The vision of the CLASCO project? A universal and digitised laser-based post-processing method for the production of functionalised parts with complex shapes is being developed in the EU-funded CLASCO project. 12 international project partners from science, industry, research and application are united in the CLASCO project. The project is coordinated by the Technical University of Dresden. In our exclusive interview series, the CLASCO project partners introduce themselves in the areas of CLASCO Machine, Optics, Process Monitoring, Sample Preparation through Additive Manufacturing, Artificial Intelligence, Strategy and Business Model as well as Circularity and Sustainability.
Get to know the CLASCO project consortium member in the area of Process Monitoring:
1. What experience do you bring to the CLASCO project, in particular your expertise in developing solutions for real-time monitoring and intelligent control of laser-based industrial processes in an industrial environment?
nLIGHTplasmo has several hundred installations of process monitoring systems in the laser welding, cutting and powder bed fusion 3D printing industry. Our process monitoring systems have real time algorithms for OK and NOK decisions.
2. The consortium partner NIT is also a specialist in the field of sensor technology for process monitoring. What is the difference between your expertise and NIT?
NIT uses a camera-based technology. nLIGHTplasmo uses a sensor-based technology which is typically 200 times faster than the camera-based technologies used by NIT. We have to keep in mind that for the laser structuring process we use ns pulses, so the monitoring speed is important. It is easier to integrate, and we have the possibility for filtering in special wavelengths.
3. What innovative sensor technique is being used in the CLASCO project by your company to assess the quality and homogeneity of the surface structures produced?
We are using a 4 channel 250 kHz sample frequency plasma monitoring sensor. We have our own optical path using specials fibers.
4. How does your sensor improve the overall quality and homogeneity of the surface functionalisation produced by the CLASCO Machine?
We can help to setup an OK process much faster. We can detect irregularities in real time. All the further things we will see during this research project.
Thank you to nLight Plasmo for the information.