Statistical Predictive Modeling and Compensation of Geometric Deviations of Three-Dimensional Printed Products

Abstract

Geometric fidelity of 3D printed products is critical for additive manufacturing (AM) to be a direct manufacturing technology. Shape deviations of AM built products can be attributed to multiple variation sources such as substrate geometry defect, disturbance in process variables, and material phase change. Three strategies have been reported to improve geometric quality in AM: (1) control process variables x based on the observed disturbance of process variables Ax, (2) control process variables x based on the observed product deviation Ay, and (3) control input product geometry y based on the observed product deviation Ay. This study adopts the third strategy which changes the computer-aided design (CAD) design by optimally compensating the product deviations. To accomplish the goal, a predictive model is desirable to forecast the quality of a wide class o f product shapes, particularly considering the vast library of AM built products with complex geometry. Built upon our previous optimal compensation study of cylindrical products, this work aims at a novel statistical predictive modeling and compensation approach to predict and improve the quality of both cylindrical and prismatic parts. Ex perimental investigation and validation of polyhedrons a indicates the promise of predict ing and compensating a wide class of products built through 3D printing technology.

Model transfer across additive manufacturing processes via mean effect equivalence of lurking variables

“…In contrast, Huang, Zhang, Sabbaghi, and Dasgupta [12] conceived of a distinct functional modeling framework that effectively accounts for the correlation in deviation between different directions, and decouples geometric shape complexity from deviation modeling and compensation. Further advances and experiments under their framework include in-plane deviation modeling for polygons and free-form shapes [11,16,25], interference modeling for discretized compensation plans [24], and out-of-plane deviation modeling for 3D shapes [14].…”

Artificial Neural Network Based Geometric Compensation for Thermal Deformation in Additive Manufacturing Processes

“…[20] studied the impact of different process parameters on part shrinkage during SLS process, and used Taguchi method to develop scaling models along x, y and z directions to compensate for the shrinkage. Huang et al [21,22] investigated the offline shape-shrinkage compensation for the individual layer contours using a statistical approach. Wang et al [23] reported a Neural Network based approach for establishing the relation between AM process parameters and shrinkage ratio for parts fabricated using SLS.…”

A Reverse Compensation Framework for Shape Deformation Control in Additive Manufacturing

“…Zha and Anand [24] presented a geometric approach to improve errors of part by modifying input stereolithography (STL) models in AM processes. Huang et al [25][26][27] conducted research using statistical approaches to model and predict in-plane shrinkage and out-of-plane deformation of different parts, and derive compensation to improve accuracy of built part in the MIP-SL process. All these research have effects on improving errors of built parts in AM processes.…”