About the project
Additive manufacturing enables the fabrication of engineering components with a high degree of geometric complexity. This geometric complexity makes the measurement and inspection of metal AM components very difficult. In this project, You will develop new methods for measuring and inspecting complex AM components.
Metal Additive manufacturing (AM) components suffer from a number of defects, such as:
- high surface roughness
- dimensional deviations
- sub-surface pores
- cracks
AM components must be rigorously checked for defects if they are to enter service in safety-critical applications. Traditional measurement and inspection techniques require line-of-sight access to the features being inspected, so they cannot be used to measure internal features such as cooling channels and lattice structures that can be fabricated easily using metal AM.
The inability to measure and inspect large and complex metal AM components is stifling industrial adoption of metal AM. To address this problem, new measurement and inspection techniques will be developed in this project.
AM defects may be detected during the fabrication process (in-process) and after the fabrication process is complete. Multiple non-destructive testing methods may be combined to provide a holistic picture of the quality of AM components.
Both traditional signal processing and machine learning approaches can be used to identify defect signals, with a view to minimise user influence in the measurement and inspection process.
You should have a keen interest in manufacturing processes, alongside sensors, instrumentation, and signal processing. This project will require processing of multi-dimensional data sets, so programming skills in Python or Matlab are highly desirable.