Quantitative in-process temperature measurement in the laser metal deposition process by means of multispectral emissivity prediction

• Applicants: Dr. Simon Altenburg; Professor Dr.-Ing. Andreas Kroll
• Subject Area: Measurement Systems ; Primary Shaping and Reshaping Technology, Additive Manufacturing
• Term: since 2023
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 516965606

In the additive manufacturing of metals, the solidification temperature intervals, phase transformation temperatures, thermal gradients as well as solidification and cooling rates are the central variables which determine the microstructural characteristics, internal stresses and possible cracks of the workpiece after the process. For the vision of a future, possibly graduated, targeted adjustment of the mechanical and surface properties of additively manufactured components, it is therefore necessary to have precise knowledge of the relationships between the manipulated variables of the manufacturing process as well as the component geometry and the temperature fields during manufacturing. Therefore, a quantitatively traceable in-process measurement of surface temperature fields is required for the validation of models and simulations of the processes with respect to the above-mentioned properties. In particular, the simultaneous temperature determination of the meltpool and the solid body also at temperatures significantly below the solidification temperature is considered unsolved and shall be addressed within the project. To this end, two camera-based measurement techniques for determining actual temperature developments in the process despite varying emissivity are to be investigated, compared and finally fused using the example of laser metal deposition of the material AISI 316L. In the part of the first project partner, the method of temperature emissivity separation based on multispectral thermography in the midwave infrared spectral range is used. For this purpose, suitable parameterized analytical spectral emissivity functions have to be developed for the different material states, which describe the actual spectral emissivity curve sufficiently well with a few degrees of freedom. In addition, in order to be able to apply these in the different image areas for temperature-emissivity separation, a classification of the image areas according to material state must be carried out. In the part of the second project partner, the data of a visual RGB camera (including external illumination) will first be pre-processed in terms of disturbance reduction and then segmented regions of respectively mathematically describable emissivity curves and mapped to emissivity using data-driven learned prediction. The obtained emissivity information is fused with the images from a broadband midwave infrared camera with a wide temperature measurement range for quantitative temperature measurement.

• DFG Programme: Research Grants