Getting Started with AI in Manufacturing
Getting Started with AI in Manufacturing
• Overview of AI applications in manufacturing.
• Benefits of AI in efficiency, quality, and cost reduction.
• Real-world use cases and success stories.
• Understanding predictive maintenance and why it's crucial.
• Data sources: IoT sensors, PLC/DCS systems.
• AI methods for predictive maintenance.
• Creating a predictive maintenance process.
Quality Control and Defect Detection (Detection-Based Approach with Segmentation Models)
Quality Control and Defect Detection (Detection-Based Approach with Segmentation Models)
• The role of computer vision in quality control.
• Object detection models (e.g., YOLO, Faster R-CNN).
• Segmentation models for detecting defects (e.g., U-Net, Mask R-CNN, SegFormer).
• Training and deploying models based on detection.
Quality Control and Defect Detection (Latest Anomaly Detection Methods)
Quality Control and Defect Detection (Latest Anomaly Detection Methods)
• Summary of Anomaly Detection in Manufacturing
• Newest Techniques in Anomaly Detection
• Comparing Detection-Based Approaches with Anomaly Detection Methods
• Training Anomaly Detection Models Using Real-World Manufacturing Data
• The Importance of Demand Forecasting in Manufacturing.
• Machine Learning Techniques for Forecasting:
• Time Series Models (e.g., ARIMA, LSTM).
• Hybrid Models Combining Time Series and Tabular Features.
• Key Metrics to Evaluate Forecasting Accuracy.
Developing an RAG Agent for Manufacturing - Part 1
Developing an RAG Agent for Manufacturing - Part 1
• Introduction to Retrieval-Augmented Generation (RAG).
• Setting up a vector database (e.g., pgvector, Pinecone, FAISS).
• Chunking manufacturing knowledge data for retrieval.
• Implementing knowledge upload and retrieval processes.
Developing an RAG Agent for Manufacturing - Part 2: SQL Agent
Developing an RAG Agent for Manufacturing - Part 2: SQL Agent
• Integrating knowledge retrieval with SQL databases.
• Using a Language Model to query and interact with SQL databases for manufacturing tasks.
• Developing a system to interpret user queries, generate SQL commands, and retrieve data.
• Deploying the SQL-powered RAG agent.
