Forward and Reverse Process Models for the Squeeze Casting Process Using Neural Network Based Approaches

The present research work is focussed to develop an intelligent system to establish the input-output relationship utilizing forward and reverse mappings of artificial neural networks. Forward mapping aims at predicting the density and secondary dendrite arm spacing (SDAS) from the known set of squeeze cast process parameters such as time delay, pressure duration, squeezes pressure, pouring temperature, and die temperature. An attempt is also made to meet the industrial requirements of developing the reverse model to predict the recommended squeeze cast parameters for the desired density and SDAS. Two different neural network based approaches have been proposed to carry out the said task, namely, back propagation neural network (BPNN) and genetic algorithm neural network (GA-NN). The batch mode of training is employed for both supervised learning networks and requires huge training data. The requirement of huge training data is generated artificially at random using regression equation derived through real experiments carried out earlier by the same authors. The performances of BPNN and GA-NN models are compared among themselves with those of regression for ten test cases. The results show that both models are capable of making better predictions and the models can be effectively used in shop floor in selection of most influential parameters for the desired outputs

Performance analysis of cow dung as an eco-friendly binder and additive material for sustainable moulding and casting

Abstract: In the present work, an attempt is made to partially replace the high cost silica sand with sustainable eco-friendly material, namely cow-dung. Practical utility of cow dung as a binding and additive material in foundries has been tested in the present work. Taguchi method is used to plan and conduct nine experiments with three replicates each. Pareto analysis of variance study is done to understand the practical significance of moulding sand variables namely percent of cow dung, percent of clay, percent of water, and degree of ramming on sand mould properties. The conflicting multiple objective functions (maximize: mould hardness, and minimize: collapsibility and gas evolution) are optimized by utilizing data envelopment analysis ranking (DEAR) method. The optimal parameter levels i.e. 6% of clay, 4% of water, 5% of cow-dung and 4 numbers of ramming strokes are obtained by applying hybrid Taguchi- DEAR method. These parameters yielded the best moulding properties i.e. mould hardness 55, gas evolution 5.9 ml/gm , and collapsibility 470 g/cm2. Thereafter, Lovejoy coupling made of aluminium is cast in the sand mould prepared with cow-dung and without cow-dung. The sand mould is prepared with the optimum set of parameters and the casting produced in the mould has been tested for its quality characteristics. The mechanical properties, surface finish, and microstructure of the casting made in sand mould with cow-dung are found to be better than that obtained with sand mould without cow-dung. The present research work is found to be more useful in foundries for sustainable production of good quality casting

Application of Response Surface Methodology for

In the present paper an attempt has been made to establish the non-linear input-output relationships to model mechanical properties of structural steel with the help of Response Surface Methodology. Central composite design is utilized to conduct the experiments. Further, surface plots have been developed for response namely Yield strength, Ultimate tensile strength and Elongation. The experiments have been conducted as per central composite design where all process variables are set at three levels. The surface plots showed that alloying elements Manganese, Silicon and Carbon have positive contribution towards both responses Ultimate tensile strength and Yield strength. Moreover, analysis of variance test has been conducted to determine the statistical adequacies of the developed models. The alloying elements Carbon and Manganese showed more contribution as compared to Silicon. It is to be noted that all the three alloying elements are found to have negative contribution towards the response- Elongation. The developed nonlinear regression models for the responses Yield strength, ultimate tensile strength and elongation have been tested for their prediction accuracy with the help of test cases. The present work is found to be useful to control the mechanical properties of structural steel by varying the major alloying elements. Moreover, most of the surface plots have shown a linear relation with the responses

Optimization of Squeeze Cast Process Parameters Using Taguchi and Grey Relational Analysis

AbstractThe near-net shape manufacturing capabilities of squeeze casting process have greater potential to achieve smooth uniform surface and internal soundness in the cast components. In squeeze casting process, casting density and surface finish is influenced majorly by process variables. Proper control of the process variables is essential to achieve better results. Hence in the present work an attempt made using taguchi method to analyze the squeeze cast process variables such as squeeze pressure, die and pouring temperature considering at three different levels using L9 orthogonal array. Pareto analysis of variance performed on each response to find out optimum process parameter levels and significant contribution of each individual process parameter towards surface roughness and density of LM20 alloy. Grey relation analysis used as a multi-response optimization technique to obtain the single optimal process parameter setting for both the responses surface roughness and casting density