January-March 2016 456 JCPS Volume 9 Issue 1 Advanced Machining Performance Study on Hardened Steel EN 31: A Differential Evaluation Approach Xavier Irudhya Raj Y, Rajesh R, Dev Anand M, Rohit IJ Department of Mechanical Engineering, P.S.R. Rengasamy College of Engineering for Women. Department of Mechanical Engineering, Noorul Islam Centre for Higher Education, Kumaracoil 629180, Tamilnadu, India Department of Aeronautical Engineering, Noorul Islam University, Kanyakumari District, Tamilnadu, India *Corresponding author: E-Mail: xavier.sharon1999@gmail.com ABSTRACT Electrochemical machining (ECM) has inaugurated itself as one of the major other possible way to conventional methods for machining hard materials and complicated outlines not having the properties like residual stresses and tool wear. Studies on Material Removal Rate (MRR) and Surface Roughness (Ra) are of extremely important in ECM, since it is one of the factors to be determined in the process decisions. Machining parameters provided by the machine tool builder cannot meet the operator’s requirements. Since for an arbitrary desired machining time for a particular job, they do not provide the optimal conditions. So the aim of present work is to investigate the MRR and Ra of EN19 alloy steel in the form of cylindrical (16mm Diameter and 32mm Height) dimensions copper electrode and solution of Sodium Chloride (NaCl) in water as electrolyte by using computational approach. To solve this task, multiple regression model and Differential Evaluation Model (DOE) model are developed as efficient approaches to determine the optimal machining parameters in ECM. Current (c), Voltage (V), Electrolyte Concentration (E) and Feed Rate (F) are considered as machining parameters and MRR and Ra are the output responses. This multi response has been modified as a single objective by a Grey Relational Analysis (GRA). Grey grade has been calculated for representing the multi-objective model. The predicted grade has been found and then the percentage deviation between the experimental grade and predicted grade has been calculated for multiple regression linear model & DOE model. Then optimizing to find best setting of process variables for higher MRR and lower Ra. Based on the testing results of the DOE the operating parameters are optimized. The designs are based on Response Surface Methodology (RSM), with the aid of MINITAB software. The optimum value has been determined which is suitable for both MRR and Ra.

This paper presents an analysis of a shell and tube heat exchanger, carried out using a finite element method for the optimal design. The main objective in any heat exchanger design is the estimation of the minimum heat transfer area required for a given heat duty, as it governs the overall cost of the heat exchanger. Step by step on designing the software is build first to make the work on calculation easy and simple by using finite element method. Numbers of configurations are possible with various design variables such as outer diameter, pitch, and length of the tubes; tube passes; baffle spacing; baffle cut etc. Hence the engineer needs an efficient strategy in searching for the global minimum. Here the shell and tube line utilizes bare tube (straight and u-tubes), helical low-fin outside, longitudinal fins (inside & outside are available). These exchangers are suitable for a variety of applications and are offered in fixed tube sheets or removable bundles. A finite element model to predict temperature distribution in heat exchanger is reported. The predictions are in good agreement with available analytical solutions. The model can be effectively used to analyze and design heat exchangers with complex flow arrangements for which no regular design procedure is available. Illustrations are provided to explain the application of the method for the analysis of shell and tube heat exchangers.

This paper attempts to the overview of design, conceptual analysis and implementation of coordinated multipurpose robot system with smart sensors. The aim of this paper is to show how to design and integrate various sensors in a single module and create a data retrieval system which can be used as a black box and an investigation device.

The use of Electrochemical Machining (ECM) as one of the best machining techniques for machining and electrically conducting tough and difficult to machine materials with appropriate machining parameters. The present study concentrates on optimizing the machining parameters for ECM considering current, voltage, Electrolyte concentration and feed rate as machining parameters and Metal Removal Rate (MRR) and Surface Roughness (SR). Grey relational analysis is applied to find the Grey Grade and it is used to represent the multi-objective model. Multiple regression model and Differential Evolution model are developed to map the relationship between process parameters and objectives in terms of grade. The percentage deviation is calculated for each model. From the analysis it is noticed that the Differential Evolution (DE) model is the best model and the machining parameters are optimized using Differential Evolution Algorithm.