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TheComputationalCore/README.md

👨‍💻 Dinesh Chandra

Applied Machine Learning Engineer | Engineering Systems | Robotics & Intelligent Manufacturing

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I am an applied machine learning engineer with a strong foundation in mechanical engineering, focused on building data-driven, production-ready ML systems for real-world engineering and robotic applications. My work bridges physics-based modeling, experimental data, robotics, and modern software engineering to deliver explainable, scalable, and deployable solutions.

🎓 Academic Background

  • B.Tech in Mechanical Engineering — Vardhaman College of Engineering, Hyderabad
    CGPA: 9.74 / 10 (2nd Rank in Branch, First Class with Distinction)
  • International Conference Speaker — ICAMS 2020 (Oral Presentation)

📄 Peer-Reviewed Research Publication

Title: Experimental Studies of Stellite-6 Hardfaced Layer on Ferrous Materials by TIG Surfacing Process
Authors: C Dinesh Chandra, B Rushikesh, Mohammed Numan, B Venkatesh
Journal: IOP Conference Series: Materials Science and Engineering
Volume: 998 (2020), 012061
DOI: 10.1088/1757-899X/998/1/012061
🔗 Full Paper: https://iopscience.iop.org/article/10.1088/1757-899X/998/1/012061

This experimental study on hardfacing alloys directly inspired my applied ML platform for material property prediction.

🔬 Research & Engineering Focus

  • Applied Machine Learning for Engineering & Robotic Systems
  • Physics-Informed & Data-Driven Modeling
  • Intelligent Manufacturing & Materials Informatics
  • Robotics, Mechatronics & Control Systems
  • Explainable AI (SHAP, feature attribution)
  • ML Deployment, MLOps & Production Systems
  • Finite Element Analysis (ANSYS) & Simulation-Driven Design

🛠 Tech Stack & Skills

Java Spring Boot Python scikit-learn Flask React PostgreSQL Docker ANSYS GitHub Actions

🚀 Flagship Projects

🧠 Material Hardness & Oxidation Prediction

Research-grade ML platform predicting mechanical and oxidation properties of hardfaced alloys (grounded in my published experimental work).

  • Random Forest + Linear Regression | SHAP Explainability | Flask Web App
    🔗 Repository | 🌐 Live Demo

🌬 Wind Turbine Blade Optimization

Physics-informed ML system optimizing blade performance using synthetic ANSYS-derived datasets.

🚀 Employee Management System (Enterprise HRMS)

Production-ready HR platform managing full employee lifecycle with AI-powered recruitment.

🚌 Bus Booking System

Modern full-stack ticketing platform with real-time seat selection and secure bookings.

👨‍💻 Let's Connect!

Open to collaborations in applied ML, robotics, intelligent manufacturing, or production systems. Feel free to reach out!

Profile Views

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  1. Material-Hardness-Oxidation-Prediction Material-Hardness-Oxidation-Prediction Public

    Machine learning system for predicting material hardness and oxidation rate with explainability, validation, and a modern web interface

    Python

  2. WindTurbineBladeOptimization WindTurbineBladeOptimization Public

    Machine Learning project for optimizing wind turbine blade performance using Random Forest models, with a full Flask web app and live deployment.

    Python

  3. bus-booking-system bus-booking-system Public

    Full-stack Bus Booking System built with React, Spring Boot, and PostgreSQL — featuring JWT authentication, real time seat selection, booking management, and a fully deployed cloud architecture on …

    JavaScript

  4. employee-management employee-management Public

    Employee Management System built with Spring Boot, PostgreSQL, Docker, and CI/CD — featuring AI-powered recruitment, onboarding, payroll, performance reviews, and role-based access.

    Java