Finite Element Analysis and Machine Learning in the Loop Additive Manufacturing Process Parameter Optimization to Acquire the Desired Dynamics of Manufactured Parts

In this study, a constrained optimization procedure was presented that adjusts processing parameters of powder bed fusion (PBF) process to acquire desired dynamic behavior of produced parts, particularly in case of geometrical design of part to be produced have already confirmed and not possible to change. This procedure includes the combination of machine learning (ML) and finite element method (FEM) in the loop to correlate between processing parameters and dynamic response of parts. PBF production technique has leading processing parameters (laser power, scanning speed, hatch distance, and beam diameter) which have effect on mechanical properties of produced parts, for example, stiffness, elasticity, or mass density. Among different mechanical properties there are some properties which are related with dynamic behavior of parts. Thus, understanding the effect of processing parameters on mechanical properties is a crucial step to predict dynamic behavior of part especially where part design has already completed and not allowed to change in geometry. To do that, mechanical properties were acquired by the experimental data and semi-analytical equations were created accordingly by ML models. Then, the equations were used in FEM to get dynamic responses such as natural frequencies and mode shapes of produced parts in an optimization procedure. Overall, experimental data were correlated with numerical FEM model in the loop of optimization cycle to find desired dynamic behavior from produced parts. Eventually, it is proofed that natural frequencies are function of material properties but mode shapes were not affected by changing material constants. In addition, proposed method showed that dynamic behavior of PBF produced parts (elasticity modulus, relative density, etc.) can be modified by optimizing processing parameters of production process, including FEM and ML in the loop of constrained optimization problem where the final part design has already fixed.