## 4 Discussion and Conclusions  
The developed model used computer vision techniques and image processing methodologies to not only detect defects in the printed resistance lines, but also to measure the quality of the final products compared to a gold standard. Nonetheless, as the complexity of the defects increases, the need for more general and robust techniques such as ML is felt [15]. As a result, due to the limited image sample availability (59 sample images including 2478 sub-images) as well as the imbalanced defect type occurrence frequency, a machine learning approach could not be implemented. Thus, a solution based on image processing techniques was applied. There were several obstacles in detecting such disconnections including the inability to use machine learning models due to the very limited dataset as well as the different types of failures in terms of their shapes. A universal disconnection detection method had to be applied to go through every line, both vertical and horizontal, and check whether the lines had a defect. Another issue was the sheer amount of noise, including dust, particles, and hairs, that made the detection very difficult as some of the resistances' line thicknesses were similar or even smaller than the dust or hair sizes. This study aimed to design and implement an in-line monitoring methodology and an innovative manufacturing process to detect defects in flexible conductive materials. Due to the limited dataset and sample data, this study aimed to explore the possibility of using NDT methodologies based on computer vision and image processing approaches for the in-line quality assessment of different steps in processing for the procurement of thin-film photovoltaics. A multistage ADR methodology was proposed to detect and classify both printing-induced defects and imperfections as well as the misalignment of the printed \begin{table}
\begin{tabular}{l l} \hline image name: Sample 075 & 20 sub-image name: CH\_WO\_08L264 quality \%: 90.44 \\ timestamp: 2022-05-02 20:27:26.365079 & health condition: Defective \\ \hline
0 sub-image name: AH\_WO\_08L232\_9 quality \%: 90.45 & 21 sub-image name: CH\_WO\_1L264 quality \%: 96.28 \\ health condition: Defective & health condition: Healthy \\ \hline
1 sub-image name: AH\_WO\_1L232\_9 quality \%: 99.0 & 22 sub-image name: CH\_WO\_3L264 quality \%: 98.26 \\ health condition: Defective & health condition: Healthy \\ \hline
2 sub-image name: AH\_WO\_3L232\_9 quality \%: 99.19 & 23 sub-image name: CH\_WO\_5L264 quality \%: 99.61 \\ health condition: Defective & health condition: Healthy \\ \hline
3 sub-image name: AH\_WO\_5L232\_9 quality \%: 98.19 & 24 sub-image name: CH\_W1L264 quality \%: 96.47 \\ health condition: Healthy & health condition: Healthy \\ \hline
4 sub-image name: AH\_W1L232\_9 quality \%: 98.62 & 25 sub-image name: CV\_WO\_08L264 quality \%: 98.13 \\ health condition: Healthy & health condition: Defective \\ \hline
5 sub-image name: AV\_WO\_08L232\_9 quality \%: 99.85 & 26 sub-image name: CV\_WO\_1L264 quality \%: 98.89 \\ health condition: Defective & health condition: Healthy \\ \hline \end{tabular}
\end{table} 
Table 1: Results of the output of the ADR system. conductors with respect to the reference design. The method proved to be very reliable (98% accuracy) and can be used independently or in conjunction with electrical testing methods for quality assurance purposes during the production of R2R prints. For the image processing framework, the algorithms could be modified for new printed flexible circuits. This is achievable with a limited dataset as it relies on image processing algorithms but requires more tailoring of the algorithms, which corresponds to more development time. Conceptualization, A.A.; methodology, A.A.; software, A.A.; validation, A.A.; formal analysis, A.A.; investigation, A.A.; resources, A.A.; data curation, A.A.; writing--original draft preparation, A.A. and T.-H.G. ; writing--review and editing, A.A. and T.-H.G. ; visualization, A.A.; supervision, T.-H.G.