## 4 Conclusions  
This research focused on the AE model for large crack growth assessment. In order to establish the AE signal feature versus the fatigue crack growth model and study the consistency and accuracy of the model, several standard fatigue experiments were performed. A set of tests were performed using standard AI7075-T6 test specimens subjected to cyclic loading with different amplitude and frequencies. A previously proposed relationship between the crack growth rate and AE signal features generated during crack growth was modified and validated. The AE-based crack growth model was found to be independent of the loading condition and loading frequency. These findings validate the previous work by [PERSON] [17] as the proposed model is independent of the loading ratio and frequency.  
Figure 10: Comparison of AE model prediction and experimental results (\(\log\,da/dN\)). WB indicates Bayesian regression analysis results using WinBugs.  
Based on three identical tests with Ti-6-4, it was concluded that while the model parameters are material-dependent, the linear model form depicting the relationship between the crack growth rate and AE count rate remains valid when the material is changed.  
The obtained experimental data was uncertain in nature due to considerable uncertainties in the optical crack detection method and measurement errors associated with the utilized crack sizing technique. The procedure of probabilistic model development and validation were discussed, and uncertainties of the model were investigated. To deal with uncertainties, a Bayesian approach was used to consider systematic and random errors in the model by capturing the effect of uncertainties. This approach provided a framework for updating the distribution of the model parameters.  
Development of the proposed AE monitoring technique reported in this paper facilitates for the prognostics and life predictions of the structure. The developed methodology can be utilized for continuous in-service monitoring of structures and has proven to be promising for use in life predictions and assessment for structures subject to fatigue loading. Ultimately, these predictions can be used to define the appropriate inspection policies and maintenance schedules.  
To update the model for any material variation, quantitative material properties that correlate to the AE count rate and crack growth rate could be identified. This process would be extensive due to the numerous material properties that affect a material's failure mechanisms including fracture toughness, modulus of elasticity, and yield strength. Once properties are identified, numerous types of materials could be tested to validate and update the model to account for material variations. In addition, the model is based on tests using the same specimen geometry of a specific plate thickness. Since crack growth behavior can also be dependent on the thickness of the material, the AE response and subsequently the model would be dependent on material thickness and specimen geometry. Future research may also focus on experimental studies to account for the effects of material thickness.  
[PERSON] developed and performed the AI/075-T6 acoustic emission and fatigue crack growth experiments while [PERSON] performed the Ti-6-4 experiments. [PERSON] and [PERSON] each performed the initial data analysis steps including crack growth measurements, de-noising, and AE count rate calculations for each of their respective experiments. [PERSON] performed the Bayesian analysis and model parameter development for all tests. [PERSON] wrote the majority of the paper, while [PERSON] contributed to several sections and assisted in editing. Finally, [PERSON] was the academic research advisor in this work and provided guidance throughout the research and edited the paper.  
The authors declare no conflict of interest.