Mahesh B. Parappagoudar

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

Abstract 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.

Modeling and optimization of furan molding sand system using design of experiments and particle swarm optimization

The present research work is focussed on establishing the complex nonlinear input–output relations of a furan resin-based molding sand system. Further, a set of input parameters, which will result in optimized mold properties, is determined. Grain fineness number, setting time, percentage of resin, and hardener are considered as process variables. Mold properties, such as green compression strength, shear strength, mold hardness, gas evolution, permeability, and collapsibility are treated as the process outputs. Nonlinear input–output relations have been developed and statistical analysis has been carried out by utilizing design of experiments, central composite design. Surface plots are developed to study and analyze the input–output relations. The input parameters that will result in best molding conditions and improve casting quality characteristics are determined by utilizing desirability function approach and multiple particle swarm optimization-based crowding distance (MOPSO-CD) techniques. The optimum value for the process variables namely grain fineness number, furan resin, hardener, and setting time are found to be equal to 55, 1.85, 1.2, and 60, respectively. The quality characteristics of the castings namely yield strength, ultimate tensile strength, hardness, density, and secondary dendrite arm spacing are found to improve by 14.03%, 15.08%, 14.14%, 12%, 2.22%, and 12.24%, respectively for the castings made in optimized molding sand conditions.

Modelling in Squeeze Casting Process-Present State and Future Perspectives

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.

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

Abstract 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.

MODELING OF HIGH-SPEED FINISH MILLING PROCESS USING SOFT COMPUTING

In the present study, forward modeling of high-speed finish milling process has been solved using soft computing. Two different approaches, namely neural network (NN) and fuzzy logic (FL), have been developed to solve the said problem. The performance of NN and FL systems depends on the structure (i.e. number of neurons in the hidden layer, transfer functions, connection weights, etc.) and knowledge base (i.e. rule base and data base), respectively. Here, an approach is proposed to optimize the above-mentioned parameters of NN and FL systems. A binary coded genetic algorithm (GA) has been used for the said purpose. Once optimized, the NN and FL-based models will be able to provide optimal machining parameters online. The developed approaches are found to solve the above problem effectively, and the performances of the developed approaches have been compared among themselves and with that of the results of existing literature.

Study of the Effect of Nano-silica Particles on Resin-Bonded Moulding Sand Properties and Quality of Casting

The cast quality in chemical bonded sand mould system is influenced primarily by sand mould properties such as, compression strength, permeability, gas evolution, and collapsibility. Amount of resin and hardener, curing time and number of strokes influence the sand mould properties. The experiments are conducted with the above mentioned input output, as per Taguchi’s L9 orthogonal array. Pareto analysis of variance is conducted to determine the percent contribution of inputs on output, individually. The optimal factor level is determined for each output separately. The conflicting requirements in foundry sand mould properties can be solved by multiple objective optimization. Principal component analysis is applied to determine the relative importance of individual output. Grey relational analysis is used to convert multiple objective functions to a single objective function for optimization task. Pareto analysis is utilized to determine the optimal input factor combination and their relative percent contribution towards moulding sand properties. The nano-silica particles are used as additive to enhance the moulding sand properties. The results have shown that, the nano-silica particles pose a remarkable improvement in sand mould properties and casting quality.