## 5 Conclusions  
This paper delves into a comprehensive investigation of weak adhesion detection within SLJ through a fusion of LW simulations and advanced machine learning techniques.  
Adhesive joints have gained considerable popularity in diverse industries, yet weak adhesion remains a formidable challenge due to its difficult detection before triggering premature structural failure. The utilization of Law, acknowledged for its sensitivity to these types of defects, coupled with machine learning algorithms, presents a novel and promising avenue for addressing this critical issue.  
The study methodology encompasses the generation of validated simulated weak adhesion scenarios, closely aligned with experimental ones. Leveraging the intrinsic capabilities of LWs, combined with the sophisticated capabilities of machine learning, enables accurate determination of weak adhesion damage levels. The simulations are compared with realistic cases, leading to LW signals that are subsequently harnessed by machine learning models for robust damage quantification with an over 26% increase in capacity. The validation process serves to underscore the efficacy of amalgamating simulated data with a more limited experimental dataset to train the machine learning models, as these augmentations of the experimental dataset result in tangible improvements in terms of detection accuracy. It also allows for further studies to be conducted only with simulated data. The proposed machine learning framework, manifested as a FFNN, exhibits remarkable potential in precisely predicting the extent of weak adhesion damage, holding true even when the defect's location within the joint is altered, with an overall damage quantification of 95%.  
In essence, this study advances the notion that the integration of LW simulations and machine learning techniques offers a pioneering solution that enhances both the precision and dependability of weak adhesion defect detection in adhesive joints. The resultant synergy between these two methodologies holds the key to elevating the quality control of adhesive bonding processes, providing an unparalleled degree of accuracy in damage assessment. This promising approach not only bolsters quality control but also offers the prospect of serving as a potent structural health monitoring tool, thereby fortifying the integrity and longevity of adhesive joint applications across an array of industries.  
Using the methods outlined in this paper along with more simulations will allow to determine not only the intensity of the damage but also its location, shape, and dimension. It can also be used to determine whether the damage is a void, weak adhesion, adhesive cracks, or any other type of damage. Furthermore, there is a large area of practical research where the effects of temperature, humidity, and noise need to be studied and determined. Finally, in order to widen the scope and practicality of this study, it should include highly  
Figure 16: Comparison between the error for each real value of damage in their x coordinate location. The size of the circles represents the dimension of the error.  
flexible adhesives, as is the case of silicone, and those with a broad band of characteristics, such as epoxies.  
Conceptualization, G.M.F.R., L.F.M.d.S. and A.M.L.; methodology, G.M.F.R. and A.M.L.; software, G.M.F.R.; validation, G.M.F.R., L.F.M.d.S. and A.M.L.; formal analysis, G.M.F.R., L.F.M.d.S. and A.M.L.; investigation, G.M.F.R. and A.M.L.; writing--original draft preparation, G.M.F.R.; writing--review and editing, G.M.F.R., L.F.M.d.S. and A.M.L.; supervision, L.F.M.d.S. and A.M.L.; project administration, L.F.M.d.S. and A.M.L. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Data available on request to the authors.  
The authors declare no conflict of interest.