Personalized Ti6Al4V implant abutment fabricated by hybrid laser powder bed fusion process: Mechanical and microstructural perspectives
Source abstract
Abstract
This study investigates the mechanical and microstructural characteristics of customized Ti6Al4V dental abutments fabricated using a hybrid laser powder bed fusion (LPBF) process combined with a machined titanium preform. Customized anterior and posterior abutments were designed and produced. Internal defects were evaluated using high-resolution X-ray micro-computed tomography (micro-CT), while microstructural analysis employed scanning electron microscopy (SEM), electron probe microanalysis (EPMA), and electron backscatter diffraction (EBSD). Hardness distributions were measured across the LPBF region, heat-affected zone (HAZ), and preform substrate. Micro-CT revealed low porosity levels in both geometries, with void fractions of 0.045% in anterior and 0.019% in posterior abutments, and mean pore diameters of 23.2 μm and 25.8 μm, respectively. SEM confirmed a continuous metallurgical bond at the preform interface without visible cracking or delamination, while EBSD identified three distinct zones, which are fine acicular α′ martensite in the LPBF region, transitional HAZ, and equiaxed α+β grains in the preform substrate. EPMA mapping indicated vanadium heterogeneity within the preform substrate and a more homogenized vanadium distribution in the fusion zone. Microhardness testing showed gradient values from 415 HV in the LPBF zone to 360 HV in the preform, with the HAZ averaging approximately 370 HV. The present findings reveal a strong metallurgical bond between the preform substrate and the LPBF region, demonstrating the potential use of the LPBF process for personalized implant abutments.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Dental Implant Techniques and Outcomes · Bone Tissue Engineering Materials · Additive Manufacturing Materials and Processes
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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