Modeling and Optimization of Phenol Formaldehyde Resin Sand Mould System

Chemical bonded resin sand mould system has high dimensional accuracy, surface finish and sand mould properties compared to green sand mould system. The mould cavity prepared under chemical bonded sand mould system must produce sufficient permeability and hardness to withstand sand drop while pouring molten metal through ladle. The demand for improved values of permeability and mould hardness depends on systematic study and analysis of influencing variables namely grain fineness number, setting time, percent of resin and hardener. Try-error experiment methods and analysis were considered impractical in actual foundry practice due to the associated cost. Experimental matrices of central composite design allow conducting minimum experiments that provide complete insight of the process. Statistical significance of influencing variables and their interaction were determined to control the process. Analysis of variance (ANOVA) test was conducted to validate the model statistically. Mathematical equation was derived separately for mould hardness and permeability, which are expressed as a non-linear function of input variables based on the collected experimental input-output data. The developed model prediction accuracy for practical usefulness was tested with 10 random experimental conditions. The decision variables for higher mould hardness and permeability were determined using desirability function approach. The prediction results were found to be consistent with experimental values.

Meta-Heuristic Structure for Multiobjective Optimization Case Study

In engineering optimization, one often encounters scenarios that are multiobjective (MO) where each of the objectives covers different aspects of the problem. It is hence critical for the engineer to have multiple solution choices before selecting of the best solution. In this chapter, an approach that merges meta-heuristic algorithms with the weighted sum method is introduced. Analysis on the solution set produced by these algorithms is carried out using performance metrics. By these procedures, a novel chaos-based metaheuristic algorithm, the Chaotic Particle Swarm (Ch-PSO) is developed. This method is then used generate highly diverse and optimal solutions to the green sand mould system which is a real-world problem. Some comparative analyses are then carried out with the algorithms developed and employed in this work. Analysis on the performance as well as the quality of the solutions produced by the algorithms is presented in this chapter.

Reverse Modeling of Green Sand Mould System Using Fuzzy Logic-Based Approaches

In the present study, reverse mapping problems of green sand mould system have been solved using Fuzzy Logic (FL)-based approaches. It is a complicated process, in which the quality of the castings is influenced by the mould properties (that is, green compression strength, permeability, hardness and others). In forward modeling, the outputs are expressed as the functions of input variables, whereas in reverse modeling, the later are represented as the functions of the former. The main advantage of reverse modeling lies in the fact that it helps in effective real-time control of the process. This paper proposes three different FL-based approaches for the reverse modeling of the green sand mould system. A binary-coded Genetic Algorithm (GA) has been used to optimize the knowledge base of the FL-based approaches, off-line. The developed approaches are found to solve the above problem effectively, and the performances of the developed approaches are compared among themselves. It has been observed that the approach “Automatic design of FL system using GA” yielded much better results in predicting a set of input variables from the set of known set of output.