Application of Statistical Modelling and Evolutionary Optimization Tools in Resin-Bonded Molding Sand System

Application of Statistical Modelling and Evolutionary Optimization Tools in Resin-Bonded Molding Sand System

Ganesh R. Chate (K. L. S. Gogte Institute of Technology, India), Manjunath Patel G. C. (Sahyadri College of Engineering and Management, India), Mahesh B. Parappagoudar (Padre Conceicao College of Engineering, India) and Anand S. Deshpande (K. L. S. Gogte Institute of Technology, India)
DOI: 10.4018/978-1-5225-5396-0.ch007


Trial-and-error methods in foundries to determine optimum molding sand properties consume more time and result in reduced productivity, high rejection, and cost. Hence, current research is focused towards development and application of modelling and optimization tools. In foundry, there is requirement of mound properties with conflicting nature (that is, minimize: gas evolution and collapsibility; maximize: compression strength, mound hardness, and permeability) and determining best combination among them is often a difficult task. Optimization of resin-bonded molding sand system is discussed in this book chapter. Six different case studies are considered by assigning different combination of weight fractions for multiple objective functions and corresponding desirability (Do) values are determined for DFA, GA, PSO, and MOPSO-CD. The obtained highest desirability value is considered as the optimum solution. Better performance of non-traditional tools might be due to parallel computing approach. GA and PSO have yielded almost similar results, whereas MOPSO-CD produced better results.
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Introduction And Literature Review

The foundry aims to produce good quality castings at reduced cost, by minimizing the incidence of many possible casting defects. To achieve the said objectives, one should have strong knowledge of process mechanics and dynamics through which the castings are made. Metal casting process involves pattern making, mould preparation, melting and pouring. Sand moulds offer greater technical advantage for the production of large tonnage castings at a low cost. Sand casting is a versatile manufacturing process, as it is used to cast high temperature metals and alloys namely, iron, copper and nickel by Saikaew and Wiengwiset (2012). The process control in casting process is often influenced by a large number of control variables (that is, quantity of binder, sand grain size and shape, degree of ramming, curing time and so on) in sand moulds. The sand mixed with a binder accommodates to hold particles of varied size and shapes,which are being compacted around a pattern to form the cavity in the sand. The casting quality or defects are related directly to sand mould properties namely, compression strength, mould hardness, permeability, gas evolution, and collapsibility. The sand mould properties are to be controlled through a proper choice of moulding sand ingredients and processing method of moulding sand mixture.

Key Terms in this Chapter

CCD: Central composite design.

B: Percent of resin.

V: Volume of air in cm 3 .

U: Upper limit.

AFS: American foundry society.

DO: Overall desirability value.

p: Air pressure in gm/cm 2 .

MOPOSO-CD: Multiple-particle-swarm-optimization-based crowding distance.

W: Weight fraction.

DFA: Desirability function approach.

GFN: Grain fineness number.

BHN: Brinell hardness number.

YS: Yield strength.

D: Setting time.

L: Lower limit.

Yi: Single desirability output value of an objective function.

p: Permeability.

DOE: Design of experiments.

T: Time in minutes.

A: Cross-sectional area of sand specimen in sq. cm.

A: Grain fineness number.

PSO: Particle swarm optimization.

GE: Gas evolution.

CP: Collapsibility.

RSM: Response surface methodology.

UTS: Ultimate tensile strength.

ANNOVA: Analysis of variance.

C: Percent of hardener.

GA: Genetic algorithm.

di: Individual desirability value.

T: Target value.

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