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Materials Science in Additive Manufacturing MAM for orthopedic bone plates: An overview

customization of generic bone plate features, paving the computational power. The training data, often obtained
way for the creation of semi-patient-specific plates with from high-fidelity simulations, must be reliable and account
remarkable efficiency . The transformative potential of for all influential factors at specific levels of resolution [102] .
[97]
this approach is further amplified when integrated with These challenges highlight the need for further research to
artificial intelligence algorithms, which can swiftly analyze ensure the robustness and transferability of ML algorithms
patient-specific data to guide design alterations. in AM.
Beyond mere design benefits, MSPI-AM extends its 7. Conclusion
promise to the domain of multi-material printing. This
capability heralds the prospect of functionally graded Bone fractures are a leading type of traumatic injury in
material design for bone plates. Lima et al. [101] , through humans and frequently necessitate the use of bone plates
their pioneering work using laser-engineered net shaping for optimal recovery. The evolution of these plates has been
(LENS), showcased the feasibility of orthopedic implants remarkable, encompassing improvements in the principles
designed with graded stiffness. Such an approach directly of healing, selection of materials, and design advancements.
addresses the long-standing challenge of stress shielding, Modern bone plates not only facilitate secondary healing
promising to enhance the longevity and efficacy of but also effectively relay mechanical stimuli to fracture
orthopedic implants. The ripple effect of such innovations segments, thereby reducing complications such as
could resonate beyond the medical sphere, influencing non-union, infections, and secondary reduction loss.
sectors such as aerospace and automotive engineering. Nonetheless, certain issues remain. A prominent concern
In essence, the MSPI-AM framework is set to is that the rigidity of metallic bone plates often surpasses
revolutionize AM design, presenting a more cohesive, that of natural bones, initiating a process known as stress
efficient, and tailored pathway to the development of high- shielding. This can lead to bone thinning and eventual
caliber, bespoke bone plates. osteoporosis. This article probes the latest progress in the
fabrication of metallic bone plates, with a specific emphasis
6.3. Optimizing bone plate manufacturing processes on AM techniques. These methods have been instrumental
The future of bone plate manufacturing in the realm of in overcoming hurdles related to material selection, design,
AM is increasingly leaning toward the integration of ML manufacturing, and post-processing.
techniques for process optimization. One of the most Recent advancements in biocompatible materials,
pressing challenges in MAM is the management of complex including β-Ti and smart alloys, have improved the crafting
thermal fields generated during the printing process. of bone plates, although challenges like implant loosening
These thermal fields can significantly vary depending persist. The exploration of biodegradable materials that
on the geometry of the part, leading to inconsistent align with bone recovery phases is ongoing, but ensuring
mechanical properties even when using the same machine their decomposition rate matches natural bone healing
and material [102] . Traditional mathematical modeling is complex. AI might provide insights into designing
approaches often fall short of capturing these complexities materials that degrade appropriately and support healing.
and can be time-consuming. In contrast, ML can offer a AM offers enhanced design flexibility, with techniques
more efficient and accurate solution by learning from such as TO and FEA enabling the production of bone plates
prior experimental data to find correlations between input with sophisticated, less rigid structures. Conventionally,
process parameters and output geometrical parameters [103] . bone plate manufacturing relied on subtractive methods
A study by Le et al. [104] utilized an ML algorithm in for mass production, but the shift to AM allows for more
conjunction with the Gurson-Tvergaard porous plasticity intricate, customizable structures. Despite the challenges in
model to predict the flexural strength of fused deposition optimizing AM settings for diverse designs, AI’s analytical
modeling bone plates made of PLA. The study found capabilities could help in fine-tuning these parameters
that ML algorithms could accurately predict mechanical to create plates with balanced rigidity and strength,
behavior, thereby reducing the time and cost associated addressing issues like stress shielding.
with experimental testing. This not only reduces the The primary objective of current metallic bone plate
time and cost associated with traditional trial-and-error research is to reduce stress shielding and potential risks
methods but also improves the accuracy of predicting linked to permanent fixtures. AM has risen as a pivotal
mechanical properties at a macro scale [105] . tool, enabling the creation of intricate designs tailored to
However, the integration of ML in the AM is not individual patients. As the field advances, AI appears well-
without challenges. The development of advanced ML positioned to further refine material and design choices
algorithms requires large, accurate datasets, and significant and fine-tune AM procedures.

Volume 2 Issue 4 (2023) 11 https://doi.org/10.36922/msam.2113