Technical Group Webinar
JOIN OUR NDT AND CONDITION MONITORING TECHNICAL GROUP (TG8) WEBINAR
Who should attend?
Structural integrity engineers, reliability engineers, NDT Engineers, asset owners.
What will you learn?
AI/ML has the potential to improve NDT effectiveness significantly. Presentations will cover:
- How AI may be used to analyse NDT data
- Benefits of the use of AI/ML for evaluation of large quantities of data
- Examples of the use of AI/ML in an NDT context
- Advantages and limitations of this approach
- Existing and emerging regulation, standards and codes
Experts from industry will be presenting on a range of topics:
- Iikka Virkkunen, CEO, Trueflaw - Today and Tomorrow for AI in NDE
- Christophe Reboud, Head of Simulation and AI Services, CEA and Pierre Calmon, NDE Scientific Director, CEA - Joint Use of Simulation and Artificial Intelligence for Applications in NDE and SHM
- Katy Tant, Senior Lecturer in AI for Engineering, University of Glasgow - Model-based Deep Learning Approaches for Weld Tomography
- Gareth Pierce, Professor, University of Strathclyde - Use of AI and ML in NDT Defect Detection
- Rich Pyle, Research Associate, University of Bristol and Sergio Cantero Chinchilla, Lecturer, University of Bristol - The Application of Deep Learning to Ultrasonic NDE
- Channa Nageswaran, Technology Consultant, TWI Ltd - An Industrial Perspective on Adopting AI/ML into NDT Procedures
Please note that this event will be recorded.
FEES
- Professional Members of The Welding Institute:
-
- AWeldI: £25 + VAT (£30)
- TechWeldI/MWeldI/FWeldI: Free
- Industrial Members of TWI Ltd: £25 + VAT (£30)
- Non-members: £95 + VAT (£114)
- Retired Members: £25 + VAT (£30)
- Students/Lecturers : £25 + VAT (£30)
- TWI Training Customers: £75 + VAT (£90)
- TWI Training School Customers with professional membership: Free: Please contact us with your Training Customer ID Number
TWI Staff - please contact Kayleigh Elt if you wish to register.
Please note that payment must be made before the event in order to receive the joining instructions for the event.
CAN'T MAKE IT ON THE DAY?
If you can't join us on the day, you can still access the recording of the webinar and the presentations by registering ahead of the event.*
*presentation dissemination is dependent on presenter consent.
ASSOCIATE MEMBERSHIP (AWeldI) OF THE WELDING INSTITUTE
Registering on this Technical Group webinar as a
non-member or a TWI Training Customer without Professional Membership gives you
full Associate Membership (AWeldI) for one year. To activate your membership, just complete the highlighted fields on the event registration form.
You will also benefit from:
BENEFITS OF ATTENDING THIS WEBINAR
- Information transfer from experts
- Discussion, questions and answers on the specific topics
- A broader understanding of the extent of standards in the industry
- Attendance of this event qualifies for CPD
CONTINUOUS PROFESSIONAL DEVELOPMENT (CPD)
The Welding Institute awards points towards CPD for delegates attending this webinar. Every hour attendance of an event will earn 2 points towards your continuing professional development.
Please contact kayleigh.elt@twi.co.uk if you require a certificate of attendance towards your continuing professional development.
MEET THE SPEAKERS
Katy Tant
University of Glasgow
Model-Based Deep Learning Approaches for Weld Tomography
Dr. Katy Tant is Senior Lecturer in Artificial Intelligence for Engineering at the University of Glasgow. Her expertise lies in the mathematical modelling and simulation of wave propagation in complex media, and how to leverage these models to exploit ultrasonic sensor data for parameter estimation and enhanced imaging algorithms, particularly in the context of non-destructive evaluation of welded structures.
Sergio Cantero Chinchilla
University of Bristol
The application of deep learning to ultrasonic NDE
Sergio is a Lecturer in Data-Driven Engineering whose expertise lies in the intersection of multiple fields, including data science, uncertainty quantification and machine learning. Sergio has developed deep learning solutions for ultrasonic NDE problems and his current interests include uncertainty quantification, explainability of AI methods, optimisation and transfer learning.
Rich Pyle
University of Bristol
The application of deep learning to ultrasonic NDE
Richard Pyle is a researcher working on ultrasonic non-destructive applications, focusing mainly on automation of data analysis. Overcoming the industry specific barriers to the application of deep learning has been Richard’s primary research goal. This work has involved studying uncertainty quantification, simulation of training sets, interpretable machine learning methods and domain adaptation techniques.
Channa Nageswaran
TWI Ltd
An Industrial Perspective on Adopting AI/ML into NDT Procedures
Channa has been working in TWI for over 20 years developing advanced ultrasonic testing techniques. His work includes novel approaches to inspecting austenitic and dissimilar materials, high temperature hydrogen attack, stress corrosion cracking, electron beam welds and at high temperatures. Channa has experience in research and development as well as in industrial tasks such as development of procedures and site implementation of those procedures. Channa holds an Engineering Doctorate in the development of phased array ultrasonic techniques for heterogeneous materials and is a Fellow of the Institution of Mechanical Engineers.
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