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- MakaleYalnız künye2026Scopus SJRQ4SURFACE ROUGHNESS AND SHORE-D HARDNESS OF SIX TROPICAL HARDWOODS; [RUGOSIDADE DA SUPERFÍCIE E DUREZA SHORE-D DE SEIS MADEIRAS TROPICAIS]; [RUGOSIDAD SUPERFICIAL Y DUREZA SHORE-D DE SEIS MADERAS TROPICALES]
Ayata, Umit; Domingos, Idalina; Ferreira, José; Cruz-Lopes, Luísa; Esteves, Bruno
Introduction: This study investigates the Shore-D hardness and surface roughness characteristics of six tropical wood species: Canelo (Nectandra spp.), Limbali (Gibertiodendron dewevrei), Difou (Morus mesozygia), Curupay (Anadenanthera macrocarpa), Ebiara (Berlinia bracteosa), and Zwarte kabbes (Diplotropis martiusii), aiming to assess their suitability for high-performance applications. Objective: To evaluate how wood species and sanding processes influence the mechanical properties and surface quality of selected tropical woods. Methods: Samples were conditioned and sanded using abrasives of different grit sizes (80, 100, 120, 150, and 180). Surface roughness parameters Ra, Rq, and Rz, as well as Shore-D hardness, were measured. Statistical analyses were performed using ANOVA and Duncan’s test. Results: Statistical analysis revealed that abrasive grit size significantly affected surface roughness (p < 0.001), with finer grits producing smoother surfaces. Wood species also had a significant effect on both roughness and hardness. Sanding had the greatest influence on Ra, Rq, and Rz parameters. Ebiara exhibited the smoothest surface, while Curupay showed the highest hardness (74.65). Conclusion: Surface roughness is primarily influenced by sanding grit, whereas hardness is determined by wood species. The results emphasize the importance of tailoring sanding procedures to the wood species in order to optimize surface quality and mechanical performance in industrial and decorative applications.
- MakaleAçık Erişim2024Scopus SJRQ2A Numerical Study of Topography and Roughness of Sloped Surfaces Using Process Simulation Data for Laser Powder Bed Fusion
Aydogan, Beytullah; Chou, Kevin
The simulation of additive manufacturing has become a prominent research area in the past decade. Process physics simulations are employed to replicate laser powder bed fusion (L-PBF) manufacturing processes, aiming to predict potential issues through simulated data. This study focuses on calculating surface roughness by utilizing 3D surface topology extracted from simulated data, as surface roughness significantly influences part quality. Accurately predicting surface roughness using a simulation remains a persistent challenge. To address this challenge, the L-PBF technique with two different cases (pre- and post-contouring) was simulated using two-step process physics simulations. The discrete element method was utilized to simulate powder spreading, followed by the Flow-3D melting simulation. Ten layers were simulated at three different linear energy density (LED) combinations for both cases, with samples positioned at a 30-degree angle to accommodate upskin and downskin effects. Furthermore, a three-dimensional representation of the melted region for each layer was generated using the thermal gradient output from the simulated data. All generated 3D layers were stacked and merged to consolidate a 3D representation of the overall sample. The surfaces (upskin, downskin, and side skins) were extracted from this merged sample. Subsequently, these surfaces were analyzed, and surface roughness (Sa values) was calculated using MATLAB. The obtained values were then compared with experimental results. The downskin surface roughness results from the simulation were found to be within the range of the experimental results. This alignment is attributed to the fact that the physics simulation primarily focuses on melt pool depth and width. These promising findings indicate the potential for accurately predicting surface roughness through simulation.
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