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A major advantage of additive manufacturing (AM) technologies is the ability to print customized products, which makes these technologies well suited for the orthopedic implants industry. Another advantage is the design freedom provided by AM technologies to enhance the performance of orthopedic implants. This paper presents a state-of-the-art overview of the use of AM technologies to produce orthopedic implants from lattice structures and functionally graded materials. It discusses how both techniques can improve the implants' performance significantly, from a mechanical and biological point of view. The characterization of lattice structures and the most recent finite element analysis models are explored. Additionally, recent case studies that use functionally graded materials in biomedical implants are surveyed. Finally, this paper reviews the challenges faced by these two applications and suggests future research directions required to improve their use in orthopedic implants.

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Additive manufacturing (AM) of high-strength Al alloys promises to enhance the performance of critical components related to various aerospace and automotive applications. The key advantage of AM is its ability to generate lightweight, robust, and complex shapes. However, the characteristics of the as-built parts may represent an obstacle to the satisfaction of the parts’ quality requirements. The current study investigates the influence of selective laser melting (SLM) process parameters on the quality of parts fabricated from different Al alloys. A design of experiment (DOE) was used to analyze relative density, porosity, surface roughness, and dimensional accuracy according to the interaction effect between the SLM process parameters. The results show a range of energy densities and SLM process parameters for AlSi10Mg and Al6061 alloys needed to achieve “optimum” values for each performance characteristic. A process map was developed for each material by combining the optimized range of SLM process parameters for each characteristic to ensure good quality of the as-built parts. This study is also aimed at reducing the amount of post-processing needed according to the optimal processing window detected.