🏗️AI-Augmented Design Feedback System

📚Table of Contents

• Introduction
• Features
• Project Structure
• Installation
• Usage
• Model Training
• Contributing
• License
• Acknowledgements

📑Introduction

The AI-Augmented Design Feedback System is an innovative solution aimed at enhancing mechanical design processes by providing AI-powered feedback on efficiency, safety, and performance. This system utilizes simulations and predictive modeling to suggest improvements, thereby optimizing the product development lifecycle.

📑Features

• 📌Efficiency Analysis: Automatically evaluate design efficiency and suggest improvements.
• 📌Safety Assessment: Predict potential safety issues based on design parameters.
• 📌Performance Optimization: Analyze and optimize design performance through predictive modeling.
• 📌Simulation Capabilities: Run simulations to foresee design outcomes and refine models.
• 📌Extensible Framework: Easily integrate with other engineering platforms or tools.

Project Structure

AI_Augmented_Design_Feedback_System/

• data/
  • sample_designs/
    • design1.json
    • design2.json
  • generated_data/
• models/
  • efficiency_model.pkl
  • performance_model.pkl
  • safety_model.pkl
• src/
  • design_feedback.py
  • predictive_modeling.py
  • simulations.py
• Dataset Generation and Model Training/
  • Model Training/
    • train_model.py
  • Data Generation/
    • efficiency_data.py
    • performance_data.py
    • safety_data.py
• tests/
  • test_design_feedback.py
  • test_predictive_modeling.py
  • test_simulations.py
• run.py

Installation

1. Clone this repository:

   git clone https://github.com/AdityaSrivastavDS/AI-Augmented-Design-Feedback-System

2. Navigate to the project directory:

   cd AI_Augmented_Design_Feedback_System

3. Install the required packages:

    pip install -r requirements.txt

Usage

1. Analyzing a Design:

• Run the run.py script to analyze a design: python run.py

2. Testing:

• Run unit tests to ensure everything is functioning as expected pytest tests/

Model Training

To train the models:

• Generate datasets by running the scripts in Data Generation.
• Train the models using the scripts in Model Training.
• Models will be saved in the models/ directory.

📒Contributing

Contributions are welcome! Please fork this repository and submit a pull request for review.

📜License

This project is licensed under the MIT License. See the LICENSE file for more details.

📒Acknowledgements

• Tools Used: Python, scikit-learn, pandas, NumPy, etc.
• Inspiration: Inspired by the need for automation in mechanical design evaluations.