Optimal offline compensation of shape shrinkage for three-dimensional printing processes

Abstract

Dimensional accuracy is a key control issue in direct three-dimensional (3D) printing. Part shrinkage due to material phase changes often leads to deviations in the final shape, requiring extra post-machining steps for correction. Shrinkage has traditionally been analyzed through finite element simulation and experimental investigations. Systematic models for accuracy control through shrinkage compensation are rarely available, particularly for complete control of all local features. To fill the gap for direct printing and compensate for shape shrinkage, this article develops a new approach to (i) model and predict part shrinkage and (ii) derive an optimal shrinkage compensation plan to achieve dimensional accuracy. The developed approach is demonstrated both analytically and experimentally in a stereolithography process, one of the most widely employed 3D printing techniques. Experimental results demonstrate the ability of the proposed compensation approach to achieve an improvement of an order of magnitude in the reduction of geometric errors for cylindrical products.

Keywords:

• Degradation modeling
• prognostics
• reliability prediction
• vibration modeling

Additional information

Notes on contributors

Qiang Huang

Qiang Huang received his Ph.D. degree in Industrial and Operations Engineering in 2003 from the University of Michigan–Ann Arbor. He is currently an Associate Professor and Gordon S. Marshall Early Career Chair in Engineering in the Daniel J. Epstein Department of Industrial and Systems Engineering, University of Southern California, Los Angeles. His research focuses on modeling and analysis of complex systems for quality and productivity improvement, with special interest in integrated nanomanufacturing and nanoinformatics, and additive manufacturing. He is a member of the IEEE, IIE, INFORMS, and ASME. He received the 2013 IEEE Transactions on Automation Science and Engineering Best Paper Award. He has been an Associate Editor of the IEEE Transactions on Automation Science and Engineering since 2012 and has been a member of the scientific committee (Editorial Board) for the North American Manufacturing Research Institution (NAMRI) of SME, 2009–2011 and 2013–2015.

Jizhe Zhang

Jizhe Zhang is current a Ph.D. student at University of Southern California.

Arman Sabbaghi

Arman Sabbaghi is an Assistant Professor in the Department of Statistics at Purdue University. His research interests include causal inference, experimental design, Bayesian statistics, and algebraic statistics. He received his Ph.D. degree in Statistics from Harvard University in May 2014 and B.S. degrees in Mathematics and Statistics from Purdue University in May 2009.

Tirthankar Dasgupta

Tirthankar Dasgupta is an Associate Professor in the Department of Statistics at Harvard University. His research interests include experimental design, statistical applications in the physical sciences and engineering, causal inference, and quality engineering. He received his Ph.D. in Industrial Engineering from Georgia Institute of Technology in 2007.