Quantification and compensation of thermal distortion in additive manufacturing: A computational statistics approach

Abstract In this work, we have developed a computational probabilistic method to quantify the permanent (non-zero strain) continuum/material deformation. Different from physical-based modeling, the method developed here is based on a data-driven statistics approach, which solves the problem without needing the information of physical deformation process. The proposed method relies only on the scanned material data from the thermal distorted configuration as well as the shape of the initial design configuration. We coined this artificial intelligence based algorithm as the material deformation finding (MDF) algorithm. In this work, the MDF algorithm was first validated by a 2D synthetic example. We then demonstrated that the proposed MDF method can accurately find the permanent thermal distortion of a complex 3D printed structural component, and hence to identify the thermal compensation design configuration. The results obtained in this work indicate that one can use this data-driven statistics approach to significantly mitigate thermal distortion of 3D printed products in additive manufacturing.