ANN-based modelling of dimensional accuracy in L-PBF

Laser-based powder bed fusion (L-PBF) is one of the AM techniques that has continued to gain an increased market acceptance and penetration, particularly in a wide range of industrial applications that include automotive, aerospace, medical/dental and robotics. However, in spite of its unstoppable rise in popularity, the L-PBF process still poses some technological challenges that need addressing in order to improve the robustness and repeatability of parts produced. In particular, when compared to other conventional manufacturing technologies, AM and L-PBF in particular lags behind when it comes to being able to predict different quality marks of the parts produced, such as dimensional accuracy and surface quality. In this context, the aim within this paper is to implement a systematic approach to improve precision of printed parts through predictive process modelling and experimental-based study. The methodology of a comprehensive Design of Experiments (DoE) study to improve process knowledge of down-facing surfaces is presented along with the methodology used to generate the dataset with regards to dimensional inaccuracy. The acquired results are used to build process models based on Artificial Neural network (ANN). ... mehrThe prediction accuracy of the proposed model is discussed, and the feasibility of the proposed approach is demonstrated. The outcome of this paper helps understand the L-PBF method and the influence of the governing parameters to further develop high precision L-PBF processes.