Automation Ensures Quality in 3D Printing
3D printing enables the manufacture of customised parts with complex geometrical designs and integrated functionalities. In addition, the production process is completely digitally controlled. Industry 4.0 is striving for batch size 1, which can be realised in principle with this process, says scientist Simina Fulga-Beising from the Fraunhofer Institute for Manufacturing Engineering and Automation IPA. So far, however, additive manufacturing still has a snag: quality. "There are still no strict established standards for the quality assurance", Fulga-Beising criticises. This means that safety and reproducibility cannot be ensured. Furthermore, the lack of quality controls during printing leads to high costs for companies. "The 3D printer works completely independently. In a worst case scenario you might only notice the error once the component is finished, but that would be after the machine had already run for several hours, meaning that materials and energy have been wasted", the researcher criticises. Having a technician supervise the process would be too costly due to the machine’s long running time.
Machine vision is the key technology
With IQ4AP, Fraunhofer IPA has developed a system that automatically checks quality in 3D printing inline. The application is based on a black box with a camera, lighting and ventilation. Machine vision is the key technology. The camera system scans freshly applied powder coatings and sintered layers directly during processing. The images are then examined using several algorithms. "Major and minor defects are detected immediately. Even sintered layer features such as lengths or hole diameters can be measured inline. The result is a part quality protocol at layer level", according to the scientist. Even tolerances can be specified.
The IPA researchers created the prototype of the inline quality control system in 2016 as part of the Application Center Industrie 4.0. The hardware costs the user 2,500 euros, is machine-independent and can be attached to any 3D printer. Theoretically, the module could also be adapted for quality control in the metal field. Fulga-Beising has developed a software and hardware concept for this possibility in her dissertation. IQ4AP is also modular in structure and so can be expanded easily. Fraunhofer IPA is now looking for partners that wish to test this system and integrate it into joint projects as required. "In the next step, using machine learning, the system should evaluate itself what the error entails for the printing process. This doesn’t just mean the machine must decide whether the process should be stopped or not, but it should also draw its own conclusions and optimise the process. This is an important step towards self-controlling production processes", says Fulga-Beising.