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Parametric process optimization to improve the accuracy and mechanical properties of 3D printed parts

Published online by Cambridge University Press:  28 December 2018

Amirhossein Hakamivala Open the ORCID record for Amirhossein Hakamivala [Opens in a new window] ,
Amirali Nojoomi ,
Alieh Aminian ,
Arghavan Farzadi  and
Noor Azuan Abu Osman
Amirhossein Hakamivala
Affiliation:
Department of Bioengineering, University of Texas at Arlington, Arlington, Texas, United States
Amirali Nojoomi
Affiliation:
Department of Materials Science and Engineering, University of Texas at Arlington Arlington, Texas, United States
Alieh Aminian*
Affiliation:
BEGO Implant Systems GmbH & Co. KG, Department of Research and Development, Bremen, Germany
Arghavan Farzadi
Affiliation:
Department of Biomedical Engineering, The Ohio State University, Columbus, Ohio, United States
Noor Azuan Abu Osman
Affiliation:
Department of Biomedical Engineering, University of Malaya, Kuala Lumpur, Malaysia
*
*Alieh Aminian BEGO Implant Systems GmbH & Co. KG, Department of Research and Development, Bremen, Germany. Email: [email protected]
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Abstract

Investigating the mechanical properties and dimensional accuracy of 3D printed parts is an important step towards achieving optimum printing conditions. This condition, which leads to the fabrication of parts with appropriate mechanical properties and accuracy, is achieved by studying the effect of different process parameters on the final structure. In this work, Response Surface Methodology (RSM) was employed to design specified experiments to investigate the effects of layer thickness, printing orientation and delay, on the compressive strength and dimensional error of the parts. The results show that an increase in the delay time in X orientation results in better binder spreading and uniformity followed by improvement in the compression strength. Furthermore, more binder spreads in the vertical direction leads to the higher dimensional error in the Z direction. The results proved that the RSM provides a time and cost-efficient design to print the prototypes with optimum strength and dimensional error.

Keywords

3D printingmodeling
Type
Articles
Information
MRS Advances , Volume 4 , Issue 24: Processing and Manufacturing , 2019 , pp. 1383 - 1392
Copyright
Copyright © Materials Research Society 2018 

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References

Peltola, S. M., Melchels, F. P., Grijpma, D. W. and Kellomäki, M., Annals of Medicine 40 (4), 268–280 (2008).CrossRefGoogle Scholar
Shanjani, Y., Hu, Y., Pilliar, R. M. and Toyserkani, E., Acta Biomaterialia 7 (4), 1788–1796 (2011).CrossRefGoogle Scholar
Vorndran, E., Klarner, M., Klammert, U., Grover, L. M., Patel, S., Barralet, J. E. and Gbureck, U., Advanced Engineering Materials 10 (12), B67–B71 (2008).CrossRefGoogle Scholar
Farzadi, A., Solati-Hashjin, M., Asadi-Eydivand, M. and Osman, N. A. A., PloS One 9 (9), e108252 (2014).CrossRefGoogle Scholar
Bezerra, M. A., Santelli, R. E., Oliveira, E. P., Villar, L. S. and Escaleira, L. A. l., Talanta 76 (5), 965–977 (2008).CrossRefGoogle Scholar
Zhou, J. G., Herscovici, D. and Chen, C. C., International Journal of Machine Tools and Manufacture 40 (3), 363–379 (2000).CrossRefGoogle Scholar
Asiabanpour, B., Palmer, K. and Khoshnevis, B., Rapid Prototyping Journal 10 (3), 181–192 (2004).CrossRefGoogle Scholar
Weheba, G. and Sanchez-Marsa, A., Rapid Prototyping Journal 12 (2), 72–77 (2006).CrossRefGoogle Scholar
Ziemian, C. and Crawn, P. III, Rapid Prototyping Journal 7 (3), 138–147 (2001).CrossRefGoogle Scholar
Kun, H., Chengkui, Z., Baoying, L., Yanhang, W., Bin, H. and Jiang, C., Journal of Ceramic Science and Technology 8 (2), 249–254 (2017).Google Scholar
Moghassemi, S., Parnian, E., Hakamivala, A., Darzianiazizi, M., Vardanjani, M. M., Kashanian, S., Larijani, B. and Omidfar, K., Materials Science and Engineering: C 46, 333–340 (2015).CrossRefGoogle Scholar
Moghassemi, S., Hadjizadeh, A., Hakamivala, A. and Omidfar, K., AAPS PharmSciTech 18 (1), 34–41 (2017).CrossRefGoogle Scholar
Yazdi Rouholamini, S. E., Moghassemi, S., Maharat, Z., Hakamivala, A., Kashanian, S. and Omidfar, K., Artificial Cells, Nanomedicine, and Biotechnology 46 (3), 524–535 (2018).CrossRefGoogle Scholar
Farzadi, A., Waran, V., Solati-Hashjin, M., Rahman, Z. A. A., Asadi, M. and Osman, N. A. A., Ceramics International 41 (7), 8320–8330 (2015).CrossRefGoogle Scholar
Najdahmadi, A., Zarei-Hanzaki, A. and Farghadani, E., Materials & Design (1980-2015) 54, 786–791 (2014).CrossRefGoogle Scholar
Myers, R. H., Montgomery, D. C. and Anderson-Cook, C. M., Response Surface Methodology: Process and Product Optimization Using Designed Experiments (Wiley Series in Probability and Statistics). (Wiley, New York, 2009).Google Scholar
Khuri, A. I. and Mukhopadhyay, S., Wiley Interdisciplinary Reviews: Computational Statistics 2 (2), 128–149 (2010).CrossRefGoogle Scholar
Box, G. E. P. and Draper, N. R., Empirical Model-Building and Response Surfaces. (Wiley New York, 1987).Google Scholar
Baş, D. and Boyacı, I. H., Journal of food engineering 78 (3), 836–845 (2007).CrossRefGoogle Scholar
Giovanni, M., Food Technology 37 (11), 41–45 (1983).Google Scholar
Khoshkalam, M., Faghihi-Sani, M. and Nojoomi, A., Transactions of the Indian Ceramic Society 72 (3), 175–181 (2013).CrossRefGoogle Scholar
Vander Heyden, Y., Nijhuis, A., Smeyers-Verbeke, J., Vandeginste, B. and Massart, D., Journal of Pharmaceutical and Biomedical Analysis 24 (5-6), 723–753 (2001).CrossRefGoogle Scholar
Asadi-Eydivand, M., Hakamivala, A., Farzadi, A., Ghavimi, S. A. A. and Solati-Hashjin, M., in Biomedical Engineering and Sciences (IECBES), 2012 IEEE EMBS Conference on (IEEE, 2012), pp. 90–94.CrossRefGoogle Scholar