Towards a Porosity Aware Stochastic Framework for Computing Apparent Mechanical Properties of Additively Manufactured Parts

Abstract The presence of pores in parts generated via metal Additive Manufacturing (AM) may substantially impact their mechanical performance. To understand the resulting performance, it is essential to identify the quantitative relationship between the size, shape, and location of the pores and the mechanical properties of the manufactured part. To obtain insight into this relationship, we have initiated the development of a stochastic framework that takes as input digital microscope images of AM part sections and provides as output the distribution of mechanical properties of interest such as the apparent (in the global sense) yield stress and the stress-strain response. The distribution of these pores has a semi-stochastic nature, which depends on the process type, process parameters, material type, and AM path. Firstly, we calculate various pore metrics using digital image processing techniques. The metrics are related to geometric characteristics, such as the distance of the pore from the specimen surface. Subsequently, we generate a two-dimensional distribution based on non-parametric principles. We use this distribution to sample exemplified geometries and develop multiple Finite Element Models (FEM). Then we perform virtual experiments to calculate the non-linear stress-strain response for each FEM. The results are then distributed to bins in order to generate distributions and histograms of mechanical properties of interest. We demonstrate the framework by applying it on an AM-produced conformal pressure vessel to show its capacity in computing the distribution of relevant quantities.