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On-demand demo webinar
Thermo Scientific Avizo Software is an all-in-one image analysis digital lab that helps you quantify 3D full-field displacement and strain maps from volume images acquired before and after material deformation. The software can both visualize and analyze the changes to quantify deformation-induced microstructural changes, such as strain transfer in multiphase materials, pore growth and coalescence related to fractures, and crack opening displacement (COD).
In our previous webinar—which is also available on-demand—we provided an introduction to digital volume correlation. In this webinar, field experts showcased Avizo Software’s DVC capabilities.
Watch now to learn:
How to design a DVC experiment
The difference between global and local approaches to DVC
How DVC is used with large datasets
The advantage of mechanical regularization
How DVC can help inform and validate numerical simulations
About the speaker(s)
Kamel Madi
CEO, 3Dmagination Ltd
Kamel has more than 15 years of academic and industrial research experience. He has acquired solid methods in 4D imaging combined with digital volume correlation to capture dynamic processes of materials in service or during the manufacturing process across diverse areas such as the glass industry and healthcare. He now uses his expertise and skills to help enhance software performance, particularly focusing on optimizing user experience.
Loic Courtois
CSO, 3Dmagination Ltd
Loic has worked in 4D imaging for many years and has a deep understanding of the complete workflow, from experimental design to analysis and data extraction. Drawing from his experience on a wide range of materials, Loic has been heavily involved in the development of the digital volume correlation interface in Avizo Software.
Luigi Raspolini
Product Marketing Manager, Thermo Fisher Scientific
Luigi is a process engineer working in materials science and characterization. With a background in spectroscopy and electron microscopy, he helps customers find the right tools to convert datasets into actionable data, track improvements, and identify imperfections in manufacturing or research processes.