Hatch Pattern Optimization of Powder Bed Fusion Additive Manufacturing Process for Minimizing Flatness error

Abstract

Although Metal Powder Bed Fusion Additive Manufacturing (PBFAM) process has emerged as an important industrial process, part distortion due to repeated heating and cooling is a major barrier that stands in the way of using this process for mass commercial production. Laser hatch pattern is a critical factor that influences the part distortion and has been widely studied through experimental investigations. In this paper, we describe an approach for hatch pattern optimization to minimize part GD&T errors using Genetic algorithms (GA). A pre-trained Artificial Neural Network (ANN) was adopted to predict the inherent strain of any hatch pattern, while a Backward Interpolation (BI) approach was used to predict the distortion of the sample part based on the inherent strain obtained from ANN. A Genetic Algorithm approach was then used to optimize the hatch angle of each layer to minimize the flatness form error of a flat feature of a sample part. Three types of island strategies were investigated for laying hatch patterns, and the results show that the increased number of scan islands in the hatch pattern results in less distortion and minimization of flatness error. The flatness values of four other benchmark hatch patterns were also investigated and compared with the optimal hatch pattern results. The comparison showed that the flatness of the sample part with the optimized hatch pattern performs better than the benchmark hatch patterns. The overall computational time for hatch pattern optimization was found to be reasonable, considering the large number of distortion simulations performed during the optimization process.