Work place: Department of Electrical & Electronics Engineering, Mirmadan Mohanlal Government Polytechnic, Gobindapur, Plassey, West Bengal 741156

Website:

Research Interests: Artificial Intelligence, Mathematics of Computing, Models of Computation

Biography

Mrs. Madhurima Majumder is currently acting as a lecturer in Department of electrical & Electronics Engineering Department, Mirmadan Mohanlal Government Polytechnic Institute, India. She received her B.Tech from MAKAUT, India in 2019. Presently she persuing M Tech from MAKAUT. Till now she published 3 research papers in a reputated journal. Her research interests are Internet of Things & Artificial Intelligence.

##### Parametric Optimization of Drilling Parameters in Aluminum 6061T6 Plate to Minimize the Burr

DOI: https://doi.org/10.5815/ijem.2021.06.04, Pub. Date: 8 Dec. 2021

In the manufacturing, process a burr has been observed during the drilling through a hole in an aluminum bar. From the view of the life of a product, minimization of the burr should be significant. So in this research main aim is to identify how input parameters: drill diameter, point angle & spindle speed influenced output parameters burr height & thickness. To execute this operation a total of 27 examinations on an Aluminum 6061T6 plate is taken. Overall research performed into two stages. In first stage, Surface response methodology is used to design two objective functions for burr height & thickness with the help of input parameters and then these two objective functions combined to construct a single objective function. In next stage improved version of elephant swarm optimization (ESWSA) algorithm is applied to get the optimum input parameters. The predicted output variable after the optimization techniques (Test 2 & Test 3) further checked with experimental result to determine the accuracy of the proposed model. In a conclusion section it is seen that the average error of drill diameter, drill point angle & spindle speed are 1.72%, 3.84% & 3.89% respectively with average RMSE is 2.56 *10^-6. For further validation of effectiveness of proposed model is also compared with the state of art techniques in the field burr minimization.

##### Parametric optimization of Liquid Flow Process by ANOVA Optimized DE, PSO & GA Algorithms

DOI: https://doi.org/10.5815/ijem.2021.05.02, Pub. Date: 8 Oct. 2021

Control of liquid level & flow are the most interest domain in process control industry. Generally process parameter of the liquid flow system is varied frequently during the operation. So the selection of the level of process parameters i.e. input variables seems to be important for achieving the optimum flow rate. In the present work focus is given on the identification of the proper combination of the input parameters in liquid flow rate process. Flow sensor output, pipe diameter, liquid conductivity & viscosity have been taken as input parameter; flow rate obtained from test is taken as response parameter. Till now several researchers have been performed various optimization methods for optimized the parameters of the process plant. But still computational time & convergence speed of the applied optimization techniques for the modelling of the nonlinear process system is still an open challenge for the modern research. In this research we proposed three evolutionary algorithms are used to optimize the process parameters of the nonlinear model implemented by ANOVA to mitigate the unbalance, convergence speed and reduce the total computational time. Overall research performed into three stage, in first phase nonlinear equation ANOVA has been used for mathematical model for the process, In second stage three evolutionary algorithms: GA, PSO & DE are applied for parametric optimization of liquid flow process to maximize the response parameter & in last phase comparative study performed on simulated results based on confirmed test & validated our proposed methodology.