Pradeep Chakravarthi Nutakki

 Time-Series Data acquisition and developing the concept of predictive maintenance on rotary lobe pump.  Generating data values from various sensors connected to the pump through Raspberry Pi & Arduino.  Creation of data prototypes, supervision of time-series data for analysis, and validation through Micropython, C/C++, MODBUS.  Implementation of machine learning techniques on pump datasests, storing data in the database, and visualization in web.

• Tools: Python programming, PyTorch, RNN, LSTM, dual quaternion algorithm, deep learning • Objective: To optimize the transport of goods on an automated logistics machine by training a machine-learning algorithm to predict the position and order of a package in the next time step by providing data from previous time steps as inputs. • Training machine learning algorithms such as RNNs using Python-based machine learning libraries.

• Tools: Pytorch, LSTM (Long Short Term Memory), Covid19- confirmed case data, Deep Learning • The project that was attached in the GitHub link consists of data of covid19 confirmed cases of recent information and trained data using (Long-Short Term Memory) LSTM techniques. • This resulted to predict an approximate increase of future confirmed covid19 cases globally with limited available data from the source.

• Tools: Python programming, Pandas, Tkinter • To make a custom .xlsx, .txt files to save data automatically through python programming with Pandas, Tkinter libraries that benefit in further extension of other projects. • The data entered can be edit, delete, show last entry, or even quit from the window of entering details. If an excel workbook is already existing with the file name then the newly entered data will be appended or else the program creates a new workbook under the desired file name.

• Tools: Python programming, PyTorch, Scikit-Learn, Numpy, Pandas, Matplotlib • Extraction of features and visualising the raw data from a senor of a metal production process. • To analyse wear & tear of the cutting tool in order to enhance the production by reducing the manufacturing downtime.

• Tools: Python programming, PyTorch and Keras, CUDA • Goal: Controlled movement of a parcel on a conveyer belt places it to the right destination box. • The implementation of machine learning techniques such as Reinforcement Learning (RL) to choose the best path in order to approach target by parcel. • The RL algorithms like Actor-Critic were developed to enhance the analysis of parcel’s motion control as not to divert its best path from sorting.

• Tools: AutoCAD, CATIA, Ansys. • Lead a team of five members for designing a mechanical device to convert the solar energy into electrical energy. • A solar panel was mounted on a geared steel framework along with a sensor that aids in the rotation of the panel along the direction of the sun. • Power consumption analysis and comparison with the case of a non-geared panel.

• Lead a team of five members to analyze the boilers for improving its efficiency to improve energy production in the power plant. • Suggested insulation techniques to use bagasse along with cotton which is available at nearby fields as an insulator to pipes and boilers to reduce the loss of energy and suitable fast production system.

Experienced about the moldings, extrusions, and other processes of steel in the foundry department.

Practical training on assembly and disassembly of IC engine parts at TVS Motors.

Machine Learning, Modelling Simulation, Project Management.

Mechanical design, FEM Analysis, Heat Transfer, Strength of Materials, Heat Transfer, Operational Research, Engineering Management, Machinery Labs, National services

During this course, I have Learned the following Subjects 1. Mathematics (99%) 2. Physics (both theoretical and practical study) 3. Chemistry (both theoretical and practical study) 4. English ( 2nd language)