Students working in a group (c) Jonas Kron


Validation of innovative process monitoring systems in additive manufacturing

Short Facts

  • Duration: 01 Jan. 2022 to 31 July 2023
  • Funded by: Bavarian State Ministry of Economic Affairs, Regional Development, and Energy


This image shows opto-thermal process monitoring during an LPBF process.

The background of the ViPaF project is to research new technologies that can help improve the process and quality monitoring in the additive manufacturing of components. The core element is to measure the absolute process temperatures and temperature gradients in the manufactured component via an adaptive sensor system. Furthermore, the method of modulated measuring of thermal conductivity for the on-site quality assurance and validation of material properties is to be established. Based on the information collected (absolute process temperature, thermal conductivity), a failure prediction via a simulation-based neuronal network is implemented.  

Project goals and contents

Goal of the project is the validation of a new and improved technology for the on-site monitoring of processes and quality in the field of additive manufacturing, in particular for the process of Laser Powder Bed Fusion (LPBF). The aim is to draw conclusions about the temperature, temperature distribution and heat flow of the manufactured components by recording, processing and evaluating various optically generated sensor data. This will allow for more reliable statements about process and component quality. Core element of this innovative approach is measuring the absolute process temperatures and temperature gradients in the manufactured component based on an adaptive sensor array for contact-less temperature measuring which is separated from the processing laser. This is to avoid the critical areas of the melting process to allow a more accurate investigation of the cooling and solidification process of the processed metallic material. In the ideal case, optimisation strategies which enable process adjustments during manufacturing are to be developed on site. For the purposes of evaluation and analysis, a neural network is being trained and used which shall provide robust and reliable predictions through simulation-based reference values. Through developing and implementing improved methods for optical process and quality monitoring, the reliability and stability of process sequences can be increased significantly, and the time for a subsequent inspection of manufactured components is reduced. Due to the higher reliability of the method as well as statements about the component quality without downstream destructive testing, costs can be reduced, which will make the LPBF method more attractive for small and medium-sized metalworking companies in the future.

Project contact(s)

Dennis Höfflin, M.Sc.

Christian Sauer, M.Eng.