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🏗️AI-Augmented Design Feedback System

Project Banner

📚Table of Contents

📑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
  1. 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.