Kalipada Maity

ANN modelling and Elitist teaching learning approach for multi-objective optimization of \upmu -EDM

Fabrication of micro-holes has been carried out in Inconel 718 using micro electrical discharge machining operation. Artificial neural network modelling has been carried out to predict Material Removal Rate, Overcut effect and Recast Layer thickness. The training, testing and validation data sets were collected by conducting experiments. It is observed that ANN is a powerful prediction tool. It provides agreeable results when experimental and predicted data are compared. Further optimization of the process variables has been carried out using different meta heuristic approaches like Elitist Teaching learning based optimization, Multi-Objective Differential Evolution and Multi-Objective Optimization using an Artificial Bee Colony algorithm. The comparisons are carried out to improve the accuracy of the model on the basis of Pareto front solutions.

Application of MCDM-Based TOPSIS Method for the Optimization of Multi Quality Characteristics of Modern Manufacturing Processes

Optimization of non-conventional machining (NCM) processes viz. AJM, AWJM, EDM, WEDM, ECM, ECMM, LBM, PAC, etc. has always been an open research area for researchers. In recent manufacturing environment, almost all the NCM processes consist of a number of input and output to be considered together. The purpose of the present article is to highlight the application of a multi-criteria decision making (MCDM) based method called Technique of Order Preference by Similarity of Ideal Solution (TOPSIS) in optimization of some modern manufacturing processes (MMPs). In the present paper, seven different MMPs namely Electro Chemical Honing (ECH), Abrasive Water Jet Machining (AWJM), Abrasive Jet Machining (AJM), Laser Beam Machining (LBM), Plasma Arc Cutting (PAC), Laser Cutting (LC) and Electric Discharge Machining (EDM) were exemplified. The multiple outcomes of all the illustrated MMPs have been optimized simultaneously by using TOPSIS method. It is a simple, systematic, and logical technique which can be employed to obtain the best parametric combination of cutting parameters. It was observed that the results attained by using TOPSIS method were almost tie with those derived by past researchers. This proves the applicability and adaptability of the TOPSIS method while solving different MCDM-based problems in a real manufacturing system.

Optimization of Machining Condition in WEDM for Titanium Grade 6 Using MOORA Coupled with PCA — A Multivariate Hybrid Approach

This paper represents a multivariate hybrid approach, combining Multi-Objective Optimization on the basis of Ratio Analysis (MOORA) and Principal Component Analysis (PCA) to optimize different corr...

Application of MCDM-based TOPSIS method for the selection of optimal process parameter in turning of pure titanium

Purpose The purpose of this paper is to explore a multi-criteria decision-making (MCDM) methodology to determine an optimal combination of process parameters that is capable of generating favorable dimensional accuracy and product quality during turning of commercially pure titanium (CP-Ti) grade 2. Design/methodology/approach The present paper recommends an optimal combination of cutting parameters with an aim to minimize the cutting force (Fc), surface roughness (Ra), machining temperature (Tm) and to maximize the material removal rate (MRR) after turning of CP-Ti grade 2. This was achieved by the simultaneous optimization of the aforesaid output characteristics (i.e. Fc, Ra, Tm, and MRR) using the MCDM-based TOPSIS method. Taguchi’s L9 orthogonal array was used for conducting the experiments. The output responses (cutting force: Fc, surface roughness: Ra, machining temperature: Tm and MRR) were integrated together and presented in terms of a single signal-to-noise ratio using the Taguchi method. Findings The results of the proposed methodology depict that the higher MRR with desirable surface quality and the lower cutting force and machining temperature were observed at a combination of cutting variables as follows: cutting speed of 105 m/min, feed rate of 0.12 mm/rev and depth of cut of 0.5 mm. The analysis of variance test was conducted to evaluate the significance level of process parameters. It is evident from the aforesaid test that the depth of cut was the most significant process parameter followed by cutting speed. Originality/value The selection of an optimal parametric combination during the machining operation is becoming more challenging as the decision maker has to consider a set of distinct quality characteristics simultaneously. This situation necessitates an efficient decision-making technique to be used during the machining operation. From the past literature, it is noticed that only a few works were reported on the multi-objective optimization of turning parameters using the TOPSIS method so far. Thus, the proposed methodology can help the decision maker and researchers to optimize the multi-objective turning problems effectively in combination with a desirable accuracy.

A Novel MCDM Approach for Simultaneous Optimization of some Correlated Machining Parameters in Turning of CP-Titanium Grade 2

The present work explores the application of a novel Multi-Criteria Decision Making (MCDM) based approach known as VIKOR analysis combined with Taguchi technique for simultaneous optimization of some correlated cutting variables in turning of commercially pure titanium grade 2 using uncoated carbide inserts. The experiments have been carried out according to Taguchi’s L27 orthogonal array. Three input variables viz. cutting speed, feed rate and depth of cut have been taken at three different levels. The impact of these cutting variables on cutting force, surface quality and material removal rate has been investigated. The optimal combination of machining parameters has been evaluated to minimize the cutting force and to maximize the surface finish and production rate using MCDM based VIKOR analysis method. ANOVA (analysis of variance) test has been performed to determine the most influencing cutting variable on overall quality measure i.e. VIKOR index (Qi). The optimal setting of machining variables has been shown using main effects plot for S/N ratio for Qi. The results of ANOVA exhibit that the cutting speed is the governing machining parameter followed by feed rate on overall quality index (Qi). The minimum (desirable) value of Qi is achieved at the parametric combination of v3-f1-d3 i.e. cutting speed (110 m/min), feed rate (0.08 mm/rev) and depth of cut (0.4 mm) respectively. The feasibility of the proposed methodology has been verified by conducting a confirmation test.

