Industrial AI Projects
Explore a range of AI-based industrial projects designed to provide hands-on training and real-world experience.
- Tools: Python, OpenCV, TensorFlow/PyTorch, CNN
- Goal: Detect visual defects (scratches, misalignments) on products using images
- Use case: Quality assurance in industrial automation
- Tools: Python, Scikit-learn, LSTM, IoT sensor data
- Goal: Predict machine failure based on historical sensor data
- Use case: Minimize downtime & reduce maintenance costs
- Tools: Python, ARIMA/LSTM, Pandas, Scikit-learn
- Goal: Forecast inventory needs based on past sales and trends
- Use case: Inventory optimization in warehouses and factories
- Tools: YOLOv8, OpenCV, Real-time video feed, Deep learning
- Goal: Detect unsafe behavior (no helmet, unsafe zones) using real-time feed
- Use case: Workplace safety in factories/construction sites
- Tools: NLP (Transformers, GPT), Python, Flask, LangChain
- Goal: Automate support queries from factory workers
- Use case: 24/7 automated support in industrial environments
- Tools: Machine Learning, Optimization Algorithms, Python
- Goal: Analyze & predict energy usage patterns; suggest optimizations
- Use case: Reduce power consumption in industrial plants
- Problem Definition
- Data Collection (synthetic or real-world)
- Data Preprocessing
- Model Selection
- Training & Evaluation
- Deployment (Flask/Django + Web UI or API)
- Documentation & Report
- Dataset (cleaned & labeled)
- Codebase (Python/Jupyter/Notebook)
- Trained model (.h5 or .pkl)
- Web App/API (optional)
- PPT for presentation
- Final Report (PDF/Doc)
