Arun Kumar Shettigar

Back propagation genetic and recurrent neural network applications in modelling and analysis of squeeze casting process

Abstract Today, in competitive manufacturing environment reducing casting defects with improved mechanical properties is of industrial relevance. This led the present work to deal with developing the input-output relationship in squeeze casting process utilizing the neural network based forward and reverse mapping. Forward mapping is aimed to predict the casting quality (such as density, hardness and secondary dendrite arm spacing) for the known combination of casting variables (that is, squeeze pressure, pressure duration, die and pouring temperature). Conversely, attempt is also made to determine the appropriate set of casting variables for the required casting quality (that is, reverse mapping). Forward and reverse mapping tasks are carried out utilizing back propagation, recurrent and genetic algorithm tuned neural networks. Parameter study has been conducted to adjust and optimize the neural network parameters utilizing the batch mode of training. Since, batch mode of training requires huge data, the training data is generated artificially using response equations. Furthermore, neural network prediction performances are compared among themselves (reverse mapping) and with those of statistical regression models (forward mapping) with the help of test cases. The results shown all developed neural network models in both forward and reverse mappings are capable of making effective predictions. The results obtained will help the foundry personnel to automate and precised control of squeeze casting process.

Influence of Welding Process Parameters on Microstructure and Mechanical Properties of Friction Stir Welded Aluminium Matrix Composite

In this study, the effect of process parameters on microstructure and mechanical properties of friction stir welded aluminium matrix composites (AMC) have been explored. The results indicated that the recrystallized grain size at the bottom of the weld region is smaller than that at the top region due to difference in the heat transfer at the weld region. The joint strength of AMCs depends on proper selection of process parameters like tool rotational speed and welding speed. If process parameter values are beyond the optimal value, the joint strength decreases due to formation of defects. Maximum tensile strength is obtained for rotational speed of 1000 rpm and welding speed of 80mm/min.

Application of Neural Network for the Prediction of Tensile Properties of Friction Stir Welded Composites

In this paper, an attempt has been made to apply the neural network (NN) techniques to predict the mechanical properties of friction stir welded composite materials. Nowadays, friction stri welding of composites are predominatally used in aerospace, automobile and shipbuilding applications. The welding process parameters like rotational speed, welding speed, tool pin profile and type of material play a foremost role in determining the weld strength of the base material. An error back propagation algorithm based model is developed to map the input and output relation of friction stir welded composite material. The proposed model is able to predict the joint strength with minimum error.

Forward and reverse mapping for milling process using artificial neural networks

The data set presented is related to the milling process of AA6061-4.5%Cu-5%SiCp composite. The data primarily concentrates on predicting values of some machining responses, such as cutting force, surface finish and power utilization utilizing using forward back propagation neural network based approach, i.e. ANN based on three process parameters, such as spindle speed, feed rate and depth of cut.The comparing reverse model is likewise created to prescribe the ideal settings of processing parameters for accomplishing the desired responses as indicated by the necessities of the end clients. These modelling approaches are very proficient to foresee the benefits of machining responses and also process parameter settings in light of the experimental technique.

Friction Stir Welding—An Overview

Friction stir welding is generally recognized as a solid state welding process and developed to overcome the difficulties of joining of aluminium alloys. Later, this process has been adapted to join copper, steel, dissimilar metals, magnesium, composites , etc. This concept can be further used in friction stir processing of metals, production of micro composites and coating of coppers on steel. This chapter elucidates the concept of friction stir welding process, material flow pattern, evolution of microstructure at weld region, and effect of process parameters on mechanical properties.

Investigation on microstructure and mechanical properties of Friction Stir Welded AA6061-4.5Cu-10SiC composite

The application of Metal Matrix Composites (MMCs) is restricted by the availability of properly developed fabrication methods. The main challenge here is the fabrication and welding of MMCs in a cost effective way. In the present study, synthesis of AA6061-4.5%Cu- 10%SiC composite was done by stir casting method. The joining of MMCs was performed by Friction Stir Welding (FSW) using a combination of square and threaded profile pin tool (CSTPP). Further, the welded composite was evaluated for microstructure and joint properties. The microstructural characterization showed uniform distribution of refined fine grains and numerous small particles at nugget zone. The hardness at the stir zone is higher than that of the base material. The tensile test revealed 96% joint efficiency in transverse direction.