Multi-Objective Optimization of Green Sand Mould System Parameters using Particle Swarm Optimization

This paper deals with multi-objective optimization of green sand mould system using Particle Swarm Optimization (PSO). It is important to note that the quality of cast products in green sand moulds is largely influenced by the mould properties (that is, responses), such as green compression strength, permeability, hardness and bulk density, which depend on the input (process) parameters (that is, grain fineness number, % clay, % water and number of strokes). In this study, non-linear regression equations developed between the control factors (process parameters) and responses have been considered for optimization utilizing PSO. An attempt is being made to form a single objective, after considering all the four individual objectives, to obtain a compromise solution, which satisfies all the four objectives. The results of this study show a good agreement with the experimental results The quality of the parts produced during moulding process depends on the properties (that is, green compression strength, permeability, hardness and bulk density) of moulding sand. It is important to note that improper levels of these properties leads to common casting defects, such as blow holes, pinhole porosity, poor surface finish, dimensional variation, scabs and rat tails, misruns etc. The properties of the mould are influenced by a large number of controllable parameters (that is, grain fineness number, % clay, % water and number of strokes). Hence, it is important to identify the levels of the input variables that provide required mould properties, which improves the quality of the parts produced by this mould. During 1960s and 70s, most of the research work on moulding sand was based on experimental and theoretical approaches. In [1], the relationship between permeability and transformation zones, mould pressure, void space control, etc., was developed through substantial mathematical equations. In addition to this, Frost and Hiller [2] established the pressure and hardness distributions in sand moulds. Later on, Wenninger [3] utilized the rigid water theory to explain sand-clay-water relationships. This approach was completely theoretical and not supported by a large number of experiments. Later on, statistical design of experiments (DOE) had been used by various investigators to study the effects of different variables on the green sand mould properties. In [4] Design of Experiments (DOE) technique was applied to study the effect of process variables on bulk density and green compression strength. Moreover, Casalino et al. [5] utilized Taguchi technique to establish third order model for permeability and compression strength in laser sintered sand moulds. Moreover, Parappagoudar et al. [6,7] developed linear and non-linear statistical models utilizing full factorial DOE, Central Composite Design (CCD) and Box-Bhenken design. In the above work, the authors had considered grain fineness number, % of clay, % of water and number of strokes as input parameters and green compression strength, permeability, hardness and bulk density as the responses. Among the non-linear regression equations developed by the above mentioned three approaches, CCD-based model was found to be the more accurate model for prediction of the responses. Optimization techniques are required to identify the optimal combination of parameters for achieving the desired performance of the green sand mould system. In single objective optimization, one attempts to obtain the best design or decision, which is usually the global maximum or minimum depending on the optimization problem. In green sand mould system, it is difficult to find a single optimal combination of parameters for green compression strength,