Predicting the core temperature during welding is an ambitious aim in many research works. In this work, a 3D-scanner with integrated pyrometer is characterized and used to measure the temperature during quasi-simultaneous laser transmission welding of polyamide 6. However, due to welding in an overlap configuration, the heat radiation emitted from the joining zone of a laser transmission weld has to pass through the upper polymer, which is itself a semi-transparent emitter. Therefore, the spectral filtering of the heat radiation in the upper polymer is taken into account by calibrating the pyrometer for the measurement task. Thermal process simulations are performed to compare the temperature field with the measured temperature signal. The absorption coefficients of the polymers are measured, in order to get precise results from the computation. The temperature signals during welding are in good agreement with the computed mean temperature inside the detection spot, located in the joining area. This is also true for varying laser power, laser beam diameter and the carbon black content in the lower polymer. Both, the computed mean temperature and the temperature signal are representing the core temperature. In order to evaluate the spatial sensitivity of the measurement system, the emitted heat radiation from both polymers is calculated on basis of the computed temperature field. Hereby it is found, that more than 90 percent of the detected heat radiation comes from the joining area, which is a crucial information for contact-free temperature measurement tasks on semi-transparent polymers.

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Multi-fidelity optimization, which complements an expensive high-fidelity function with cheaper low-fidelity functions, has been successfully applied in many fields of structural optimization. In the present work, an exemplary cross-die deep-drawing optimization problem is investigated to compare different objective functions and to assess the performance of a multi-fidelity efficient global optimization technique. To that end, hierarchical kriging is combined with an infill criterion called variable-fidelity expected improvement. Findings depend significantly on the choice of objective function, highlighting the importance of careful consideration when defining an objective function. We show that one function based on the share of bad elements in a forming limit diagram is not well suited to optimize the example problem. In contrast, two other definitions of objective functions, the average sheet thickness reduction and an averaged limit violation in the forming limit diagram, confirm the potential of a multi-fidelity approach. They significantly reduce computational cost at comparable result quality or even improve result quality compared to a single-fidelity optimization.