University: University of Manchester Supervisors: Dr Wuliang Yin and Professor Anthony Peyton
Start date: October 2021
Additive Manufacturing (AM) of metal components has rapidly come to prominence as a valid and convenient alternative to other production techniques, due to its advantages in terms of lead-time reduction, design flexibility and capability, and reduced manufacturing waste. All metal AM techniques require a heat source and feed materials to build parts and the way the material is delivered can be a pre-placed bed or direct feed. Each of the metal AM technologies has specific advantages due to its particular properties. In particular, the WAAM (wire + arc additive manufacturing) technique has a high deposition rate (e.g. many kilograms per hour) with no limitation to components’ size. It has shown the capability to make large titanium and aluminium aero-structures in a timely manner (weeks instead of months) and with much reduced cost (up to 70% cheaper than machining from solid), resulting in a tremendous industry pull.
However, additive manufacturing such components is challenging; a particular difficulty is ensuring part quality and consistency, which has been referred to as the Achilles’ heel of AM. For example, parts produced with wires from different providers can vary significantly. Conversely, the potential of this technology is enormous if quality/consistency is improved to cure the Achilles’ heel.
A critical step to dealing with this problem is the development of in-process sensing capability and link the sensing information to build quality control. Online quality control can significantly increase the robustness of the process by enabling the quality check of the building process at the earliest possible stage, such that early corrective actions can be taken during the process. This is in contrast with a posteriori quality control which does not allow taking early corrective measures if the quality of the part / process deviates from the desired.
Aims and Objectives
The aim of this project is to build on our experience and expertise in electromagnetic (EM) sensor system design, EM computation and imaging, this project aims to realise new real-time in-process NDT/E sensing systems and related inspection protocol for improving the quality control of AM processes. The sensor system and inspection protocol intend to inspect/monitor AM processes at each inter-pass stage and identify defects from sensor outputs in real time.
At the same time, there are particular microstructure features and defects associated with AM technology which compromise the reliability of the process and the quality of the final built parts. For example, due to the repeated layer-upon-layer melting and rapid solidification of the metal AM process, parts experience a complex thermal history involving directional heat transfer.
A particular focus of this project is to investigate high temperature EM sensor arrays that can operate at high temperature and follow close behind the weld pool for wire arc additive manufacturing or be integrated within the rake for powder bed AM processes.
Supervision and Student Development
The research will be primarily supervised by Prof Tony Peyton and Dr. Wuliang Yin a Reader in the Electromagnetic Sensing Group in the Department of Electrical Engineering and Electronics. The student will gain access to a wide range of equipment/facilities within the research team. The team have a long-standing relationship with the metal production industry, which has resulted in the EMspec technology, which is now licenced to Primetals Technologies. Presently, the EMspec technology is deployed on the hot strip mill of Tata Steel in The Netherlands, and forms the background to aspects of this project. Consequently, UoM is well placed to further the electromagnetic inspection research.
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 modelling, simulation tool, and programming as well as MATLAB and LabVIEW programming courses to acquire the essential coding and system integration skills.
The student will also benefit from association with the UK Research Centre in NDE (RCNDE) network, which provides further industry links and wider academic pool of collaborators and research opportunities (workshops, training, supplemental funding, etc).
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.