The ICT post-master designers program is a two-year salaried program in the field of technological design in Electrical Engineering. The program leads to a Professional Doctorate in Engineering (PDEng) degree.
In this PDEng project, you need to develop novel techniques to ensure that future printers produce consistent high quality prints. In most print-sites the quality is currently still checked manually by operators which costs a lot of time while being prone to errors. Checking frequency is low and many print quality related problems may go unnoticed. Current inline defect inspection methods use special test charts which also limit inspection frequency and represent waste.
The focus of this project will be on inkjet printers and the artifacts that are likely to occur by printing. We would like to detect the print artifacts with a camera in real-time within the printer. We want to detect the artifacts before the print leaves the printer (preferably as soon as possible) and communicate this to the operator and to a decision making unit in the printer.
The aim is to inspect all pages printed and detect if an artifact has occurred by applying deep learning. The learning system will primarily process the images taken by the in-line vision system but can be complemented by the information obtained from other types of sensors as well. The detection has to be fast enough to allow the system to discard the paper containing the artifact into an error location and provide the required status information to restore the required print quality sequence without generating too much waste.