## 3 Related Literature  
While destructive testing methods, such as the Erichsen cupping test, often provide the most detailed quality insights for general welding practices, non-destructive testing is more interesting for continuous quality control in a production setting. Non-destructive methods include visual checks performed by an operator and automated checks using measurements from specialized sensors installed in or following the welding machine. [PERSON] et al. present a review on the advantages and limitations of various weld quality monitoring techniques for spot welding [2]. [PERSON] and [PERSON] provide another overview of quality monitoring techniques and also discuss the wide range of possible causes of bad welds [3]. Interestingly, they question the general applicability of many techniques due to the lack of physical support and limitations related to actual production environments. While both works focus on spot welding, findings may be relevant for mash seam welding as well, as resistance seam welding can be seen as a series of (overlapping) spot welds [4], and mash seam welding is a type of resistance seam welding where the overlap between welded sheets is minimal [5]. Despite this wide range of available techniques, most works describing methods applied in the steel production industry use quality estimation techniques involving measurements directly linked to the welding machine, such as temperature, pressure or welding current. This can be somewhat explained by the restrictions of the continuous production process. For example, radiography or ultrasound approaches require specialized and expensive installations that are difficult to include in a production line [6]. The use of acoustic methods may be unreliable in the noisy factory setting, and other techniques may be affected by the high electromagnetic influence of the welding machine [2]. We found eight works focusing specifically on anomaly detection for mash seam welds in continuous production lines in steel mills. One work uses a continuous waveform transform to convert a one-dimensional Eddy current signal to a two-dimensional image to be processed by a recurrent neural network for the detection and classification of poor quality welds [7]. This approach was later extended to also include 3D laser scanning features [8]. The third work describes the use of an ultrasonic probe with an integrated water chamber that is passed over new welds [9]. The resulting measurements are used to estimate the thickness of the weld; a thickness threshold then classifies the weld as healthy or faulty. Unfortunately, no evaluation or details regarding the thickness estimation are provided. A fourth work uses acoustics for mash seam welding for galvanizing lines but only evaluates a prototype in a lab setting [10]. The remaining four publications are most related to this work, as they utilize welding machine measurements. All were developed at the ArcelorMittal site in Asturias (Spain) and describe expectation-based models that utilize one or more welding measurements to find anomalous welds. In the first work, [PERSON] et al. state that temperature and current are the most significant signals to detect poor welds, and that average values per weld suffice [11]. They suggest a system where the average temperature and current of each weld are compared against historical welds of similar welding programs. A score is calculated for both measurements using the outer 1 and 5 percentile values as a reference. These scores are aggregated and used to classify each weld as healthy or defective. The second work extends this approach by including a pre-filtering check to operating errors in setup voltage, speed and pressure using historical data [6]. Following this, weld temperature values are assigned an assessment score (based on the most recent welds in the same welding program), sorted, and the 10-percentile value is used as a score. This approach comes down to checking how many individual measured values fall below or above a threshold defined by historical data.