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Effects of laser parameters and scanning strategy on structural and mechanical properties of 3D NiTi implants fabricated with selective laser melting

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Effects of laser parameters and scanning strategy on structural and

mechani-cal properties of 3D NiTi implants fabricated with selective laser melting

de Wild M

1

, Schollbach T

1

, Schumacher R

1

, Schkommodau E

1

, Bormann T

1

1

Institute for Medical and Analytical Technologies, School of Life Sciences, University of Applied Sciences

Northwestern Switzerland, 4032 Muttenz, Switzerland

Michael.deWild@fhnw.ch

Abstract:

The additive manufacturing method Selective Laser Melt-ing (SLM) allows the creation of open-porous shape memory structure directly from CAD data. However, during the SLM process, laser parameters and scanning strategy must be carefully selected. In this study, we show that they have a significant effect on geometrical and mechanical properties of NiTi scaffolds for the treatment of large bone defects.

Keywords: NiTi, shape memory alloy, SLM, scanning

strategy, laser parameters

Introduction

Implant morphology, biocompatibility and mechanical properties are strongly influenced by the SLM manufac-turing process. This process is controlled by a variety of parameters which are stored in a material file: scanning strategy, laser energy, exposure time, point distance, hatch distance, minimal hatch length, fill contour offset, lens position, energy density, etc. The geometry, mechan-ical property and microscopic structure strongly depend on these settings [1]. The µCT and mechanical investiga-tion give insight into the structural characteristics of the SLM processed scaffolds [2].

Methods

The NiTi scaffolds with a rhombo-dodecahedral unit cell and strut thicknesses around 300 µm (see fig. 1A) are produced with a Realizer 100 system (Borchen, Germany) equipped with a 100 W fiber laser [3]. The compressive strength was measured by a Zwick/Roell Z100 testing machine. The mass of the open-porous scaffolds was determined gravimetrically (Acculab). The materials porosity, defined as closed micro-pores within the metal-lic struts, was determined by analysing the 3D dataset obtained by a SkyScan 1172 µCT system with the CTAn software (SkyScan NV, Belgium), see fig. 1B.

Results

The scaffolds mass and therefore the overall porosity are found in the range of 0.145–0.366 g, resp. 71.6–88.7% when applying different laser parameters and scanning strategies. As expected, the compressive strength of the scaffolds is highly correlated to the structures mass, see fig 2A. The tomographic analyses reveal slightly more micro-pores in heavier scaffolds, see fig. 2B.

Discussion

The SLM process allows the creation of open-porous NiTi scaffolds with porosity up to 88%. The lattice mate-rial itself is very dense (density > 99.8%). The laser pa-rameter and scanning strategy have to be considered as they affect the geometry and mechanical properties. These parameters were identified in order to improve the implant design and fabrication.

Figure 1: A) metallographic section of the NiTi scaffold. B) Cross section of reconstructed µCT dataset.

Figure 2: Correlation between the scaffold mass and A) the mechanical strength, B) the materials porosity.

Acknowledgement

The multi-disciplinary team gratefully acknowledges the financial support of the Swiss National Science Founda-tion within the program NRP 62 ‘Smart Materials‘.

Bibliography

[1] Stamp, R. et. al.: The development of a scanning strate-gy for the manufacture of porous biomaterials by selec-tive laser melting, J Mater Sci Mater Med, vol. 20, pp. 1839-1848, September 2009.

[2] Lin, C.Y. et. al.: Structural and mechanical evaluations of a topology optimized titanium interbody fusion cage fabricated by selective laser melting process, J Biomed

Mater Res A, vol. 88, pp. 272-279, November 2007.

[3] Bormann, T., et al.: Tailoring Selective Laser Melting Process Parameters for NiTi Implants, J Mat Eng Perf, vol. 21, pp. 2519-2514, July 2012.

Figure

Figure 1: A) metallographic section of the NiTi scaffold. B)  Cross section of reconstructed µCT dataset.

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