Sensor-enabled robotics for ultrasonic NDE

Rights statement
Awarding institution
  • University of Strathclyde.
Date of award
  • 2021
Thesis identifier
  • T15996
Person Identifier (Local)
  • 201786694
Qualification Level
Qualification Name
Department, School or Faculty
  • Fusion welding is a process commonly employed in High Value Manufacturing (HVM), with the automated welding industry expected to reach $10.8 billion by 2026. The sector is, however, facing a rising gap in the workforce and new, sensor-enabled, intelligent systems are required to address the increased demand. Traditional, manually deployed Non-Destructive Evaluation (NDE) is integral to the welding industry, responsible for ensuring the quality, safety and lifetime of welded joints. The weld NDE sector is also under increasing pressure and has become the bottleneck of the supply chain, due to its poor integration with the manufacturing process and the increased production rates from automation. Future welding HVM operations must react to these challenges, by adopting a holistic approach where the NDE activities are merged with the welding deposition process. Such an approach would fundamentally increase the production quality and reduce the overall costs and lead-time inconsistencies, by providing an early indication for defect formation and enabling their in-process repair. This thesis presents novel research and multiple developments that contribute to the field of automated fusion welding and in-process ultrasonic NDE. A flexible robotic welding and NDE system was underpinned by a novel adaptive real-time control approach, based on sensory input. Ultrasonic thickness measurement has been deployed for the first time in-process, during live arc welding, for on-the-fly welding parameter control. Lastly, the in-process weld penetration screening of thin butt-welded joint in steel plates was achieved through non-contact ultrasonic guided wave testing, performed during the welding deposition.
Advisor / supervisor
  • MacLeod, Charles
  • Pierce, Gareth
Resource Type