Anirudh Kumar

Dynamic Shear Stress Evaluation on Micro-Turning Tool Using Photoelasticity

The paper presents an experimental methodology for evaluation of shear stress induced on a micro-turning tool during micro-turning operation. A micro-turning tool manufactured by Sandvik Coromant was used for micro- turning of a brass spindle. The front face of the tool was cleaned, polished and coated with a thin layer of Ethylene-Vinyl Acetate (EVA), a birefringent material. Subsequently, reflection photoelastic experiments were conducted to find the shear stresses induced on the micro-tool. A custom designed grey field poledioscope was used for this purpose which was pre-calibrated and verified using disk under compression test. The tool was subjected to turning operation and dynamic images of the tool were captured using Casio ELIXIM ZR-200 camera in high speed movie mode at 1000 fps (frames per second) for four different orientations of the analyzer simultaneously. These images were processed using a code developed in MATLAB software to generate a shear stress map of the tool dynamically at different time instants of the machining process.

Appliance classification using energy disaggregation in smart homes

In this work we have addressed the problem of appliance classification and power consumption anomaly detection using energy disaggregation and machine learning techniques. The active power consumption data, received from a smart-meter, has been used as the only parameter for solving our problem. We have implemented a decision tree algorithm to classify appliances based on thresholds of their power consumption. Additionally, we have also proposed and implemented an algorithm for unusual fluctuation detection based on average magnitude of such fluctuations and an appliance quality recommender based on power-factor of the appliance. Initial results are promising as the classifier works correctly for 74% of instances, while the anomaly detector works correctly for 80% anomalies.

An uncommon encounter during temporary pacemaker implantation – A double inferior vena cava

Double inferior vena cavae (IVC) is a congenital variation caused by an unusual embryological development of the IVC. We report an incidental finding of infrarenal double IVC in a 70-year-old female.

FPGA based model predictive controller for dynamic power management of a battery powered electric car

Any battery powered electric vehicle is a safety critical system due to very high probability of untimed power failure. In this work a model predictive controller has been implemented in Field Programmable Gate Array (FPGA) for safe state generation by enhancing the runtime of electric car based on predicted battery state of charge. Initially an area and time efficient Coulomb counting technique has been implemented in FPGA. Subsequently a proactive load controller has been developed using FPGA from predicted State of Charge (SOC). The controller proactively optimizes the constrained battery energy by varying the power delivered to the noncritical loads and supports critical loads as per demand. The paper validates the proposed mechanism by experimental results.