non-conventional modelling tools applied for various casting processes. In addition the research effort made by various authors till date in modelling and optimization of the squeeze casting process has been reported. Furthermore the necessary steps for prediction and optimization are high lightened by identifying the trends in the literature. Ultimately this research paper explores the scope for future research in online control of the process by automatically adjusting the squeeze cast process parameters through reverse prediction by utilizing the soft computing tools namely, Neural Network, Genetic Algorithms, Fuzzy-logic Controllers and their different combinations. The present work also proposed a detailed methodology, starting from the selection of process variables till the best process variable combinations for extreme values of the outputs responsible for better product quality using experimental, prediction and optimization methodology. Citation: Manjunath Patel GC, Krishna P, Parappagoudar MB (2015) Modelling in Squeeze Casting Process-Present State and Future Perspectives. Modelling in Squeeze Casting

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Linear and non-linear statistical modelling of green sand mould system

Cited by 48 publications

References 3 publications

Modeling and Analysis of Resin Bonded Sand Mould System Using Design of Experiments and Central Composite Design

Modeling and Analysis of Resin Bonded Sand Mould System Using Design of Experiments and Central Composite Design

Review on the Methods for Measuring the Moisture Content of Green Sand

Modelling in Squeeze Casting Process-Present State and Future Perspectives

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