Assessment of Shape Descriptors for Distortion Prediction in Powder Bed Fusion Process

Abstract

Abstract Metal additive manufacturing paves the way for industries to create new applications through unique design capabilities. The powder bed fusion process is one among many metal additive manufacturing technologies that are commercially successful. Despite its numerous advantages and application in various fields, defects may occur during processing, which causes premature failure of components. Distortion is one of the major defects, and it depends on process settings, geometry, and orientation related. These distortions and dimensional deviations should be predicted faster for part qualification for many industrial applications. This work attempts to predict distortions based on shape descriptors to address this issue. Shape descriptors are definitions used to identify the details of the shape of a model to be printed. It can be either two dimensional or three dimensional. In this work, 2D shape descriptors are selected for analysis. These 2D shape descriptors can help identify how the design features significantly affect the part distortion in the PBF process. In this work, a few 2D shape descriptors are defined and modelled as a design feature to achieve the objective. Then the respective models are subjected to distortion analysis. The relationship between shape descriptors and distortion are studied through inherent strain method based simulation of distortion. It is observed from the results that most shape descriptors defined in this work can be used to predict the distortion. This work serves as a base and can help create knowledge for proposing design guidelines for the metal powder bed fusion process and helps in redesigning to prevent distortions.