Process parameter optimization for removable partial denture frameworks manufactured by selective laser melting.

Traditional methods for fabricating removable partial denture (RPD) frameworks rely on lost-wax casting, a process with known limitations in precision and reproducibility. Additive manufacturing, particularly selective laser melting (SLM), offers a promising alternative. However, the impact of process parameters on the accuracy of 3D-printed RPD frameworks remains poorly understood. Prior research has shown that SLM can produce dental prosthetics with high detail, but no prior work had resolved how specific parameters influence accuracy in RPDs. This gap motivated the current investigation into how SLM process variables affect framework precision. Existing studies lack quantitative assessments of dimensional accuracy in RPDs made via SLM. While general knowledge about SLM in dental applications exists, its specific application to RPDs is less explored. The lack of standardized parameter settings for RPD frameworks remains a barrier to clinical adoption. This uncertainty drove the need to evaluate how design choices and process parameters influence final product accuracy.

This in vitro study aimed to determine how SLM process parameters affect the accuracy of RPD frameworks. The primary goal was to identify optimal settings for laser power, scan speed, hatch distance, and layer thickness. The study also sought to compare the accuracy of frameworks produced under different parameter conditions. By using quantitative analysis, the researchers aimed to provide a reproducible method for evaluating dimensional accuracy. The motivation stemmed from the need to replace traditional casting with a more precise and efficient manufacturing approach. The study focused on Kennedy Class II RPDs, a common clinical scenario requiring high accuracy. The goal was to ensure that frameworks met clinical standards for fit and function. The researchers aimed to demonstrate that SLM could achieve acceptable accuracy when process parameters are optimized.

The study used two software programs, CAMbridge and Magics, to design RPD frameworks with varying orientations and support structures. Twelve frameworks were printed using four different process designs, each with three replicates. Melt-pool parameters were adjusted empirically to find optimal settings for laser power, scan speed, hatch distance, and layer thickness. 3D scanning was used to capture the printed frameworks' dimensions. The original STL design was compared with the scan data using the best-fit algorithm in Geomagic software. This allowed for a quantitative assessment of dimensional accuracy. The study focused on Kennedy Class II RPDs, which have specific design challenges. The comparison of different process parameters enabled the identification of the most accurate manufacturing setup.

The study found that the optimized process parameters (P=180 W, v=1200 mm/s, h=60 μm, t=30 μm) produced the highest accuracy in RPD frameworks. The mean accuracy of these frameworks was 167 ±105 μm, which was significantly better than the other groups. The nonoptimized groups had mean accuracies ranging from 180 ±121 μm to 222 ±136 μm. The transverse orientation and interconnected support structure yielded the best results. These findings suggest that process design choices strongly influence dimensional accuracy. The optimized frameworks met clinically acceptable standards for fit and function. The comparison of different parameter sets revealed that laser power and scan speed had notable effects. The results support the potential of SLM to replace traditional casting in RPD fabrication.

The study demonstrated that SLM process parameters significantly affect the accuracy of RPD frameworks. The authors propose that optimized parameters can produce frameworks with clinically acceptable accuracy. The transverse orientation and interconnected support structure were found to be most effective. The results suggest that SLM can replace traditional lost-wax casting for RPD fabrication. The study supports the use of quantitative analysis to guide process optimization. The findings indicate that design choices influence dimensional accuracy in SLM. The authors suggest that further research could explore the long-term performance of optimized frameworks. The study concludes that SLM offers a viable alternative to conventional methods when process parameters are properly controlled.