Nonlinear Improved Adaptive Optimized Grey Model for Real-Time Monitoring and Control of Weld Bead Geometry in Robot-Assisted Wire Arc Additive Manufacturing

Abstract

WAAM with directed energy deposition process and live monitoring of weld bead shape gives researchers and manufacturers real-time information, allowing them to regulate the metal deposition process layer by layer effectively. This work created an improved adaptive optimized grey model (IAOGM), for online monitoring of metal deposits' height and depth in each layer. The IAOGM(1,N) model takes fewer training samples and has limited information. To improve prediction accuracy, the training data was constantly updated by eliminating old data and introducing newer data. The framework's parameters comprised welding time, current, the absolute difference in current at 5-second intervals, and arc force. The best accurate weld bead height and depth estimates were obtained by calculating the root mean square error (RMSE) for various parameter combinations. The interplay of time, current, and arc force was discovered to have a considerable impact on weld bead diameters. Using these parameters, the model predicted weld bead height and depth with MAPE, RMSE, and MAE, values of 5.48, 3.32, and 7.56 respectively, when compared to experimental data. This technique enables users to properly balance process parameters and achieve desired weld bead heights without the requirement for substantial training.