Material ratio curve of 3D surface topography of additively manufactured parts: an attempt to characterise open surface pores

Lou, Shan; Zhu, Zicheng; Zeng, Wenhan; Majewski, Candice; Scott, Paul J.; Jiang, Xiangqian

doi:10.1088/2051-672x/abedf9

Cited by 21 publications

(11 citation statements)

References 38 publications

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“…The peak material volume parameter (Vmp) was employed in this study to analyze the particle features on the truncheon surface topography. Instead of using the default Mr1 ratio of 10%, it is found that material ratio thresholds Smr1 and Smr2 derived from the Sk parameters are more suitable for the analysis of PBF topography features, e.g., particles and subsurface pores [62].…”

Section: Hybrid Parametersmentioning

confidence: 99%

Surface Texture Characterization of Metal Selective Laser Melted Part With Varying Surface Inclinations

Narasimharaju

Liu

Zeng

et al. 2021

Journal of Tribology

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Additive manufacturing offers the advantage of infinite freedom to design and fabricate complex parts at reduced lead-time. However, the surface quality of additively manufactured parts remains well behind the conventionally processed counterparts. This paper aims to systematically investigate the impact of varying surface inclination angles with respect to the build direction on the resultant surface textures. A bespoke metal truncheon artefact with inclination angles varying from 0° to 180° was built by selective laser melting. Focus variation microscopy was used to measure the topography of inclined surfaces with the tilt angle up to 132°. The measurement data was then analyzed to characterize the staircase effect and the particles adherent to the artefact surface. Areal surface texture parameters, including height parameters, spatial parameters, functional parameters, and feature parameters, were explored to quantify the general surface topography, the staircase case effect, and the particle features. The areal surface texture characterization and particle analysis reveal the resulted surface topographies are strongly correlated with the surface inclination angles.

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Section: Hybrid Parametersmentioning

confidence: 99%

Surface Texture Characterization of Metal Selective Laser Melted Part With Varying Surface Inclinations

Narasimharaju

Liu

Zeng

et al. 2021

Journal of Tribology

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“…These in situ monitoring techniques are integral to understanding the microstructural development and are used to create models that predict the process-induced porosity. In [20], the material ratio curves were studied to differentiate AM topographies and correlate volume with open surface pores. Currently, the in situ monitoring techniques help relay information to the models but do not identify porosity in real time.…”

Section: In Situ Monitoring Techniques In Lmdmentioning

confidence: 99%

A Physics-Informed Convolutional Neural Network with Custom Loss Functions for Porosity Prediction in Laser Metal Deposition

Gawade

Guo

2022

Sensors

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Physics-informed machine learning is emerging through vast methodologies and in various applications. This paper discovers physics-based custom loss functions as an implementable solution to additive manufacturing (AM). Specifically, laser metal deposition (LMD) is an AM process where a laser beam melts deposited powder, and the dissolved particles fuse to produce metal components. Porosity, or small cavities that form in this printed structure, is generally considered one of the most destructive defects in metal AM. Traditionally, computer tomography scans measure porosity. While this is useful for understanding the nature of pore formation and its characteristics, purely physics-driven models lack real-time prediction ability. Meanwhile, a purely deep learning approach to porosity prediction leaves valuable physics knowledge behind. In this paper, a hybrid model that uses both empirical and simulated LMD data is created to show how various physics-informed loss functions impact the accuracy, precision, and recall of a baseline deep learning model for porosity prediction. In particular, some versions of the physics-informed model can improve the precision of the baseline deep learning-only model (albeit at the expense of overall accuracy).

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“…S Lou et al [31] made attempt to study the 3-D surface parameter (Sa, Sq, Ssk, Svk, Sk) to better understand the surface topography of 3-D parts. The 3-D topographies shown in Figure 2d, Figure 4d, Figure 5d, and Figure 6d revealed wear track in the G1, G3, G4, and G5 specimens.…”

Section: Sem Characterisation and 3-d Profilometrymentioning

confidence: 99%

3-D Surface Morphological Characterization of CAD/CAM Milled Dental Zirconia: An In Vitro Study of the Effect of Post-Fabrication Processes

Patil

Jebaseelan

2022

Materials

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Objective: To investigate the effect on zirconia surface of the post-fabrication surface treatments on the morphological characteristics and mechanical properties of CAD/CAM milled dental zirconia specimens as well as to identify the critical parameters in the measurement of oral retention under in vitro circumstances. Method: The zirconia specimens (N = 20, n = 4) were subjected to CAD/CAM milling and divided into five groups. The specifications were: Group G1—sintered; Group G2—sintered followed by a polishing process; Group G3—sintered followed by polishing and sandblasting with alumina particles Al2O3 (110 µm); Group G4—sintered followed by sandblasting; Group G5—sintered followed by sandblasting with polishing as the end process. All the groups were subjected to Fretting wear tests, 3-D surface roughness measurements, and Vickers’s Micro hardness tests. Investigation of the phase transformation using XRD, and surface feature examination using SEM were also carried out. Additionally, one-way ANOVA, Tukey, and Pearson correlations were statistically analysed. Results: The fabrication processes had a significant effect on the performance of zirconia specimens in all the groups (p > 0.05). Specimens that underwent polishing as the last process exhibited lower surface roughness. The monoclinic phase of zirconia was observed in all the specimens before and after wear except for those in the G2 and G5 groups, where polishing was the end process. In G5, the post-wear surface properties revealed lower surface roughness and hardness. Further, the SEM and 3-D topography show grooves as seen by the dale void volume (Vvv) values; shallow valley depth (Svk); micro craters; and wear track. Conclusion: Specimens in G5 that were subjected to multistep post-fabrication process, namely sandblasting followed by polishing, yielded better results when compared to those in the other groups (G1, G2, G3, and G4). G5 with an interlayer of alumina is recommended for clinical applications due to its enhanced surface properties, mechanical properties, and low wear.

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Material ratio curve of 3D surface topography of additively manufactured parts: an attempt to characterise open surface pores

Cited by 21 publications

References 38 publications

Surface Texture Characterization of Metal Selective Laser Melted Part With Varying Surface Inclinations

Surface Texture Characterization of Metal Selective Laser Melted Part With Varying Surface Inclinations

A Physics-Informed Convolutional Neural Network with Custom Loss Functions for Porosity Prediction in Laser Metal Deposition

3-D Surface Morphological Characterization of CAD/CAM Milled Dental Zirconia: An In Vitro Study of the Effect of Post-Fabrication Processes

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