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research-article

A Reverse Compensation Framework for Shape Deformation Control in Additive Manufacturing

[+] Author and Article Information
Kai Xu

Epstein Department of Industrial and Systems Engineering, University of Southern California, Los Angeles, CA 90089
kaixu@usc.edu

Tsz Ho Kwok

Epstein Department of Industrial and Systems Engineering, University of Southern California, Los Angeles, CA 90089
tom.thkwok@gmail.com

Zhengcai Zhao

Epstein Department of Industrial and Systems Engineering, University of Southern California, Los Angeles, CA 90089
zhaozhengcaihappy@163.com

Yong Chen

Epstein Department of Industrial and Systems Engineering, University of Southern California, Los Angeles, CA 90089
yongchen@usc.edu

1Corresponding author.

ASME doi:10.1115/1.4034874 History: Received July 22, 2016; Revised August 28, 2016

Abstract

Shape deformation is a well-known problem in additive manufacturing (AM). For example, in the stereolithography (SL) process, some of the factors that lead to part deformation include volumetric shrinkage, thermal cooling, added supporting structures, and the layer-by-layer building process. Variant sources of deformation and their interactions make it difficult to predict and control the shape deformation to achieve high accuracy that is comparable to numerical controlled machining. In this paper, a computational framework based on a general reverse compensation approach is presented to reduce the shape deformation in AM processes. In the reverse compensation process, the shape deformation is first calculated by physical measurements. A novel method to capture the physical deformation by finding the optimal correspondence between the deformed shape and the given nominal model is presented. The amount of compensation is determined by a compensation profile that is established based on nominal and offset models. The compensated digital model can be rebuilt using the same building process for a part with significantly less part deformation than the built part related to the nominal model. Two test cases have been performed to demonstrate the effectiveness of the presented computational framework. There is a 40% to 60% improvement in terms of L2- and L8-norm measurements on geometric errors.

Copyright (c) 2016 by ASME
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