Artificial Neural Network-Based Optimisation of Geometric Characteristics in Laser Metal Deposition of TiC/Ti6Al4V

Abstract

Laser metal deposition operates on the principle of layer-by-layer material addition, wherein each layer is formed by overlapping individual single tracks. Consequently, clads formed serve as the fundamental building blocks for this technology. Their quality directly affects the overall build quality, particularly the geometric characteristics, which are also critical to process productivity. In the present work, geometric characteristics of TiC/Ti6Al4V single tracks fabricated via laser metal deposition are optimised. An artificial neural network model was developed to predict the clad width, height, and dilution using processing parameters, laser power, scan speed, and powder feed rate, as model inputs. The Particle Swarm Optimisation algorithm was employed for hyperparameter selection. The hyperparameter-optimised model achieved a mean squared error of 0.00183 and an R2 score of 0.979 during training, and a mean squared error of 0.00709 and an R2 score of 0.887 during testing. Although the small discrepancy between training and testing metrics suggests slight overfitting, likely due to the size of the dataset, the model achieved a mean absolute percentage error of less than 10% during testing. Subsequently, process plots generated by the model predictions were used to identify suitable parameters, and a processing map was developed to highlight the window that achieves suitable dilution (14–24%), defect-free sound bonding, and thick and dense clads.