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

Surekha, B.; Rao, D. Hanumantha; Rao, G. Krishna Mohan; Vundavilli, Pandu R.; Parappagoudar, Mahesh B.

doi:10.1515/jmsp-2012-0003

Cited by 14 publications

(21 citation statements)

References 9 publications

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“…Selection of the operating range of the process parameters is important because too wide a selection may form an infeasible range for the response surface, while a narrow range can result in poor information regarding sensitivity [14]. The levels for each process parameter were decided on the basis of information provided by industry, initial experiments and consulting the literature (Table S3).…”

Section: Selecting the Appropriate Levels For The Process Parametersmentioning

confidence: 99%

Modelling of the Effect of Concentrated Nitration Conditions on the Efficiency of the Production of 3,7-Dinitro-1,3,5,7-tetraazabicyclo[3,3,1]nonane (DPT)

Keshavarz¹,

Hadi²,

Esmaeilpour³

et al. 2018

Cent. Eur. J. Energ. Mater.

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3,7-Dinitro-1, 3,5,7-tetraazabicyclo[3,3,1]nonane (DPT) is one of the most important intermediates in the synthesis of octahydro-1,3,5,7-tetranitro-1,3,5,7-tetrazocine (HMX). A suitably modified Bachmann process, nitrolysis of solid hexamine in the presence of ammonium nitrate-nitric acid and acetic anhydride on a laboratory scale, is introduced to increase the efficiency, production capacity and purity of the DPT produced. Various quantitative and qualitative analytical methods were used for the identification and quality control of the product. A central composite design (CCD) of experiments was used to optimize the production process, increasing the production capacity, reducing the amount of acetic acid as the reaction medium to a suitable limit, and examining the effects of the main factors impacting on the efficiency of the nitration, e.g. the volume of ammonium nitrate-nitric acid solution, nitration temperature reactor addition time and volume of acetic anhydride. The overall results indicated that DPT was obtained with an efficiency of 64.58% and a production capacity of 20.77 (100 g·mL ).

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Section: Selecting the Appropriate Levels For The Process Parametersmentioning

confidence: 99%

Modelling of the Effect of Concentrated Nitration Conditions on the Efficiency of the Production of 3,7-Dinitro-1,3,5,7-tetraazabicyclo[3,3,1]nonane (DPT)

Keshavarz¹,

Hadi²,

Esmaeilpour³

et al. 2018

Cent. Eur. J. Energ. Mater.

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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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“…Further, Caylak and Mahnken [4] experimentally studied the effect of solidification of casting on the thermo-mechanical properties of the resin bonded sand. Few researchers had utilized the statistical modeling technique to establish the inputoutput relationships in clay bonded [5], sodium silicate bonded [6] and resin bonded [7] sand mould systems. It is important to note that, the statistical modeling will help to establish accurate and response-wise relationships.…”

Section: Introductionmentioning

confidence: 99%

ABC and GA Optimized NN to Model Resin Bonded Mould/Core Sand System: A Soft Computing-based Approach

Vundavilli

Surekha

Parappagoudar³

2014

Journal for Manufacturing Science and Production

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Resin bonded sand system is an emerging area, and it can be used to produce dimensionally accurate castings with good surface finish. In the present paper, experimental investigations are carried out on the resin bonded cores, to develop a non-linear mathematical model, using the concept of design of experiments. Subsequently, an artificial neural network (ANN) with four neurons each on input and output layers has been used to model the resin bonded sand system. It is important to note that the process parameters, such as percentage of resin, percentage of hardener, number of strokes and curing time are considered as inputs and the mechanical properties of the core, namely compression strength, tensile strength, shear strength and permeability are treated as the outputs of the network. It is to be noted that the performance of developed ANN depends on several factors of the network, such as type of transfer functions, coefficients of transfer functions, number of neurons in the hidden layer and connecting weights between different layers. In the present study, two population based search and optimization algorithms, namely genetic algorithm (GA) and artificial bee colony (ABC) are used for optimizing the parameters of ANN. It has been observed that both GA and ABC trained neural networks (that is, GA-NN and ABC-NN) are found to have good agreement with the experimental data and can be used effectively to model the resin bonded core sand system.

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

Cited by 14 publications

References 9 publications

Modelling of the Effect of Concentrated Nitration Conditions on the Efficiency of the Production of 3,7-Dinitro-1,3,5,7-tetraazabicyclo[3,3,1]nonane (DPT)

Modelling of the Effect of Concentrated Nitration Conditions on the Efficiency of the Production of 3,7-Dinitro-1,3,5,7-tetraazabicyclo[3,3,1]nonane (DPT)

Modelling in Squeeze Casting Process-Present State and Future Perspectives

ABC and GA Optimized NN to Model Resin Bonded Mould/Core Sand System: A Soft Computing-based Approach

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