# Development of an Artificial Intelligence-Based System for Predicting Weld Bead Geometry
###### Abstract  
The prediction of the weld bead geometry parameters is an important aspect of welding processes due to it is related to the strength of the welded joint. This research focuses on using statistical design techniques and a deep learning neural network to predict the weld bead shape parameters of shielded metal arc welding (SMAW), metal inert gas (MIG), and tungsten inert gas (TIG) welding processes. With the statistical design techniques, experiments were carried out to obtain the data for generating the regression models. Establishing mathematical models that shows the relationship between welding process parameters and weld bead size is significant for practical applications. The mathematical model enables the determination of the weld bead size when setting specific welding process parameters. In this research, experimental research results were obtained to build mathematical models showing the relationship between welding process parameters and weld bead geometries for SMAW, MIG, and TIG welding processes. The research results serve as the basis for establishing predictive systems or optimizing welding process parameters. With deep learning neural network techniques, we developed an artificial intelligence-based system for predicting complicated relations between the welding process parameters and the weld bead size. Both a regression model and the deep learning model result in a good correlation between the welding process parameters and the weld bead geometry. predictive system; shielded metal arc welding (SMAW); metal inert gas (MIG); tungsten inert gas (TIG); artificial intelligence; regression model; weld bead geometry +
Footnote †: journal: Medical Engineering  
relationship between the SMAW process parameters (including welding current, arc length, welding speed, welding gap, and electrode diameter) and the weld bead geometry [1]. Mathematical models using the regression technique and statistical analysis were generated to predict the weld bead geometry in the double pulse gas metal arc welding process [8]. [PERSON] et al. considered the effect of the welding current on the weld bead geometry when changing the welding current in experiments [9]. For optimizing the welding parameters in the SMAW process, the factorial design approach was used to establish the relationship between the welding deposition area and the welding voltage, welding current, and welding speed [10]. The MIG welding process with welding speed, voltage, wire feed rate, gas flow rate, nozzle plate distance, and torch angle as the input parameters and weld bead width, weld bead height, and weld bead penetration as the output parameters was analyzed using the regression technique. Both linear and nonlinear regression analyses were employed to establish the input-output relations [2]. Artificial intelligence techniques were applied for predicting the weld bead shape. [PERSON] et al. developed a radial basis function neural network (RBF-NN)-based model and a multilayer perceptron neural network (MLP-NN) model for predicting the weld base shape in the SMAW welding process [11]. The results show that the RBF-NN model was able to achieve a high level of accuracy in simulating the weld geometry and very satisfactorily predicted all parameters in comparison with the MLP-NN model. This confirms that the RBF-NN technique is a powerful tool for analysis and modeling [11]. In the SMAW process, research on the effect of the welding process parameters, cryo-treatment, and preheating on the welding characteristics using an artificial neural network (ANN) was carried out [12]. For the SMAW process, [PERSON] et al. used an ANN to predict the weld width in consideration of values of current, voltage, speed of welding, and external magnetic field to produce the best quality of weld with respect to weld width [13]. Integration of regression and artificial intelligence techniques was also proposed for predicting the weld bead in a single welding process. [PERSON] et al.