Modeling of Dimensional Accuracy in Three Dimensional Printing for Light Alloy Casting

Abstract:

The purpose of this paper is to develop mathematical model to investigate the influence of shell casting parameters. Three input parameters such as shell wall thickness (SWT), Pouring temperature (PT) and weight density (WD) were selected to give output in the form of average outer diameter (AOD) as dimensional accuracy. After identification of component, technological prototypes were produced. In this work three dimensional printing (3DP) has been used as rapid shell casting to make shell mould by using Zcast 501 powder with different shell wall thickness for six different light alloy materials. Measurements on a coordinate-measuring machine helped in calculating the dimensional tolerances of the castings produced. For obtaining tight casting tolerances the dimensional accuracy of component is the most important element. The thickness, curing time and orientation of the shell molds, play an important role in providing a high quality of the cast part in time. The dimensional accuracy was found to be more in the case of maximum layer thickness and horizontal position of the component. The investigation has led to conclusions as the Quadratic models were developed for the response. The F - value is 23.93, which implies that the model as well as lack of fit is significant. The value of Prob > F is less then the standard value 0.05, which indicates model terms are significant. With the help of Post curing, shell Mold temperature was not found to affect the dimensional accuracy of the castings, significantly. It was observed that high pouring temperatures also produced castings with better dimensional accuracy. This study will provide main effect of the inputs on average outer diameter as dimensional accuracy in three dimensional printing of light alloys castings. Statistically in this case B, C, A², B², C², AB, BC is the model terms which contributes significantly to the model developed for dimensional accuracy.

• Periodical: Materials Science Forum (Volume 808)
• Pages: 65-78
• DOI: https://doi.org/10.4028/www.scientific.net/MSF.808.65
• Online since: December 2014
• Authors: Rajesh Kumar*, Rupinder Singh, Inderpreet Singh Ahuja
• Keywords: Coordinate Measuring Machine, Dimensional Accuracy, Mathematical Modeling, Three Dimensional Printing

References

[1] E.M. Sachs, J.S. Haggerty, M.J. Cima and A.P. Wiliams, Three dimensional printing techniques, United States Patent No. US 005340656, (1994).

[2] N. P. Karapatis, J.P.S. Griethuysen and R. Glardon, Direct rapid tooling: a review of current research, Rapid Prototyping Journal 4(2) 1998 77–89.

DOI: 10.1108/13552549810210248

[3] E. Bassoli, A. Gatto, L. Iuliano and M.G. Violante, 3D printing technique applied to rapid casting, Rapid Prototyping J. 13(3) 2007 148–155.

DOI: 10.1108/13552540710750898

[4] D.W. Dimitrov, K. Schreve and N. Beer, Advances in three dimensional printing–state of the art and future perspectives, Rapid Prototyping J. 12(3) 2006 136–147.

DOI: 10.1108/13552540610670717

[5] P.M. Dickens, R. Stangroom, M. Greul, B. Holmer, K.K.B. Hon, R. Hovtun, R. Neumann, S. Noeken and D. Wimpenny, Conversion of RP models to investment castings, Rapid Prototyping Journal 1(4), 2011, 4-11.

DOI: 10.1108/13552549510104401

[6] R. Brian, Rapid tooling for casting prototypes, Assembly Automation 22 2002 40-45.

DOI: 10.1108/01445150210416664

[7] Y. Tang, J.Y.H. Fuh, H.T. Loh, Y. S Wong and L. Lu, Direct laser sintering of silica sand, Mat. and Design 24 2003 623-629.

DOI: 10.1016/s0261-3069(03)00126-2

[8] Ferreira JC, Albes NF (2003), Integration of reverse engineering and rapid tooling in foundry technology, J of Materials processing Technology, 142, pp.374-382.

DOI: 10.1016/s0924-0136(03)00601-0

[9] J.C. Ferreira, Manufacturing core-boxes for foundry with rapid tooling technology, J of Materials Processing Technology, 155-156 2004 1118-1123.

DOI: 10.1016/j.jmatprotec.2004.04.407

[10] S. Vieira and R. Hoffmann, Estatística Experimental, Editora Atlas, São Paulo, (1989).

[11] A. Noriega, D. Blanco, B.J. Alvarez and A. Garcia, Dimensional accuracy improvement of FDM square cross-section parts using artificial neural networks and an optimization algorithm, International Journal of Advanced Manufacturing Technology 69 2013 2301-2313.

DOI: 10.1007/s00170-013-5196-2

[12] D. Chatterge, B. Oran, G. Sutradhar and P.K. Bose, Prediction of hardness for sintered HSS components using response surface method, Journal of materials processing Technology 190 2007 123-129.

DOI: 10.1016/j.jmatprotec.2007.03.069

[13] D.C. Montgomery, Design and analysis of experiments, John Wiley & Sons, New York, (1991).

[14] K.Y. Kung, J.T. Horng and K.T. Chiang, Material removal rate and electrode wear ratio study on the powder mixed electric discharge machining of cobalt-bonded tungsten carbide, "International Journal Of Advanced Manufacturing Technology 40(1-2) 2001 95-104.

DOI: 10.1007/s00170-007-1307-2

[15] S.H. Panda, M.R. Swain, K. Shaktimay and R.C. Ray, Stastical optimization of α–Amylase production by probiotic lactobacillus plantarum MTCC 1407 in submerged fermentation, Journal of Microbiology 57(2) 2007 149-155.