## 4 Discussion
### Following Research and Emerging Technologies  
Based on the findings presented in this study, further investigation will be conducted as part of the experimental research. This subsequent phase will involve interferometric measurement of stresses induced by volumetric shrinkage, and throughout the calibration and thermal testing. This will help determine which technological configuration yields the most stable results. Furthermore, an analysis will be conducted to examine the correlation between bonded joint homogeneity and stability using CT Scan technology. Figure 15 displays a cross-sectional view of a sample obtained by removing a portion of the sample through machining, revealing the presence of voids within the sample. Recent developments in engineering sciences opened promising opportunities for future research and innovation. Emerging methodologies such as \"deep learning\" and \"artificial neural networks\" have introduced novel avenues for the analysis of complex systems, offering comprehensive insights into a wide array of structural responses. In the context of the study on the precision and dimensional stability of bonded joints in CFRP parts, it is essential to acknowledge the potential influence of these advanced techniques. \begin{table}
\begin{tabular}{c c c c c} \hline \multirow{2}{*}{**Sample Number**} & \multirow{2}{*}{**Calibrated**} & \multicolumn{2}{c}{**Total Runout Deviation**} & \multicolumn{2}{c}{**Total Runout Deviation**} & \multicolumn{2}{c}{**Total Runout Deviation**} \\  & & **after 1 st Temperature Test** & **after 2 nd Temperature Test** & **after Mechanical Test** \\  & & [\%] & **[\%]** & **[\%]** \\ \hline
1 & yes & 13.9 & 86.1 & 4.28 \\
2 & yes & 22.03 & 38.99 & 38.99 \\
3 & no & 100 & 63.77 & 9.29 \\
4 & yes & 100 & 52.46 & 3.88 \\
5 & yes & 100 & 67.25 & 26.76 \\
6 & yes & 75.64 & 24.36 & 9.53 \\
7 & no & 100 & 40.67 & 10.74 \\
8 & yes & 81.2 & 77.38 & 96.18 \\
9 & no & 100 & 93.29 & 5.63 \\
10 & yes & 100 & 58.63 & 44.16 \\
11 & no & 75.75 & 24.25 & 32.8 \\
12 & no & 84.11 & 0.79 & 15.1 \\
13 & no & 100 & 58.63 & 10.41 \\
14 & yes & 100 & 5.63 & 78.4 \\
15 & no & 11.7 & 88.3 & 85.3 \\ Average & & 77.62 & 52.03 & 31.43 \\ \hline \end{tabular}
\end{table} 
Table 6: Total runout deviation at different stages of testing. Figure 14: Deviation of total runout during the environmental tests with the first cycle of temperature tests as a starting point. One of the significant aspects of these emerging research areas is the identification of inherent biases in deep convolutional neural network models, as outlined by [PERSON] et al. [73]. Recognizing and addressing these biases are of paramount importance in the broader landscape of research, as they can significantly impact analysis and result interpretation. Although [PERSON] et al. [74] focused on a different domain, their work presents a multigradient-direction-based deep-learning model for disease identification. This concept of employing deep learning for pattern recognition holds potential in assessing the homogeneity and recognizing defects of bonded joints. Additionally, [PERSON] et al. [75] introduced a probabilistic time series forecasting approach with deep nonlinear state space models, which could have many applications in manufacturing technologies in general. Lastly, [PERSON] et al. [76] introduce an intelligent vehicle lateral control system based on a radial basis function neural network sliding mode controller. While their application differs from ours, it underscores the potential of neural network-based control strategies in general, which could be useful in any automation. In recent years, there has been a growing body of research utilizing deep-learning techniques for the analysis of (CFRP). [PERSON] et al. [77] presented a comprehensive benchmarking study that assesses strain in CFRP laminates using computer vision. Their work delves into the distinction between traditional machine learning and deep-learning approaches, offering valuable insights into potential methodologies for future investigations in our field.