Predictive Analysis on Responses in WEDM of Titanium Grade 6 Using General Regression Neural Network (GRNN) and Multiple Regression Analysis (MRA)

In the present investigation two smart prediction tools, namely the general regression neural network (GRNN) and multiple regression analysis (MRA) models were developed to predict and compare some of the key machinability aspects like average kerf width, average surface roughness and material removal rate in the wire electrical discharge machining process of titanium grade 6. Pulse-on time, pulse-off time, wire feed and wire tension were considered as machining variables to develop the predictive model. In order to curtail cross-validation error in GRNN, optimized kernel bandwidth was utilized using the grid search method. The neural network and regression models were trained, validated and tested with measured data. A mathematical model was developed using multiple regression analysis. The ANOVA test was also conducted to determine the significant parameters affecting the responses. The results indicated that the predicted responses lie within ± 5% and ± 10% error for GRNN and MRA, respectively, which suggests that the GRNN model is more reliable and adequate than the regression model. A comparative study with previous research work was also done to confirm the novelty along with application potential of the proposed model.

EFFECTS OF PROCESS PARAMETERS AND CRYOTREATED ELECTRODE ON THE RADIAL OVERCUT OF AISI 304 IN SiC POWDER MIXED EDM

Powder mixed electro discharge machining (PMEDM) is further advancement of conventional electro discharge machining (EDM) where the powder particles are suspended in the dielectric medium to enhance the machining rate as well as surface finish. Cryogenic treatment is introduced in this process for improving the tool life and cutting tool properties. In the present investigation, the characterization of the cryotreated tempered electrode was performed. An attempt has been made to study the effect of cryotreated double tempered electrode on the radial overcut (ROC) when SiC powder is mixed in the kerosene dielectric during electro discharge machining of AISI 304. The process performance has been evaluated by means of ROC when peak current, pulse on time, gap voltage, duty cycle and powder concentration are considered as process parameters and machining is performed by using tungsten carbide electrodes (untreated and double tempered electrodes). A regression analysis was performed to correlate the data between the response and the process parameters. Microstructural analysis was carried out on the machined surfaces. Least radial overcut was observed for conventional EDM as compared to powder mixed EDM. Cryotreated double tempered electrode significantly reduced the radial overcut than untreated electrode.

Determination of Material Removal Rate and Radial Overcut in Electro Discharge Machining of AISI 304 Using Dimensional Analysis

Electro discharge machining (EDM) is a most commonly used machining process among all the non-conventional machining process which removes materials via electrical and thermal energy. The primary goal of EDM is to get more material removal rate (MRR) with lower radial overcut (ROC). Normally, the responses are predicted using empirical models which are limited to only machining parameters and they do not consider the effects of work material properties on the process performance. Therefore in this study, a model has been developed including machining parameter as well as thermo-physical property of work material. In this investigation, a semi-empirical model has been established for the material removal rate (MRR) and radial overcut (ROC) by adopting the dimensional analysis technique. Dimensional analysis is a technique of dimensions and a mathematical technique that deals with the physical quantities concerned with the experiments to formulate a model for the response in terms of response control parameters as well as some physical properties of the materials. Buckingham’s л theorem is a main theorem in dimensional analysis and it is a signification of Rayleigh’s method of dimensional analysis. The theory is applied to gather each and every variable presenting the problem in a number of the dimensionless products. For this study, the thermo-physical properties viz. density, thermal conductivity and coefficient of thermal expansion and machining parameters like peak current, pulse on time, gap voltage and duty cycle are considered as input factor. AISI 304 stainless steel used as work material and Tungsten carbide is used as tool material for this investigation.

Parametric Optimization of Some Non-Conventional Machining Processes Using MOORA Method

Non-conventional manufacturing techniques are most widely used in industries in order to achieve high accuracy and desirable product quality. Therefore, the selection of an appropriate machining parameter has become a crucial job before starting the operation. Several optimization methods are available to resolve the upstairs situation. The current study explores a novel technique namely multi-objective optimization on the basis of ratio analysis (MOORA) to solve different multi-objective problems that are encountered in the real-time manufacturing industries. This study focuses on the application of MOORA method for solving some non-conventional machining processes that have multiple criteria problems. Wire-Electric Discharge Machining (WEDM), Plasma Arc Cutting (PAC), Electro Chemical Micro Machining (ECMM), Electro Chemical Machining (ECM), Abrasive Jet Machining (AJM), Abrasive Water Jet Machining (AWJM), Ultrasonic Machining (USM), Laser Beam Machining (LBM) and Laser cutting process are the major attentions in the current study. Total nine NTM multi-criteria problems which include selection of proper machining parameters have been studied. The optimal settings of input variables obtained by using MOORA method nearly tie with those derived by the earlier investigators.

Quality Optimization of Micro-Hole In Laser Drilling

Laser drilling is a popular non-traditional machining technique for producing large numbers of cooling holes of various sizes (<1mm) and angles in modern aerospace gas turbine components such as turbine blades, nozzle guide vanes, combustion chambers and after burners. Though the rate of production of micro-hole (i.e. productivity) is very high but the quality of hole (such as straightness, circularity, HAZ) is very poor due to unique nature of the process. In the present study, SEM analysis is carried out on the micro-holes produced on a medium carbon steel specimen based on Taguchi’s orthogonal array and finally recommendation is made for optimum selection of process parameter such as: pulse frequency, pulse width and assist gas flow rate for getting a good quality hole.