Interpreting Detected Defects in WAAM with NDE Techniques and AI

In this section

Back to Core Research Programme AMorph – 4D Printing for Field Morphing Structures Automation of Composite and Hybrid Joining Process Coaxial Wire Additive Manufacturing Processes and In-Line Monitoring Tools Development of Coded Excitation Technique for Non-Metallic Pipes Inspection Development of Digital Twin technology as a demonstrator for real-time integrity assessment of crack growth in tensile specimens Development of Friction Stir Welding (FSW) for Hydrogen Fuel Storage Tanks Development of Leak-Before-Break Hydrogen Storage Composite Pressure Vessels With Polymeric Liner Establishing Assessment Methods for Determining Safe Service Limits of High-Temperature Materials in Extreme Environments Fracture toughness in aggressive environments: effect of crack monitoring techniques on test results In-process detection and non-destructive testing of electron beam weld imperfections Internal Bore Coatings for Applications in Corrosion and Hydrogen Environment Joining of Additively Manufactured Materials Long term behaviour of polymeric liner materials/coatings in hydrogen service Techniques for High Temperature Ultrasonic Inspection of Arc Welding Ultimate Leverage Fund

Project Code: 35926

Start date and planned duration: April 2024, 36 months

Objective

Develop a knowledge baseline for NDE detectable flaws in WAAM builds
Develop AI algorithms for the detection of defects arising from the WAAM process using novel and established in-situ monitoring techniques
Demonstrate the suitability of the NDE collection of data during the manufacture of the WAAM build with a goal of seeking to enhance both the reliability of defect detection and the overall quality of WAAM parts
Develop baseline guidance for meaningful flaw detection for WAAM industry

Project Outline

This new CRP project seeks to build on the knowledge of application of NDE for WAAM, to advance the baseline of knowledge for NDE detected flaws, to give a guideline and provide a meaningful flaw detection sentencing confidence for industry.

Once the knowledge baseline for flaw detection has been achieved, it then makes sense to investigate the interpretation of captured NDE data and use the knowledge gained for the flaw detection baseline to investigate AI-based in-process flaw detection methods for WAAM. The secondary goal is to be able to automate the application of NDE and collection of data during the manufacture of the WAAM build and use automated flaw detection / recognition and characterisation through application of computational or artificial intelligent (AI) means.

Industry Sectors

- Aerospace

- Defence

- Oil and Gas

- Power

Benefits to Industry

Guidelines that can help the industry to assess the quality and reliability of WAAM parts during build and post build, through the provision of a clear and consistent criterion for flaw detection and evaluation.
Utilisation of the expertise gained on artificial intelligence discrimination using in-situ inspection and in-process monitoring of AM for development of their specific AM equipment or components.
Cost saving, quality improvement and increased productivity through reverse engineering of extended capabilities into existing manufacturing streams.