Additive Manufacturing Researchers want to make 3D printing ready for broad scale series production
A research project conducted by the Laboratory for Machine Tools and Production Engineering (WZL) of RWTH Aachen University investigates the use of sensors and artificial intelligence for model-based control in 3D printing. The aim of the project is to improve the quality of 3D printed series production.
Additive manufacturing (AM) processes are characterised by their flexibility and possibilities for individual production. By reducing the batch size, thanks to demand-oriented production and the character of additive manufacturing, resources in the form of material, energy and time can be saved along the entire product life cycle, making a significant contribution to sustainability in production. For these reasons, AM is used especially in lightweight construction and custom manufacturing. However, AM technologies are not yet ready for series production on a broad scale, which is due to the fact that 3D printing technologies often do not yet permit reproducible and controllable production of high-quality components and thus require a high level of effort for the qualification of the produced components.
Against this background, the DFG-funded research project “Smopa3D — Sensor-supported model-based parameterisation of 3D printing processes”, the second phase of which starts in November 2021, is investigating how the quality of the print can be evaluated in the process by integrating laser photoelectric sensors and how this knowledge can be used for real-time model-predictive control. This will enable the printing processes to guarantee quality despite sudden disturbances or unsuitable parameterisation and to avoid aborted print jobs.
The project, which is being carried out at the Chair of Production Metrology and Quality Management of the RWTH Aachen, headed by Prof. Robert Schmitt, continues the first phase of the project, in which an automatic defect detection system was implemented with the help of laser photoelectric sensors. This measuring system scans the individual component layers with a resolution of 50 µm and forms a digital model of the component's condition. By comparing the model with the target model, deviations can be detected which may lead to a reduced quality of the component. Using machine learning methods, the project team was able to show that it is possible to predict quality-relevant characteristics of the final component.
Building on these findings, a real-time capable process control system is to be developed and implemented in the next funding period. For this purpose, deviations that occur should not only be detected, but also categorised according to quality and type. Subsequently, quality-relevant characteristics of successive layers will be estimated on the basis of this data and the control parameters of the printer, in order to be able to predict serious defects that lead to reduced component quality or print failure. This knowledge is to be used for the implementation of a process control, which provides for a dynamic correction of the machine code or the control parameters. This enables automatic optimisation of the printer during printing.
“We hope that the data-driven control of 3D printing processes will lead to greater acceptance of the industrial use of these technologies and thus to more resource-efficient production through material savings and the avoidance of overproduction,” says Jonas Großeheide, research associate at the Chair of Production Metrology and Quality Management at the Laboratory for Machine Tools and Production Engineering WZL at RWTH Aachen University, about the project vision. Over the next two years of the project, the two-person team consisting of Hanna Brings and Jonas Großeheide expects to successfully implement a real-time capable control system on an FDM printer.