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1.
J Mech Behav Biomed Mater ; 144: 105989, 2023 08.
Article in English | MEDLINE | ID: mdl-37369172

ABSTRACT

Graded lattice scaffolds based on rhombic dodecahedral (RD) elementary unit cell geometry were manufactured in 316L stainless steel (SS) by laser powder bed fusion (LPBF). Two different strategies based on varying strut thickness layer-by-layer in the building direction were adopted to obtain the graded scaffolds: a) decreasing strut size from core to edge to produce the dense-in (DI) structure and b) increasing strut size in the same direction to produce the dense-out (DO) structure. Both graded structures (DI and DO) were constructed with specular symmetry with respect to the central horizontal axis. Structural, mechanical, and biological characterizations were carried out to evaluate feasibility of designing appropriate biomechanical performances of graded scaffolds in the perspective of bone tissue regeneration. Results showed that mechanical behavior is governed by graded geometry, while printing parameters influence structural properties of the material such as density, textures, and crystallographic phases. The predominant failure mechanism in graded structures initiates in correspondence of thinner struts, due to high stress concentrations on strut junctions. Biological tests evidenced better proliferation of cells in the DO graded scaffold, which in turn exhibits mechanical properties close to cortical bone. The combined control of grading strategy, printing parameters and elementary unit cell geometry can enable implementing scaffolds with improved biomechanical performances for bone tissue regeneration.


Subject(s)
Bone and Bones , Prostheses and Implants , Powders , Lasers , Tissue Scaffolds/chemistry
2.
Materials (Basel) ; 12(18)2019 Sep 06.
Article in English | MEDLINE | ID: mdl-31489893

ABSTRACT

Laser Powder Bed Fusion (LPBF) technology was used to produce samples based on the Ti-6Al-4V alloy for biomedical applications. Solid-state phase transformations induced by thermal treatments were studied by neutron diffraction (ND), X-ray diffraction (XRD), scanning transmission electron microscopy (STEM) and energy-dispersive spectroscopy (EDS). Although, ND analysis is rather uncommon in such studies, this technique allowed evidencing the presence of retained ß in α' martensite of the as-produced (#AP) sample. The retained ß was not detectable by XRD analysis, nor by STEM observations. Martensite contains a high number of defects, mainly dislocations, that anneal during the thermal treatment. Element diffusion and partitioning are the main mechanisms in the α ↔ ß transformation that causes lattice expansion during heating and determines the final shape and size of phases. The retained ß phase plays a key role in the α' → ß transformation kinetics.

3.
Materials (Basel) ; 12(15)2019 Jul 24.
Article in English | MEDLINE | ID: mdl-31344794

ABSTRACT

Metal additive manufacturing is now taking the lead over traditional manufacturing techniques in applications such as aerospace and biomedicine, which are characterized by low production volumes and high levels of customization. While fulfilling these requirements is the strength of metal additive manufacturing, respecting the tight tolerances typical of the mentioned applications is a harder task to accomplish. Powder bed fusion (PBF) is a class of additive manufacturing in which layers of metal powder are fused on top of each other by a high-energy beam (laser or electron beam) according to a computer-aided design (CAD) model. The quality of raw powders for PBF affects the mechanical properties of additively manufactured parts strongly, and therefore it is crucial to avoid the presence of any source of contamination, particularly cross-contamination. In this study, the identification and quantification of cross-contamination in powders of Ti-6Al-4V and maraging steel was performed using scanning electron microscopy (SEM) and energy-dispersive spectroscopy (EDS) techniques. Experimental results showed an overall good reliability of the developed method, opening the way for applications in machine learning environments.

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