Em-ReSt: Technological innovation and monitoring for the AM industry
Residual stress and micro cracks that occur during additive manufacturing (AM) process can result in irreversible damage and structural failure of the object after its manufactured. The complex nature of some AM methods means that not all NDT techniques are effective in detecting residual stresses. EM-ReSt functions as add on for the existing AM processes, comprising two sets of NDT techniques: Electromagnetic Acoustic Transducers (EMAT) and Eddy Current Transducers (ECT). A crucial (and novel) extension of the proposed EM-ReSt system is the incorporation of machine learning algorithms applied to the inspection sensors data for for estimation of likelihood of the AM technique to introduce anomalies into the printed structures.
The key objectives of this project are the theoretical and experimental investigation of potential and limitations of EMAT and ECT for monitoring of AM manufacturing process; the development of EMAT and ECT systems specifically designed for online and continuous 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 parameters and potentially the development of preliminary standards in AM.
The proposed EM-ReSt system offers several performance advantages over current AM NDT inspection methods like thermography, X-ray computed tomography (CT scan) or digital radiography. EM-ReSt is fast (ms/measurement and overall scanning time does not exceed a minute), reliable (99% Probability of Detection), 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. In addition, the technology allows for a low profile sensing hardware with potential for EMAT and ECT miniaturization.
Brunel Innovation Centre's Role
For EM-ReSt project, Brunel Innovation Centre (BIC) will develop a Machine Learning (ML) algorithm for real-time residual stress estimation from signals obtained from ultrasonic EMAT (and (EC data. For EM-ReSt, BIC is leading the FEA modelling work for EMAT wave propagation and sensitivity to defects and stress concentration. BIC is mainly leading the development and the training of an ML-driven Automated Defect Recognition System that will assess the integrity of the AM part based and provide a decision whether the built component is acceptable or would it require remanufacturing.
- Ether NDE
- Hybrid Manufacturing Technologies
- Brunel University London
Meet the Principal Investigator(s) for the project
Professor Tat-Hean Gan - Professional Qualifications CEng. IntPE (UK), Eur Ing BEng (Hons) Electrical and Electronics Engg (Uni of Nottingham) MSc in Advanced Mechanical Engineering (University of Warwick) MBA in International Business (University of Birmingham) PhD in Engineering (University of Warwick) Languages English, Malaysian, Mandarin, Cantonese Professional Bodies Fellow of the British Institute of NDT Fellow of the Institute of Engineering and Technology Tat-Hean Gan has 10 years of experience in Non-Destructive Testing (NDT), Structural Health Monitoring (SHM) and Condition Monitoring of rotating machineries in various industries namely nuclear, renewable energy (eg Wind, Wave ad Tidal), Oil and Gas, Petrochemical, Construction and Infrastructure, Aerospace and Automotive. He is the Director of BIC, leading activities varying from Research and development to commercialisation in the areas of novel technique development, sensor applications, signal and image processing, numerical modelling and electronics hardware. His experience is also in Collaborative funding (EC FP7 and UK TSB), project management and technology commercialisation.
Related Research Group(s)
Brunel Innovation Centre - A world-class research and technology centre that sits between the knowledge base and industry.
Partnering with confidence
Organisations interested in our research can partner with us with confidence backed by an external and independent benchmark: The Knowledge Exchange Framework. Read more.
Project last modified 26/02/2021