Dilip Kumar Bagal

Bio: Dilip Kumar Bagal is an academic researcher from Government College of Engineering, Kalahandi. The author has contributed to research in topics: Taguchi methods & Materials science. The author has an hindex of 7, co-authored 36 publications receiving 225 citations. Previous affiliations of Dilip Kumar Bagal include National Institute of Technology, Rourkela & Government College.

Effect of process parameters on cut quality of stainless steel of plasma arc cutting using hybrid approach

Abstract: An optimization concept of the various machining parameters for the plasma arc cutting procedures on AISI 316 stainless steel conducting a hybrid optimization method has been carried out. A new composition of response surface methodology and grey relational analysis coupled with principal component analysis has been proposed to evaluate and estimate the effect of machining parameters on the responses. The major responses selected for these analyses are kerf, chamfer, dross, surface roughness and material removal rate, and the corresponding machining parameters concentrated for this study are feed rate, current, voltage and torch height. Thirty experiments were conducted on AISI 316 stainless steel workpiece materials based on a face-centered central composite design. The experimental results obtained are applied in grey relational analysis, and the weights of the responses were evaluated by the principal component analysis and further evaluated using response surface method. The results show that the grey relational grade was significantly affected by the machining parameters directly as well as with some interactions. This method is straightforward with easy operability, and the results have also been established by running confirmation tests. The premise attributes beneficial knowledge for managing the machining parameters to enhance the preciseness of machined parts by plasma arc cutting.

Estimating the parameters of susceptible-infected-recovered model of COVID-19 cases in India during lockdown periods

Abstract: Owing to the pandemic scenario of COVID-19 disease cases all over the world, the outbreak prediction has become extremely complex for the emerging scientific research. Several epidemiological mathematical models of spread are increasing daily to forecast the predictions appropriately. In this study, the classical susceptible-infected-recovered (SIR) modeling approach was employed to study the different parameters of this model for India. This approach was analyzed by considering different governmental lockdown measures in India. Some assumptions were considered to fit the model in the Python simulation for each lockdown scenario. The predicted parameters of the SIR model exhibited some improvement in each case of lockdown in India. In addition, the outcome results indicated that extreme interventions should be performed to tackle this type of pandemic situation in the near future.

Brain Tumour Classification Using Noble Deep Learning Approach with Parametric Optimization through Metaheuristics Approaches

Abstract: Deep learning has surged in popularity in recent years, notably in the domains of medical image processing, medical image analysis, and bioinformatics. In this study, we offer a completely autonomous brain tumour segmentation approach based on deep neural networks (DNNs). We describe a unique CNN architecture which varies from those usually used in computer vision. The classification of tumour cells is very difficult due to their heterogeneous nature. From a visual learning and brain tumour recognition point of view, a convolutional neural network (CNN) is the most extensively used machine learning algorithm. This paper presents a CNN model along with parametric optimization approaches for analysing brain tumour magnetic resonance images. The accuracy percentage in the simulation of the above-mentioned model is exactly 100% throughout the nine runs, i.e., Taguchi’s L9 design of experiment. This comparative analysis of all three algorithms will pique the interest of readers who are interested in applying these techniques to a variety of technical and medical challenges. In this work, the authors have tuned the parameters of the convolutional neural network approach, which is applied to the dataset of Brain MRIs to detect any portion of a tumour, through new advanced optimization techniques, i.e., SFOA, FBIA and MGA.

Comparative Evaluation for Studying the Parametric Influences on Quality of Electrode Using Taguchi Method Coupled with MOORA, DFA, and TOPSIS Method for Electrochemical Machining

Abstract: The selection of optimal parameters of electrochemical machining (ECM) processes plays a noteworthy part in optimizing the measures of process parameters. Hence, evaluation of material removal rate and surface roughness is of enormously important in ECM. Developed in this paper is the application of Taguchi-based MOORA technique, desirability function analysis, and TOPSIS method for optimizing the responses of chromoly steel with the help of hexagonal-shaped brass electrode and brine solution. Multi-objective optimization has been done to investigate the influence of process parameters, i.e., voltage, tool feed rate, and signal on MRR and surface roughness. Optimal factor setting obtained from each optimization technique was compared, and confirmation test was done to validate the results obtained from electrochemical machining.

Reproductive number of coronavirus: A systematic review and meta-analysis based on global level evidence.

Abstract: BACKGROUND: The coronavirus (SARS-COV-2) is now a global concern because of its higher transmission capacity and associated adverse consequences including death. The reproductive number of coronavirus provides an estimate of the possible extent of the transmission. This study aims to provide a summary reproductive number of coronavirus based on available global level evidence. METHODS: A total of three databases were searched on September 15, 2020: PubMed, Web of Science, and Science Direct. The searches were conducted using a pre-specified search strategy to record studies reported the reproductive number of coronavirus from its inception in December 2019. It includes keywords of coronavirus and its reproductive number, which were combined using the Boolean operators (AND, OR). Based on the included studies, we estimated a summary reproductive number by using the meta-analysis. We used narrative synthesis to explain the results of the studies where the reproductive number was reported, however, were not possible to include in the meta-analysis because of the lack of data (mostly due to confidence interval was not reported). RESULTS: Total of 42 studies included in this review whereas 29 of them were included in the meta-analysis. The estimated summary reproductive number was 2.87 (95% CI, 2.39-3.44). We found evidence of very high heterogeneity (99.5%) of the reproductive number reported in the included studies. Our sub-group analysis was found the significant variations of reproductive number across the country for which it was estimated, method and model that were used to estimate the reproductive number, number of case that was considered to estimate the reproductive number, and the type of reproductive number that was estimated. The highest reproductive number was reported for the Diamond Princess Cruise Ship in Japan (14.8). In the country-level, the higher reproductive number was reported for France (R, 6.32, 95% CI, 5.72-6.99) following Germany (R, 6.07, 95% CI, 5.51-6.69) and Spain (R, 3.56, 95% CI, 1.62-7.82). The higher reproductive number was reported if it was estimated by using the Markov Chain Monte Carlo method (MCMC) method and the Epidemic curve model. We also reported significant heterogeneity of the type of reproductive number- a high-value reported if it was the time-dependent reproductive number. CONCLUSION: The estimated summary reproductive number indicates an exponential increase of coronavirus infection in the coming days. Comprehensive policies and programs are important to reduce new infections as well as the associated adverse consequences including death.

A new multi-objective Jaya algorithm for optimization of modern machining processes

Abstract: In this work, the multi-objective optimization aspects of plasma arc machining (PAM), electro-discharge machining (EDM), and micro electro-discharge machining (μ-EDM) processes are considered. Experiments are performed and actual experimental data is used to develop regression models for the considered machining processes. A posteriori version of Jaya algorithm (MOJaya algorithm) is proposed to solve the multi-objective optimization models in a single simulation run. The PAM, EDM and μ-EDM processes are optimized using MO-Jaya algorithm and a set of Pareto-efficient solutions is obtained for each of the considered machining processes and the same is reported in this work. This Pareto optimal set of solutions will provide flexibility to the process planner to choose the best setting of parameters depending on the application. The aim of this work is to demonstrate the performance of MO-Jaya algorithm and to show its effectiveness in solving the multi-objective optimization problems of machining processes. © 2016 PEI, University of Maribor. All rights reserved.