# Development of an Artificial Intelligence-Based System for Predicting Weld Bead Geometry
###### Abstract
carried out experiments to generate a linear regression model with welding speed, wire feed rate, % cleaning, gap, and welding current as the input parameters and the weld bead shape as the response in the TIG welding process [6]. A back-propagation neural network also was used for predicting the weld bead shape [6]. [PERSON] et al. proposed both a regression model and a machine learning model for predicting the weld bead shape in the gas metal arc welding (GMAW) process with the input parameters including plate thickness, welding current, and welding speed and the weld bead shape as the response [14]. In addition, with the GMAW welding process, [PERSON] et al. carried out experiments for comparison with the results of the weld bead shape generated by applying ANN. The comparison of experimental and predicted results shows that an ANN-based model can be successfully employed to predict multiple weld geometries [15]. For the TIG welding process, [PERSON] et al. developed mathematical models for weld bead shape parameters using the multiple linear regression technique with experimental data. Then, these data also were used to predict the weld bead shape parameters using a back-propagation neural network [16]. Back-propagation with the Levenberg-Marquardt training algorithm was used to train a neural network for predicting the weld bead geometry in laser micro-welding [17]. The The Levenberg-Marquardt training algorithm also was used for predicting weld beads in the SMAW welding process [18]. A neural network and a second-order regression analysis were proposed for predicting bead geometry in robotic gas metal arc welding [19]. In this study, a neural network was adopted to characterize the complex relationship between input variables and the bead geometry. [PERSON] et al. reported research on multiple regression and back-propagation neural network approaches in modeling the top bead height of multi-pass gas metal arc (GMA) welds with the input parameters including pass number, welding current, welding speed, and arc voltage [20]. A deep learning neural network with four-hidden-layer architecture and a sigmoid activation function for estimation of the weld bead parameters in the TIG welding process was proposed by [PERSON] et al. [21]. From the literature review, experiments for generating the mathematical models which show the relationship between the welding process parameters and the weld bead shape were studied. Artificial intelligence techniques for developing models for predicting weld bead shape also were used. However, the integration of a predictive system for predicting the weld bead shape with the SMAW, MIG, and TIG welding processes has not yet been carried out and reported. Currently, we are developing a virtual welding system for training students to have welding skills. The proposed virtual welding system enables learners to practice welding operations. So, they gain welding experience to minimize errors when carrying out real welding. With this purpose, we propose the development of a system that integrates three welding processes, namely the SMAW, MIG, and TIG welding processes. In this paper, we propose the development of a predictive system that enables the prediction of the weld bead information according to the welding process parameters of the SMAW, MIG, and TIG welding processes. This is one part of the whole virtual welding system. In order to consider the effects of the main factors of the welding process, a linear regression technique has been used to develop mathematical models for the weld bead shape parameters of SMAW, MIG, and TIG welding processes.