Webinar details
Robotics is increasingly moving beyond automated inspection towards systems that can interpret what they detect and support decisions during fabrication. But what does it take to connect robotic mobility, sensing and AI-based visual recognition with practical manufacturing processes?
This webinar explores the progression from robotic inspection to AI-enhanced large-format additive manufacturing in the built environment. Drawing on façade-inspection research using convolutional neural networks (CNNs) for crack detection, the session connects robot mobility, sensing, and visual recognition with the broader perception-to-action workflow.
The webinar then considers how coordinated robots could combine deposition, inspection, and corrective action in applications such as concrete 3D printing. It examines how inspection feedback could inform process adjustments, pauses, repairs, or human review, creating opportunities for more responsive fabrication processes.
The session will also discuss key technical challenges, including shared spatial understanding, reliable AI perception, and robot coordination. Throughout the webinar, demonstrated inspection capabilities will be clearly distinguished from proposed pathways towards intelligent fabrication, providing a practical view of where the technology stands today and where further development is needed.
- The webinar will be recorded and will be sent out to registered attendees afterwards.
- A certificate of attendance will be provided to attendees who request one near the end of the live webinar session.
- Please note: the time stated on this event is in UTC. You will need to convert this to your own time zone.
Key takeaways from this webinar
- Learn how robotic mobility, sensing, and AI-based visual recognition work together to support surface inspection and defect detection.
- Explore how inspection feedback could guide process adjustments and corrective actions in large-format additive manufacturing.
- Identify the challenges involved in coordinating fabrication and inspection robots, including shared spatial understanding, perception reliability, and human oversight.
About the presenter
Dr. Maryam Kouzehgar – EIT Lecturer
Dr. Maryam Kouzehgar is a Lecturer at the Engineering Institute of Technology (EIT), based at the Melbourne Campus, where she collaborates with the Schools of Electrical Engineering and Industrial Automation Engineering.
Dr. Kouzehgar holds B.Sc., M.Sc., and Ph.D. degrees in Electrical Engineering, specialising in Control Systems. She completed her Ph.D. in 2015 and has more than 15 years of academic experience in Australia and internationally, spanning teaching, research, postgraduate supervision, and curriculum development and leadership.
Before joining EIT, Dr. Kouzehgar worked as a Senior Postdoctoral Research Fellow at the SUTD-MIT International Design Centre (IDC) in Singapore.