# Data Attributes in Quality Monitoring of Manufacturing Processes: The Welding Case
## 3 Case Studies
### Case Study II  
In resistance spot welding (RSW), the quality of the welds is affected by many variables [48], including the properties of the materials used, the smoothness and purity of their surface, the size and shape of the electrodes, and the parameters of the welding machine. Expulsion is a very common defect during RSW [49], and in extreme situations, it can lead to nugget thinning that may compromise the mechanical properties of the joint and the aesthetic appearance of the part's surface [50]. Expulsion is typically accompanied by sparks during the process caused by high welding current, but it can also be due to electrode misalignment and thus not easily identifiable. Typically, in the industry, postprocess inspection is carried out to determine the quality of the weld; however, it is sample-based, and when it includes destructive techniques, it definitely cannot be applied to products that will reach the market [51]. For that matter, in-process methods and systems have been implemented for assessing the quality aspects of a joint. As with the previous case study, the majority of them aim to establish a relationship between process emissions and joints' qualities. Herein, a system based on the same monitoring hardware (Figure 3) is used to record the resistance spot welding of 202 galvanized steel-316 overlapped coupon with dimensions 200 \(\times\) 25 mm\({}^{2}\), 1 mm thickness. To weld these coupons, six different welding sets of parameters were used, where the welding current and duration were varied while the clamping force was constant. These sets were the following: 15A-10 cycles, 15A-5 cycles, 10A-15 cycles, 15A-5 cycles, 18A-5 cycles, and 8A-10 cycles. \begin{table}
\begin{tabular}{l l} \hline \hline
**Action** & **Description** \\ \hline (Removing) Data preprocessing steps & Remove noise-cancellation as already described in the previous step. \\  & Leave entries that correspond to faulty ECR \\ (Including) Ill entries & measurements. \\ (Removing) Normalization & Do not scale the features. \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Case study I—factors that compromise data veracity. A label was assigned to each video to indicate if expulsion occurred after visually inspecting the welded samples. Once again, the current approach aims to classify the IR image data according to the expulsion binary label by reducing the problem into a supervised one. Based on the previous case study, it is obvious that data veracity indeed can affect the performance of a data-driven application, however, the intensity of its effect is strictly related to the nature of the data and the value that is exploited from them and eventually ends up to each model. With that in mind, in this case study, another factor is inserted, the Value of the data for its effect to be assessed in the context of a real-world application. A series of steps described in the following bullet points are used sequentially to construct a single approach as described in the following table. 1. Data pre-processing:  
As in this application, the recording time was significantly longer (including the cool-down phase), apart from the noise, thermal drift was also an issue that should have been faced. Two steps were applied, one for correcting the thermal drift and one for the noise cancellation. Apart from these, due to reasons that were made clear in subsequent steps (feature extraction), image stabilization was implemented to remove any vibrations transferred to the monitoring system during the process that were throwing off-center the thermal signature. 2. Feature extraction:  
With all the videos having the same dimensions, each one was flattened and appended to a single array. Then, principal components analysis (PCA) [51] was utilized as a linear, component-based transformation to extract a new set of feature vectors from the initial dataset (principal components--PC). Their dimensions defined the captured variance from the initial data. 3.