A point cloud is a digital representation of a part that consists of a set of data points in space. Typically point clouds are produced by 3D scanners that hover above a part and records points in a large number that represent the external surface of a part. Additive remanufacturing offers a sustainable solution to end-of-use (EoU) core disposal and recovery and requires quantification of part damage or wear that requires reprocessing. This paper proposes an error propagation approach that models the interaction of each step of the additive remanufacturing process. This proposed model is formulated, and the results of the errors generated from the parameters of the scanner and point cloud smoothing are presented. Smoothing is an important step to reduce the noises generated from scanning, knowing the right smoothing factor is important since over smoothing results in dimensional inaccuracies and errors, especially in cores with smaller degrees of damage. It is important to know the error generated from scanning and point cloud smoothing to compensate in the following steps and generate appropriate material deposition paths. Inaccuracies in the 3D model renders can impact the remainder of the additive remanufacturing accuracy, especially because there are multiple steps in the process. Sources of error from smoothing, meshing, slicing, and material deposition are proposed in the error propagation model for additive remanufacturing. Results of efforts to quantify the scanning and smoothing steps within this model are presented.