Raja Das

Bio: Raja Das is an academic researcher from VIT University. The author has contributed to research in topics: Electrical discharge machining & Artificial neural network. The author has an hindex of 10, co-authored 20 publications receiving 219 citations. Previous affiliations of Raja Das include Purushottam Institute of Engineering & Technology.

Comparisons of neural network models on surface roughness in electrical discharge machining

Abstract: In this work, two different artificial neural network (ANN) models — back-propagation neural network (BPN) and radial basis function neural network (RBFN) — are presented for the prediction...

Fuzzy logic controller based maximum power point tracking for PV system

Abstract: Solar Photovoltaic (PV) exploitation is a significant renewable energy source. The energy converted directly from sunlight through PV panel is not steady due to different solar intensity. Maximum power point tracking (MPPT) is used extract maximum power from the solar panel, high-performance soft computing techniques can be used as a maximum power point tracking techniques. This paper proposes fuzzy logic controller (FLC) based MPPT method for the PV system under constant and varying climatic conditions. FLC-based MPPT is able to differ the PV operating voltage and seek for the maximum power that the PV panel can produce. The performance of fuzzy logic with various membership function (MF) is analyzed to optimize the MPPT. Simulation results demonstrate that the recital of FLC-based MPPT is better than unadventurous perturb and observe (P&O) MPPT.

Recurrent neural network estimation of material removal rate in electrical discharge machining of AISI D2 tool steel

Abstract: An Elman network is used for the prediction of material removal rate (MRR) in electrical discharge machining (EDM). An Elman network is a dynamic recurrent neural network that can be used to model non-linear dynamic systems. Training of the models is performed with data from series of EDM experiments on AISI D2 tool steel from finishing, semi-finish to roughing operations. The machining parameters such as discharge current, pulse duration, duty cycle, and voltage were used as model input variables during the development of predictive models. The developed model is validated with a new set of experimental data that was not used for the training step. The mean percentage error of the model is found to be less than 6 per cent, which shows that the proposed model can satisfactorily predict the MRR in EDM.

ANN and RSM approach for modelling and multi objective optimization of abrasive water jet machining process

Abstract: Article history: Received March 16, 2017 Received in revised format: October 20, 2017 Accepted November 28, 2017 Available online November 28, 2017 Abrasive Water Jet Machining is one of the novel nontraditional cutting processes found diverse applications in machining different kinds of difficult-to-machine materials. Process parameters play an important role in finding the economics of machining process at good quality. This research focused on the predictive models for explaining the functional relationship between input and output parameters of AWJ machining process. No single set of parametric combination of machining variables can suggest the better responses concurrently, due to its conflicting nature. Hence, an approach of Multi-objective has been attempted for the best combination of process parameters by modelling AWJM process using of ANN. It served a set of optimal process parameters to AWJ machining process, which shows a development with an enhanced productivity. Wide set of trail experiments have been considered with a broader range of machining parameters for modelling and, then, for validating. The model is capable of predicting optimized responses. Growing Science Ltd. All rights reserved. 8 © 201

A probabilistic neural network approach for classification of vehicle

Abstract: The vehicle classification, which consists of determining the vehicle of different company, is very important for a customer because a vehicle may fit into multiple categories. Before buying a vehicle we consult reviews, ratings from numerous agencies. Some agencies perform rigorous testing on the vehicles and quantize the vehicle features like acceleration, braking, fuel economy etc, while other relies on the consumer reviews, awards won by particular vehicle model. Therefore mathematically we can say that: Vehicle success = ƒ (Vehicle features). In this work, we propose a new approach for vehicle classification based on a Probabilistic Neural Network and feature selection. Our goal is to classify a customer liking vehicle among the number of different vehicles available in the market on the basis of market survey. For this purpose, the datasets are extracted from a genuine source and are used as training datasets. The proposed work provides a soft computing-based tool capable of classifying vehicles, as closely as possible to classifications performed by skilled operators. Such a tool is capable of extracting a number of numerical parameters characterizing the vehicles in areas like Value for Money. For data analysis in this paper the vehicle is a car type of different company.

Nature Inspired Methods and Their Industry Applications—Swarm Intelligence Algorithms

Abstract: In this paper, we present the swarm intelligence (SI) concept and mention some metaheuristics belonging to the SI. We present the particle swarm optimization (PSO) algorithm and the ant colony optimization (ACO) method as the representatives of the SI approach. In recent years, researchers are eager to develop and apply a variety of these two methods, despite the development of many other newer methods as Bat or FireFly algorithms. Presenting the PSO and ACO we put their pseudocode, their properties, and intuition lying behind them. Next, we focus on their real-life applications, indicating many papers presented varieties of basic algorithms and the areas of their applications.

Modelling and experimental investigation on the effect of nanopowder-mixed dielectric in micro-electrodischarge machining of tungsten carbide

Abstract: Micro-electrodischarge machining (micro-EDM) is one of the most effective methods used in die and mould industries for machining difficult-to-cut tool and die materials such as tungsten carbide (WC). The quality and integrity of the surface finish resulting from the micro-EDM process in die and mould making can have a significant impact on the product performance. The present study intends to investigate the feasibility of improving the surface finish in micro-EDM of WC in a dielectric mixed with graphite (Gr), aluminium (Al), and alumina (Al2O3) nanopowders. The mechanism of powder-mixed micro-EDM is presented theoretically in terms of the effect of additive powder characteristics on the dielectric breakdown and gap width. In addition, the effect of the nanopowders’ mixed dielectric on surface topography, average surface roughness (Ra), peak-to-valley roughness (Rmax), material removal rate (MRR), and electrode wear ratio (EWR) was studied experimentally. It has been shown theoretically that the ...