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MADE_4 - Case Studies in AI: AI-Powered Quality Control in Manufacturing (Pilot)

Type of course:

Digital learning, Lesson

Language:

EN

Duration:

20 minutes

Proficiency:

Intermediate

Target:

Professionals

This course introduces working professionals in the manufacturing field to the transformative potential of Artificial Intelligence (AI) in quality control. Participants will learn to harness AI-powered tools, including machine vision and deep learning algorithms, to enhance defect detection, automate inspections, and ensure product consistency. Through practical examples and case studies, this course equips learners with the skills to integrate AI into their quality control processes effectively.

About The Author

Carlo Ongini is the Head of Innovation at the MADE Competence Center in Milan, Italy. He holds a PhD in System and Control Automation and a master’s degree in Computer Engineering from Politecnico di Milano. With a deep expertise in Computer Vision, AI, and Machine Learning, his research focuses on their application in Automation, Robotics, and Industry 4.0.

Ongini has authored over 10 publications and a patent application. He has extensive experience across various industry sectors, having led advanced technology initiatives at Electrolux, driving intelligent automation solutions in manufacturing, and at Vodafone Business, where he managed the development of 5G and IoT solutions for industry applications. He also played a key role in Vodafone’s 5G Trials in Milan and was instrumental in the growth of Smart Robots, an Italian startup specializing in robotic and visual automation systems.

MADE is a Competence Center for Industry 4.0 created to implement Orientation, Training, and Finalization activities for technology transfer projects with companies on Industry 4.0 issues. The ultimate goal of the Competence Center is to keep the profile of companies high, competitive, and sustainable. Moreover, MADE  supports manufacturing companies, especially small and medium enterprises, on the path of digital transformation to factory 4.0: smart, connected, and sustainable, by providing a wide range of knowledge, methods and tools on digital technologies.


Learning outcomes

  1. Understand the principles of AI and machine vision and their applications in manufacturing quality control.
  2. Develop the ability to design and implement AI-powered defect detection systems using deep learning techniques.
  3. Evaluate the benefits, challenges, and future trends of AI integration in quality control processes.

Topics

Uncategorized

Tags

Machine learning, Features, Featrue selection

Content created in 2024
+46 enrolled
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