Preliminary studies have shown the superiority of convolutional neural networks (CNNs) compared to other network architectures for determining the surface quality of friction stir welds. In this paper, CNNs were employed to detect cavities inside friction stir welds by evaluating inline measured process data. The aim was to determine whether CNNs are suitable for identifying surface defects exclusively, or if the approach is transferable to internal weld defects. For this purpose, 120 welds were produced and examined by ultrasonic testing, which was the basis for labeling the data as “good” or “defective.” Different types of artificial neural network were tested for predicting the placement of the welds into the defined classes. It was found that the way of labeling the data is significant for the accuracy achievable. When the complete welds were uniformly labeled as “good” or “defective,” an accuracy of 98.5% was achieved by a CNN, which was a significant improvement compared to the state of the art. When the welds were labeled segment-wise, an accuracy of 79.2% was obtained by using a CNN, showing that a segment-wise prediction of the cavities is also possible. The results confirm that CNNs are well suited for process monitoring in friction stir welding and their application enables the identification of various defect types.

Friction stir welding (FSW) is an innovative solid-state welding technology that produces high quality joints and is widely used in the aerospace industry. Previous studies have revealed welding temperature to be a decisive factor for joint quality. Consequently, several temperature control systems for FSW have been developed. These output feedback control systems usually require delicate and expensive temperature measuring equipment, which reduces their suitability for industrial practice. This paper presents a novel state feedback system of the welding temperature to remedy this shortcoming. The system uses a physical model of the FSW process (digital twin) for the determination of the welding temperature signal from the process torque signal. The digital twin is based on a multi-input nonlinear time invariant model, which is fed with the torque signal from the spindle motor. A model-based ℒ₁ adaptive controller was employed for its robustness with respect to model inaccuracies and fast adaption to fluctuations in the controlled system. The experimental validation of the feedback control system showed improved weld quality compared to welded joints produced without temperature control. The achieved control accuracies depended on the results of the temperature calculation. Control deviations of less than 10 K could be achieved for certain welding parameters, and even for a work piece geometry, which deliberately caused heat accumulation.

Water-based processing of electrodes is associated with an enhanced environmental footprint for lithium-ion battery (LIB) production in conjunction with reduced costs. This trend is accompanied by an increasing demand for electrode dispersion processing in inkjet printing. However, most of the dispersion components show a low inherent dispersibility with poor stability in aqueous formulations. This is particularly important when it comes to qualifying electrode dispersions for use in inkjet printing, since the effect of agglomeration and sedimentation effects must be effectively prevented. Therefore, additives are needed to improve the dispersive behavior. This paper analyzes the suitability of dispersants for organic electrode components, in particular graphite and carbon black. An empirical approach was devised on the basis of comprehensive theoretical considerations. Empirical investigations revealed that the utilization of polyvinylpyrrolidone (PVP) favored the enhanced stabilization of graphite nanoparticles. The addition of Triton X-100 (TX-100) resulted in an improved stabilization of carbon black. Based on these empirical studies, a methodology was derived, which supports the application of suitable dispersants in printable dispersions.

Lithium-ion battery cells are used for energy storage in many industrial sectors, such as consumer electronics or electromobility. Due to the diversity of these applications, the demand for tailored battery cells is increasing. Consequently, the technical development of the cells leads to numerous coexisting cell variants. Examples of such variants are altering cell materials, formats, and capacities. Different target capacities can be realized by changing the geometrical dimensions of the individual electrodes or the number of electrodes in the cell cores. The increasing quantity of variants poses challenges within battery cell manufacturing, such as the need to adapt the process parameters for the cell-internal contacting of a higher number of electrode sheets in the cell stack. Each new cell variant currently requires elaborate experimental parameter studies for its manufacture. An approach for selecting suitable process parameters for laser-based cell-internal contacting in terms of a modification of the cell properties is presented in this paper. A model was built to determine the weld depth in copper sheets using a millisecond pulsed laser welding strategy. The process parameters for welding stacks of electrode sheets to an arrester tab were calculated on the basis of this model. The necessary weld depth in the arrester tab for achieving suitable mechanical properties of the cell-internal joint was considered. The presented approach was validated by welding different numbers of foils to an arrester tab and varying the thickness of the foils. It was shown that the experimental effort for the selection of the process parameters for laser-based contacting can be reduced significantly.