University: University of Strathclyde  Supervisor:  Dr Ehsan Mohseni & Prof Stephen Gareth Pierce  Start date: October 2021


The Wire Additive Manufacturing (WAM) technology offers high deposition rates allowing to manufacture large-scale near-net shape components within shorter lead-times. Therefore, it is of interest to some of the well-known industries in the aerospace and military sectors. the very high sensitivity applications and operations of these industries demand very high integrity of manufactured components. Therefore, similar to any other manufacturing process products, the AM components should go through Non-Destructive Evaluations (NDE) trials used in conventional industrial settings, where a WAM component is transferred to the inspection cell after manufacturing and cooling down for the final inspection which can take from hours to days, depending on the component size and the complexity. In contrast, the robotic delivery of the NDE system during the manufacturing process and in-situ intelligent analysis of the results will provide early intervention opportunities during the material deposition process allowing to correct the potential defects without generating material waste resulting in remediated defects at the point of manufacture, fine-tuned manufacturing parameters, and optimized buy-to-fly ratios. Ultrasound (UT) and eddy current testing (ECT) are both well-established NDT methods that are frequently used in industry, and each offers unique opportunities for inspection of AM components. 

Aims & Objectives 

The aim of this Ph.D. is the novel amalgamation of ECT arrays, with high sensitivity to surface defects, together with the of UT arrays with the strong capability of bulk coverage and sensitivity to subsurface defects to form a Hybrid ECT&UT (HECT&UT) module. Uniting these sensors and incorporating them within an in-process inspection system offers an unparalleled opportunity to not only guarantee the quality of the last deposited AM layer through ECT inspections but also to ensure a thorough sweep of the previous layers and bulk inspection of the component for any potential delayed cracking through the UT inspection. Despite the fact that merging these two systems as one NDE head and having the combined inspection results in one report result in reductions in the time and cost associated with the post-manufacturing inspection and material waste, there are some challenges concerning the sensors integration, robotic delivery and signal fusion and interpretation that needs to be scrutinized through this Ph.D. to form the unified system and to optimize its performance. As presented below, there are three distinct development stages for forming this complex system and there are objectives within each stage to achieve:  

  1. Sensor modifications: ECT and UT sensors selection and design modifications to withstand high temperatures and accommodate to irregularities of the AM as-built surface during the inspection, 
    1. Investigating novel coupling designs of the ECT and UT arrays contact surfaces for enhanced thermal resistance, conformity to WAM as-built surface, and ease of automation. 
    2. Investigating the potential designs to merge the ECT and UT arrays as one NDE head to form the HECT&UT unit. 
  2. Automated robotic deployment: programming data-driven industrial robotic arms to use the force/torque sensor feedback for delivery and smooth manipulation of the sensors on the as-built surface while maintaining the optimum contact force and orientation throughout the inspection, 
    1. High accuracy real-time robotic control based on the positional and contact force data, and interfacing HECT&UT module with the robot for accurate encoding of the NDE data.  
    2. Establishing a robust communication and collaboration platform between the manufacturing and the NDT robots for smart manufacturing, inspection, and repair  
  3. Intelligent sensor data fusion and analysis: designing, training, and testing an intelligent machine-learning algorithm to fuse the bi-sensor data and to automate the defect detection process.
    1. Designing a machine learning algorithm for raw sensor data processing, multi-sensor data fusion, and image forming for the HECT&UT integrated sensor. 
    2. Developing the intelligent defect detection module, and training and testing the algorithm to its success rate in defect detection and sizing using model-based data to save the cost associated with the onerous fabrication of real defective components. 

Supervision & Student development 

The research will be primarily supervised by Dr. E. Mohseni who is a new academic at Centre for Ultrasonic Engineering (CUE) enabling him to leverage his NDE automation knowledge for successful directing of this Ph.D. project. H/She also gains access to a wide range of NDE automation knowledge/equipment/facilities within the research team of prof. S. G. Pierce. The proposed subject, focused on automated NDE of AM, could be relevant to the immediate demand of some of the industries who are a member of NDEvR and readily encompass safety and performance-critical assets (Airbus, BAE Systems, Rolls-Royce, Dstl, EDF). 

In addition to benefiting from the wide range of NDE courses offered by FIND-CDT, the candidate will be also provided with off-campus training courses in robotics to gain knowledge about the robot safety procedures, operation, and programming as well as MATLAB and LabVIEW programming courses to acquire the essential coding and system integration skills. The candidate will be trained to operate the first commercial WAAM cell (RoboWAAM), located at Lightweight Manufacturing Centre (LMC/NMIS), to build WAAM components and examine the performance of the developed automated hybrid NDE system during manufacturing. The candidate will have access to WAAM components of different built strategies and materials through TechnipFMC, Airbus, and WAAM3D Ltd. The student will also have access to the £2.6M state-of-the-art RES hub facilities with several advanced industrial robots and NDT equipment, and the Aerospace Innovation Centre (AIC) established by Spirit AeroSystems at their Prestwick manufacturing facility.  


The project has a strong impact on different sectors through supporting the AM industry/technology, which is at the top of the UK’s high-value manufacturing (HMV) strategic list, by accelerating and enhancing the quality assurance and certification procedures. The proposed research theme has also a strong strategical alignment: ) Internationally, with Key Enabling Technologies in EU, Aerospace, Marine, Defense, transport, and Nuclear sectors, ) Nationally, with EPSRC Delivery plan’s first objective (Delivering economic impact and social prosperity through new investments in projects, networks and fellowships in manufacturing),  RCNDE and NDEvR 5-10-20 years vision (published in 2017 and targeting trend to in-process monitoring for manufacturing processes; inspection of 3D-printed components; high-accuracy robotic NDE for large complex-shaped components), Innovate UK CTAPULT program to increase the competitiveness and value added of UK’s manufacturing industry through HVM, and KTN sustainable and resilient manufacturing goals, and ) Locally, with objectives of the new Research Chair (Prof. S.G. Pierce) in Automated NDT sponsored by the Royal Academy of Engineering and Spirit AeroSystems Ltd, the new Robotically Enabled Sensing (RES) facility at CUE, University of Strathclyde as one of the key robotic NDT centers of excellence in UK academia, Glasgow City Innovation District through collaboration with National Manufacturing Institute of Scotland (NMIS), Lightweight Manufacturing Centre (LMC, where the RoboWAAM  cell is located) and Advanced Manufacturing Centre (AFRC). 


This studentship covers fees at the home/EU rate, a stipend of £16877 per annum and the full technical and professional training programme as part of the FIND CDT.