Demonstration of Using Open Source Software to Own Your NDT Data
Tracks
NDT Methods
| Tuesday, October 22, 2024 |
| 8:00 AM - 8:45 AM |
| 213/214 - Campfire & Deep Dive |
Details
The purpose of this presentation is to demonstrate to the audience how to get your NDT data into open source software tools and provide inspiration on how you could use these tools to add value to your inspection results and to your inspection business.
We will discuss what NDT data is, at its basic level just collections of numbers with metadata.
We will import NDT data from commercial hardware into an open source software, NDEToolbox. This will include UT and ET data.
We will discuss a variety of algorithms in the AI/ML family that may be applicable to NDT data analysis.
We will use a simple AI algorithm to examine the UT data for indications.
We will discuss what NDT data is, at its basic level just collections of numbers with metadata.
We will import NDT data from commercial hardware into an open source software, NDEToolbox. This will include UT and ET data.
We will discuss a variety of algorithms in the AI/ML family that may be applicable to NDT data analysis.
We will use a simple AI algorithm to examine the UT data for indications.
Speaker
Mr David Forsyth
Principal Scientist
Texas Research International
Demonstration of Using Open Source Software to Own Your NDT Data
8:00 AM - 8:45 AMPresentation Description
The purpose of this presentation is to demonstrate to the audience how to get your NDT data into open source software tools and provide inspiration on how you could use these tools to add value to your inspection results and to your inspection business.
We will discuss what NDT data is, at its basic level just collections of numbers with metadata.
We will import NDT data from commercial hardware into an open source software, NDEToolbox. This will include UT and ET data.
We will discuss a variety of algorithms in the AI/ML family that may be applicable to NDT data analysis.
We will use a simple AI algorithm to examine the UT data for indications.
We will discuss what NDT data is, at its basic level just collections of numbers with metadata.
We will import NDT data from commercial hardware into an open source software, NDEToolbox. This will include UT and ET data.
We will discuss a variety of algorithms in the AI/ML family that may be applicable to NDT data analysis.
We will use a simple AI algorithm to examine the UT data for indications.
Biography
David Forsyth is an ASNT Fellow. He has a Master’s degree in Engineering, is studying towards a Master’s Degree in AI, and has been involved in nondestructive testing for over 30 years. He has worked to measure and improve the reliability of NDT in aerospace and other industries, with a concentration on software tools. He has participated in accident investigations in civilian and military aviation. He chairs the NDT Reliability Studies Committee of ASNT and is on the NASA Engineering Safety Center’s Technical Discipline Team for NDE.
