Type of course:
Digital learning, Lesson
Language:
EN
Duration:
10 minutes
Workload:
2 hours
Proficiency:
Intermediate
Target:
Manager, Professionals, Workers
SUMMARY
Are you ready to harness the full power of data-driven quality control? In today’s manufacturing world, the key to superior quality and streamlined efficiency lies in knowing exactly where to position your sensors—especially cameras. This course dives into the essentials of “optimal sensor placement,” revealing the methods and strategies that industrial engineers and quality control specialists use to detect anomalies, reduce waste, and improve product consistency through smart sensor placement.
Imagine being able to spot defects in real-time or catch subtle irregularities with precision. It all starts with understanding where to place your sensors for maximum impact. You’ll learn not just the technical principles of field of view, line of sight, and lighting, but also practical techniques for placing cameras and sensors along production lines, inspecting everything from electronics to automotive parts to food packaging. This course covers real-world scenarios and includes interactive case studies, allowing you to practice designing sensor placement for various manufacturing needs.
By the end of this course, you’ll be equipped to design a high-impact sensor network tailored to your industry, saving time, reducing errors, and ensuring that your products meet the highest standards. Join us to transform how you approach quality control and make sensor-driven manufacturing work for you.

About The Author
Dilek Dustegor is a Professor of Computing Science at the University of Groningen in the Netherlands. She is interested in bridging the gaps between research, development and implementation using AI and automation. She is pursuing research about modeling, design and analysis of large scale / networked systems using IoT and ML techniques, with a special interest in smart city applications. She is a seasoned educator, and loves using the newest educational technologies for an enhanced learning experience.
Learning outcomes
- By the end of this lesson, students will be able to identify key factors in sensor placement through the analysis and application of essential principles—such as field of view, lighting conditions, and line of sight—to determine optimal placement of various sensors in an industrial setting.
- By the end of this lesson, students will be able to develop theoretical frameworks for sensor placement that incorporate principles of redundancy, data relevance, and process efficiency to enhance quality control and anomaly detection across diverse manufacturing applications.
- By the end of this lesson, students will be able to explain various approaches to camera placement for quality control, including factors like perspective, depth, and lighting, to ensure optimal image capture for reliable data analysis in industrial quality control.
Course Content
Topics
Automation and Sensoring, Automation and Robotics, Digital Transformation, Machine Learning
