In this work, a dissimilar copper/aluminum lap joint was generated by force-controlled hybrid friction diffusion bonding setup (HFDB). During the welding process, the appearing torque, the welding force as well as the plunge depth are recorded over time. Due to the force-controlled process, tool wear and the use of different materials, the resulting data series varies significantly, which makes quality assurance according to classical methods very difficult. Therefore, a Convolutional Neural Network was developed which allows the evaluation of the recorded process data. In this study, data from sound welds as well as data from samples with weld defects were considered. In addition to the different welding qualities, deviations from the ideal conditions due to tool wear and the use of different alloys were also considered. The validity of the developed approach is determined by cross validation during the training process and different amounts of training data. With an accuracy of 88.5%, the approach of using Convolutional Neural Network has proven to be a suitable tool for monitoring the processes.

No abstract

No abstract

No abstract

Monitoring installation process and utilisation of glass and hybrid components in structural glass has become increasingly important. However, to date, there are no generally accepted standard procedures to evaluate the in‐situ stress condition of a built‐in glass. With the aid of photoelasticity a qualitative statement of the stress state in the glass can be made. For this reason, a measurement method and a functional model for a mobile device are being developed with which it is possible to measure in‐situ and identify stress peaks, for example, due to incorrect assembly of a glass fitting. By supporting finite element method (FEM) simulations it is possible to transfer the qualitative results of photoelastic measurements into quantitative results.