Additive manufacturing, in particular the powder bed fusion of metals using a laser beam, has a wide range of possible technical applications. Especially for safety-critical applications, a quality assurance of the components is indispensable. However, time-consuming and costly quality assurance measures, such as computer tomography, represent a barrier for further industrial spreading. For this reason, alternative methods for process anomaly detection using process monitoring systems have been developed. However, the defect detection quality of current methods is limited, as single monitoring systems only detect specific process anomalies. Therefore, a new methodology to evaluate the data of multiple monitoring systems is derived using sensor data fusion. Focus was placed on the causes and the appearance of defects in different monitoring systems (photodiodes, on- and off-axis high-speed cameras, and thermography). Based on this, indicators representing characteristics of the process were developed to reduce the data. Finally, deterministic models for the data fusion within a monitoring system and between the monitoring systems were developed. The result was a defect detection of up to 92% of the melt track defects. The methodology was thus able to determine process anomalies and to evaluate the suitability of a specific process monitoring system for the defect detection.

The interpretation of sensor system data is critical for monitoring industrial welding processes and providing reliable information about the condition of the weld seam. Previous investigations have shown that acoustic emissions of frequencies up to several kilohertz during laser beam welding are parameter-dependent and contain valuable information about the process. A microphone was employed to record the acoustic emissions produced when performing deep penetration laser beam welding of copper. Experiments were conducted in which the laser power and the feed rate were varied so as to obtain acoustic data comprising frequencies of up to 1 MHz. The signals were preprocessed and features were extracted using Fourier and wavelet analysis as well as speech analysis techniques. The relationship between the features extracted from the acoustic signal and the weld depth was modeled using Gaussian process regression. The results showed that acoustic emissions during laser beam welding can be used to predict the weld depth without having to rely on process parameters, i.e., the laser power and the feed rate. Overall, 17 features were extracted from acoustic signals, with the zero-crossing rate displaying the highest significance for determining the weld depth. These investigations open up new possibilities of robust quality assurance for laser beam welding applications based on acoustic emissions.

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.

The mobility sector is considered a major contributor to global greenhouse gas emissions and air pollution. As a result, many countries have initiated the transition from fossil fuel-powered to electrified powertrains. This transformation of the powertrain concept will lead to a rapid increase in the production of electric vehicles and, therefore, to a high demand for so-called traction batteries. As a production step of the traction batteries, a connection between the cell connector and the terminal of the battery cell has to be manufactured. For this purpose, laser beam welding is a reliable and efficient joining technique. In order to ensure continuous quality of the welding process during production and to detect defects in real time, reliable process monitoring is required. In this study, spectral and acoustic emissions during laser beam welding were recorded using a laser welding monitor and an optical microphone. For determining possible correlations between the signals and weld defects, various failure cases were generated by the systematic placement of disturbance elements. These elements included a contaminated surface, a gap between the cell connector and the battery cell, and a misalignment of the cell connector. Based on the recorded signals, statistical metrics were calculated. Finally, weld seams with and without defects were compared to assess the capability of both sensor systems for detecting the weld defects.