Introduction to Quality Control and AI/ML Technologies

• Overview of Quality Control (QC) systems and their importance in modern industries
• Introduction to Artificial Intelligence (AI) and Machine Learning (ML)
• How AI and ML are transforming quality control and manufacturing processes
• Understanding the role of data in AI/ML for quality control
• Key concepts: Supervised vs. unsupervised learning, neural networks, and deep learning
• Exploring the types of data used in AI/ML models for quality control

Data Collection, Preparation, and Analysis for AI/ML

• The importance of data quality in AI/ML applications
• Methods for collecting and preparing data for AI/ML models
• Data cleaning, normalization, and feature selection
• Statistical analysis techniques for quality control (e.g., regression analysis, hypothesis testing)
• Using AI tools for data visualization and anomaly detection

AI and ML Techniques for Predictive Maintenance and Quality Forecasting

• Introduction to predictive maintenance and its role in quality control
• Building predictive models using AI and ML: Regression models, time series forecasting, and decision trees
• Applying machine learning algorithms for defect detection and anomaly prediction

Automation of Defect Detection Using AI and Machine Learning

• Introduction to automated defect detection in quality control
• Using computer vision and image recognition for defect detection
• Training ML models to recognize defects and anomalies in production processes

Integration, Impact Evaluation, and Future of AI/ML in Quality Control

• Integrating AI and ML systems with existing quality management frameworks
• Evaluating the impact of AI/ML-driven quality control systems on operational efficiency
• Managing the transition to AI-powered quality control systems