Real-time simulation for long paths in laser-based additive manufacturing: a machine learning approach

Abstract

This study predicts in real time the evolution of temperature and density for arbitrary long tracks in laser-based additive manufacturing using artificial neural networks (ANNs). First, a random laser path is transformed appropriately to serve as an ANN input via a custom trajectory decomposition method providing a local description of each trajectory point relative to its surroundings, which is calculated and load-optimized using K-d trees. For each trajectory point, a “Master” ANN calculates the present temperature, using the trajectory descriptors and previous step temperature as inputs. This is made into a parallel procedure by exploiting a “Pilot” ANN without temperature feedback, making a first pass over the whole trajectory to estimate temperature responses. The Master ANN uses these results as a feedback that is iteratively refined. A “Rider” ANN uses the final temperatures as input and calculates local density evolution. Four hundred fifty finite element model experiments of short random walk trajectories were used for ANN training. Results are presented for various types of laser paths, including random, hatch, spiral, fractal, and spline. ANN simulation execution time consistently taking under 50% of the real process time in all validated cases for track length in excess of 100 m proved the ability of the platform to operate at the full layer-scale of LBAM.