Priyanka Devi Pantula

Bio: Priyanka Devi Pantula is an academic researcher from Indian Institute of Technology, Hyderabad. The author has contributed to research in topics: Sobol sequence & Fuzzy clustering. The author has an hindex of 4, co-authored 20 publications receiving 94 citations. Previous affiliations of Priyanka Devi Pantula include Indian Institutes of Technology.

KERNEL: Enabler to build smart surrogates for online optimization and knowledge discovery

Abstract: KERNEL – A novel parameter-free surrogate building algorithm using Adaptive Neuro Fuzzy Inference System (ANFIS) is presented to provide an intelligent and robust technology to optimally es...

Data driven robust optimization of grinding process under uncertainty

Abstract: Uncertain process parameters present in industrial grinding circuits (IGC) increase the difficulty in modeling IGC Conventionally, researchers have resorted to box approach for sampling in the unc

Pattern Recognition and Machine Learning

Abstract: Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

[IEEE IEEE International Conference on Networking, Sensing and Control, 2004 - Taipei, Taiwan (March 21-23, 2004)] IEEE International Conference on Networking, Sensing and Control, 2004 - Particle swarm optimization algorithm and its application to clustering analysis

Abstract: Clustering analysis is applied generally to Pattern Recognition, Color Quantization and Image Classification. It can help the user to distinguish the structure of data and simplify the complexity of data from mass information. The user can understand the implied information behind extracting these data. In real case, the distribution of information can be any size and shape. A particle swarm optimization algorithm-based technique, called PSO-clustering, is proposed in this article. We adopt the particle swarm optimization to search the cluster center in the arbitrary data set automatically. PSO can search the best solution from the probability option of the Social-only model and Cognition-only model[1, 2, 3J. This method is quite simple and valid and it can avoid the minimum local value. Finally, the effectiveness of the PSO-clustering is demonstrated on four artificial data sets.

Constrained multi-objective optimization of EDM process parameters using kriging model and particle swarm algorithm

Abstract: This paper investigates the highly nonlinear relationship between process parameters and machining responses, including material removal rate (MRR), surface roughness (SR), and electrode wear rate ...

Optimization of Machine Process Parameters in EDM for EN 31 Using Evolutionary Optimization Techniques

Abstract: Electrical discharge machining (EDM) is a non-conventional machining process that is used for machining of hard-to-machine materials, components in which length to diameter ratio is very high or products with a very complicated shape. The process is commonly used in automobile, chemical, aerospace, biomedical, and tool and die industries. It is very important to select optimum values of input process parameters to maximize the machining performance. In this paper, an attempt has been made to carry out multi-objective optimization of the material removal rate (MRR) and roughness parameter (Ra) for the EDM process of EN31 on a CNC EDM machine using copper electrode through evolutionary optimization techniques like particle swarm optimization (PSO) technique and biogeography based optimization (BBO) technique. The input parameter considered for the optimization are Pulse Current (A), Pulse on time (µs), Pulse off time (µs), and Gap Voltage (V). PSO and BBO techniques were used to obtain maximum MRR and minimize the Ra. It was found that MRR and SR increased linearly when discharge current was in mid-range however non-linear increment of MRR and Ra was found when current was too small or too large. Scanning Electron Microscope (SEM) images also indicated a decreased Ra. In addition, obtained optimized values were validated for testing the significance of the PSO and BBO technique and a very small error value of MRR and Ra was found. BBO outperformed PSO in every aspect like computational time, less percentage error, and better optimized values.

Multi-objective optimization of iron ore induration process using optimal neural networks

Abstract: Induration in steel industries is the process of pelletizing iron ore particles. It is an important unit operation which produces raw materials for a subsequent chemical reduction in Blast ...