Goureesh Joshi

Project 1: Developed machine vision algorithms for improved production efficiency and reduced product defects using image processing techniques. Project 2: Conducted heat dissipation studies of copper pipes to improve understanding of heat transfer mechanisms and energy efficiency. Experiments measured heat transfer coefficients and thermal resistance. Skills: Machine vision · Python (Programming Language) · Data Analysis · CAE · CAD

Titel : "Development of digital twin to predict the life of industrial components" Developed a digital twin for industrial component failure prediction, analyzed real-time sensor signals, created a reduced order model, validated results with 95% accuracy, and analyzed data through a dashboard. Result: 5% increase in component sales. Skills: Response Surface Methodology · Sensor Fusion · Fatigue and damage tolerance · Digital twin development · Data Analysis · FMU · DoE · ANSYS · CAD Solidworks · CAE

- Conducted extensive information gathering and literature survey to develop a comprehensive understanding of digital twins and their application for predicting component life. - Conducted research to identify trends and best practices in digital twin development and application for predictive maintenance. Analyzed existing research and industry reports for insights into the latest technologies and methods. Skills: Literature survey · Market Research

All the projects during Bosch tenure: https://www.linkedin.com/in/goureesh-joshi/ - Developed and validated hydraulic, steering, and braking systems designs for various clients using CAD and CAE tools like Creo, CATIA V5, Abaqus, and ANSYS. - Streamlined design process with data management and automation. - Conducted simulations and analysis to assess component performance, reliability, and safety. - Validated results with experimental data and made recommendations for improvements.

Title: " Development of a dynamic simulation environment to analyze real-time friction forces using neural network-based models." Developed a dynamic simulation environment in Python using the Newmark Beta algorithm and a neural network trained on the Stribeck friction model to predict the behavior of the beam under sliding forces. Validated results with Ansys and visualized with Matplotlib and interactive dashboards.

Subjects included:- Strength of Material,Theory of Mechanics, Fluid Mechanics,Hydraulics and pneumatics,