# Data Attributes in Quality Monitoring of Manufacturing Processes: The Welding Case
## 3 Case Studies  
### Case Study I  
The electrical contact resistance (ECR) of joints during the assembly of battery packs is a critical factor for accepting or rejecting battery packs or modules [44]. Postprocess inspection of ECR, in general, is something that poses a lot of challenges, as adding a new step in production [45] is not feasible due to accessibility issues; the latter are different among product variants, depending on battery pack architectures. The approach herein aims to establish a connection between the ECR level of joints and the infrared (IR) images captured during the welding of battery tabs in the context of a supervised machine-learning problem (Figure 2). For that, a fiber laser equipped with an off-axis infrared high-speed IR camera was used to monitor the welding of Al-Cu overlapped battery tabs (as of 31 December 2021, the ZELD-e EU project, [[http://zeld-e.eu/](http://zeld-e.eu/)]([http://zeld-e.eu/](http://zeld-e.eu/)) (accessed on 15 September 2023)). \begin{table}
\begin{tabular}{l l} \hline \hline  & **Feature** & **Factors** \\ \hline Volume & 1. Process duration \\  & 2. Resolution of camera \\  & 3. Sampling rate of the camera \\ Velocity & Depending on the speed of the process monitoring; how many \\  & single frames per second (also on how many different files need \\  & handling)? \\ Variety & This has to do with the fact that the monitoring system may not \\  & rely only on one camera (single spectrum, or single angle) but on \\  & multispectral and stereo vision, possibly with different sampling \\  & rates. \\ Variability & This can be related to potential reuses of the system in different \\  & welding configurations or the use of the data in multiple control \\  & loops with different sampling rates. \\  & Viscosity \\  & This depends on the combination of material and camera \\  & sensitivity, the thermal drift of the camera, and all the features \\  & that favor the introduction of noise, both from the camera itself (i.e., lambertian source) and the environment (background \\ Visualization/Verification & At a minimum, this is related to the ease of extracting features \\  & that can offer a good correlation with the ultimate goal of the \\  & application. \\ Value & Among technical factors, such as monitoring performance, \\  & business-wise, it could also be linked to explainable machine \\  & learning in the case of human-centric manufacturing [43]. \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The 7 Vs for the case of welding quality monitoring. The data acquisition included three different power levels (1.2, 1.4, and 1.6 kW) using a fiber laser with a 0.15-mm spot size and a constant welding speed of 12 m/min for the welding of 270 Al-Cu overlap tab-pairs with dimensions 45 \(\times\) 45 mm\({}^{2}\), 0.4 mm and 45 \(\times\) 45 mm\({}^{2}\), 0.2 mm. The resulting seams were 40 mm in length and inspected as per their electrical resistance across the Al-Cu tabs. Each video recorded during the welding of the tab pair was matched with the corresponding ECR value measured. The baseline supervised approach for developing a model able to classify the electrical quality included a list of actions as described in the following points:  
(1) ECR threshold definition:  
An industrial empirical threshold rule was applied to the measured values of the ECR, as would normally be implemented for statistical process control. The threshold was defined both by taking into consideration the production requirements of the statistical distributions and the measured values. (2) Data cleaning:  
Data entries were rejected either due to problems during image acquisition or due to problems during the measurement of the resistance (measuring equipment was affected by external E/M fields). (3) Data preprocessing  
The high gain setting used for capturing the IR emissions of the Al tab during welding introduced a lot of thermal noise, making it difficult to easily identify the thermal signature. Thus, noise reduction was incorporated into this study to remove the background noise.