Deep learning driven prediction method for roughness and hardness of laser in-situ forging additive manufacturing Ti-6Al-4V

Laser powder bed fusion suffers from surface quality, thermal stress, defects, and poor fatigue performance in Ti-6Al-4V components due to unstable melting/solidification. Based on this, this research proposes laser in situ forging additive manufacturing (LIF-AM). By using an ultrafast laser to impact the molten layer in situ, the performance of additive manufacturing components can be improved. Addressing complex parameters and unclear mechanisms, this research constructs a deep learning model based on a CNN (Convolutional Neural Network)-Transformer to predict the surface roughness and hardness of LIF-AM components. The CNN’s dynamic sliding kernels extract local interactions, while the Transformer’s self-attention captures global dependencies. This resolves the nonlinear coupling and long-range correlation challenges in high-dimensional parameter space. In the experimental verification, the coefficient of determination R² is used to measure the degree of fit between the model's predicted values and the actual values. For predicting roughness and hardness, the model achieves R² values of 0.922 and 0.920, respectively, with mean absolute errors of 0.095 and 3.824, respectively. This research provides a data-driven intelligent solution for the optimisation of the LIF-AM process, significantly reducing the traditional trial-and-error cost and promoting intelligent manufacturing of high-performance titanium alloy components in fields such as aerospace.