Compression Behavior and Fatigue Strength of Additively Manufactured Ti6Al4V Strut-Based Lattice Structures: A Review
摘要
Additive Manufacturing (AM) paved the way for developing and fabricating intricate porous structures with tailor-made properties. AM allows the designing of complex models, which would otherwise be difficult to manufacture using conventional methods. Laser powder bed fusion and electron beam melting are the well-known broad categories of AM techniques for printing metals and alloys. Porous structures are highly preferred for ingesting human tissues and bones, which becomes possible with AM. This review discusses several lattice structures constituting Ti6Al4V, flaws during AM, surface morphology and mechanical properties such as compressive strength and fatigue life. This discussion focuses on Cubic, Diamond, and BCC structures, as researchers have used them most frequently in the past decade. Other lattice structures are also being explored and compared. When considering relative density as the comparison factor, the compression strength of Ti-6Al-4 V lattice structures follows the order: FCC > SC > BCC > Star > Octahedron > Tetrahedron > Diamond. The high fatigue strength of the simple cubic structure, approximately 80% of its yield strength, is attributed to its geometry, with straight, load-aligned struts that ensure uniform load distribution and minimize stress concentrations. Its simple design reduces weak points, enhancing fatigue performance compared to more complex geometries like BCC, FCC, and diamond, which tend to have uneven stress distribution, various deformation modes and localized areas of high stress during cyclic loading, leading to earlier fatigue failure. This review provides a guide for numerical analysis under static and fatigue conditions, highlighting the procedure, including model setup, material properties, boundary conditions, and validation. The study emphasizes validating simulation results with experimental data to ensure accurate lattice structures static and dynamic performance predictions.