Effects of various polishing processes on surface morphology, corrosion resistance, and RPD framework adaptation of SLM‐printed dental Co–Cr alloys

Abstract Purpose This in vitro study aims to compare the effects of electrolytic polishing (EP), plasma electrolytic polishing (PP), dry electropolishing (DP), and their combination on the surface characteristics, corrosion resistance, and material reduction of selective laser melting (SLM) printed dental cobalt–chromium (Co–Cr) alloy. Materials and Methods Standard samples and removable partial denture (RPD) frameworks were SLM‐printed and then polished using the following methods: mechanical polishing (MP), EP, PP, DP, PDP (PP + DP), DPP (DP + PP), and PDPP (PP + DP + PP). Surface characteristics were analyzed using optical profilometry, scanning electron microscopy (SEM), and x‐ray photoelectron spectroscopy (XPS). Corrosion resistance was assessed through electrochemical testing and ion release experiments. The metal reduction was evaluated by measuring weight loss, major connector and clasp thickness loss, and framework adaptation. The post‐processing time for the technician's fine polishing of these frameworks was recorded to further evaluate the polishing quality of the frameworks. All measurements were presented as mean ±standard deviation. Statistical data were analyzed using analysis of variance (ANOVA) and nonparametric one‐way analysis. Results The MP group exhibited the lowest roughness (0.04 ± 0.01 µm), followed by the PDPP group (0.49 ± 0.12 µm). All groups had a passivation film composition predominantly composed of Cr 2 O 3 , with a small amount of CrO 3 observed in the PDP group. PDPP exhibited minimal ion release (0.12 µg/cm 2 ) and stable Nyquist plots. PDPP can significantly reduce the technician's post‐processing time (45.75 s, p < 0.05) without compromising the adaptation of the frameworks. Conclusions PDPP forms a stable passivation film with extremely low ion release and a highly smooth, uniform surface. This significantly reduces the time required for manual post‐processing while maintaining the accuracy of RPD frameworks, making it highly suitable for large‐scale clinical framework polishing.