# Development of an Artificial Intelligence-Based System for Predicting Weld Bead Geometry
## 2 Materials and Methods
### Testing and Evaluating the Functionality of the Developed Predictive System  
The functionality of the developed system was tested successfully. The predicted results are reliable in comparison with the experimental results and the calculated results. Table 10 shows the prediction results of the developed system for the TIG welding process, in which P-K\({}_{\text{T}}\), P-R\({}_{\text{T}}\), and P-P\({}_{\text{T}}\) are the predicted weld bead width, reinforcement, and penetration, respectively. C-K\({}_{\text{T}}\), C-R\({}_{\text{T}}\), and C-P\({}_{\text{T}}\) are the weld bead width, reinforcement, and penetration calculated by using Equations (7), (8), and (9), respectively. Dev. of K\({}_{\text{T}}\), Dev. of R\({}_{\text{T}}\), and Dev. of P\({}_{\text{T}}\) are the deviations between the prediction and calculation results of the weld bead width, reinforcement, and penetration, respectively. The % of K\({}_{\text{T}}\), % of R\({}_{\text{T}}\), and % of P\({}_{\text{T}}\) are the deviations between the prediction and calculation results of the weld bead width, reinforcement, and penetration in % for the TIG welding process, respectively.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c} \hline \hline
**M** & **N** & **O** & **P** & **Q** & **C-K\({}_{\text{T}}\)** & **C-R\({}_{\text{T}}\)** & **C-P\({}_{\text{T}}\)** & **P-K\({}_{\text{T}}\)** & **P-R\({}_{\text{T}}\)** & **P-P\({}_{\text{T}}\)** & **Dev.** & **\% of** & **Dev.  
welding process, maximum of the deviation between the predicted and calculated results is 0.33 mm (or 4.49%) for the weld bead width, 0.05 mm (or 4.58%) for the reinforcement, and 0.11 mm (or 5.55%) for the penetration. The maximum is 0.2 mm (or 2.74%), 0.14 mm (or 3.85%), and 0.13 mm (or 4.75%) for the deviation between the predicted and calculated results of the weld bead width, the reinforcement, and the penetration in the MIG welding process, respectively. With the SMAW welding process, it is 0.36 mm (or 3.87%), 0.05 mm (or 3.6%), and 0.09 mm (or 4.16%) for the deviation between the predicted and calculated results of the weld bead width, the reinforcement, and the penetration, respectively. The maximum deviation between the predicted result and the calculated result in the case of predicting the weld bead penetration of the TIG welding process is 5.55%. This deviation is acceptable and reliable. In comparison with the experimental results, the ranges of values of the weld bead geometry are within the allowed limitations as shown in Table 13. These results demonstrate the accuracy of mathematical models and artificial intelligence-based models.  
In consideration of the research contribution to the classic theory of welding, the mathematical models generated by using the regression technique for analyzing the experiment data enable us to determine the main factors of the welding process parameters which affect the weld bead geometries. From that, we can define the appropriate welding process parameters to ensure weld quality.  
From the main factors determined from the generated mathematical models, we developed the artificial intelligence-based models with the input including the main factors of the welding process parameters and the outputs which are the weld bead geometries such as the weld bead width, reinforcement, and penetration. For establishing the artificial intelligence-based models, we used the deep learning neural network technique which uses the experimental data for training networks to obtain the predicted weld bead geometries accurately. The artificial intelligence-based models will be applied for the online monitoring of the welding process to keep the weld quality consistent.