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Funding body

Innovate UK


Principal investigator: Professor Tat-Hean Gan

Project description

Residual stresses and micro-cracks that occur during the additive manufacturing (AM) process can result in irreversible damage and structural failure of the object after its manufacturing. The nature of some AM methods means that not all Non-Destructive Testing (NDT) techniques are effective in detecting residual stresses. Thermography, X-ray computed tomography (CT scan), or digital radiography are limited by the resolution of images, they are bulky and costly (up to £100k), are not suited to crack detection. Our solution, Em-ReSt, functions as an add-on robotic arm to existing AM processes, comprising two sets of ultrasonic transducers: Electromagnetic Acoustic Transducers (EMAT) and Eddy Current Transducers. Big data collection and analysis will be performed and blended with statistical, machine learning and big data analysis for the estimation of the likelihood of AM techniques to introduce anomalies into the printed structures before the beginning of the manufacturing. A digital system that will estimate the potential and deficiencies of any AM technique for given structures will be developed and utilised for the establishment of a preliminary set of AM standards. Hence, more robust and reliable components will be printed and used.

EM-ReSt is fast (msecs/measurement and overall scanning time does not exceed a minute), reliable (99% PoD), non-destructive online monitoring of AM techniques, can achieve 15% reduction of faulty outputs with the use of 10 times more cost-effective monitoring system, has low profile sensing hardware with potential for EMAT and EC miniaturization. The key objectives of this project are the theoretical and experimental investigation of potential and limitations of UT and ECT for monitoring of AM manufacturing process; Development of EMAT and ECT systems specifically designed for online and continuous UT and ECT of AM structures; integration of the two systems together in AM machines; and Machine Learning and Big Data analysis of monitoring data for the optimisation of AM procedures and potentially the development of preliminary standards in AM.

This project represents a clear technological innovation for the UK AM industry, and major growth opportunity for the SME supply chain consortium, which is forecast to generate revenues of £72.5M and 362 new jobs 5 years post commercialisation. The project is led by the consortium: ETher NDE, Sonemat, XcaLe3D, Brunel University London, and TWI.