KTEK0070 Machine Learning in Digital Manufacturing

Overview

This course offers a comprehensive exploration of Machine Learning (ML) in Digital Manufacturing, showcasing how 📊 data-driven insights and 🤖 intelligent decision-making enhance process monitoring and quality assurance. It begins with an introduction to process quality and in-situ monitoring, emphasizing how real-time data acquisition enables early defect detection and precision optimization. Participants will examine key sensorization technologies 📡—including optical and acoustic systems—and explore data acquisition methods that convert raw sensor outputs into actionable insights. These insights support accurate process control and improved manufacturing reliability.

📌 Key Topics Include

• Introduction to ML in Digital Manufacturing: Concepts, benefits, and real-world applications.
• Process quality and in-situ monitoring: Real-time data capture for early defect detection.
• Sensorization technologies: Optical and acoustic sensors for process control.
• Data processing and statistical methods: Cleaning, transforming, and analyzing raw datasets.
• ML Fundamentals: Data-driven modeling, neural networks, and backpropagation.
• Applications in defect detection and automation: Enhancing efficiency and product consistency.
• Integration of intelligent systems: IIoT, smart factories, and self-adaptive manufacturing.
• Real-world case studies: Use cases in AM, 3D printing, laser-based processing, and surface engineering.
• Future trends and interactive discussions: The role of AI in sustainable and intelligent manufacturing.

🎓 Who Should Attend?

This course is ideal for:

• Graduate students, researchers, and industry professionals interested in ML applications for manufacturing.
• Engineers and analysts seeking to optimize workflows using sensor data and ML-based strategies.
• Professionals aiming to integrate AI-driven control systems for enhanced manufacturing performance.

🏆 Learning Outcomes

By the end of the course, participants will be able to:

• 🔍 Apply real-time defect detection methods using sensorization techniques.
• 🤖 Understand how ML models process sensor data within manufacturing systems.
• 📈 Design and implement ML-driven strategies to improve quality and efficiency in digital manufacturing.

📅 Format & Delivery

• Lectures delivered via in-person sessions.
• Includes interactive labs, quizzes, and project-based assessments.
• Supplementary materials and slide decks available on Moodle.

🔗 Resources

• 📂 Download Slides: Click here