scholarly journals Effect of Cut-Off, Evaluation Length, and Measurement Area in Profile and Areal Surface Texture Characterization of As-Built Metal Additive Manufactured Components

2021 ◽  
Vol 11 (11) ◽  
pp. 5089
Author(s):  
Arun Prasanth Nagalingam ◽  
Moiz Sabbir Vohra ◽  
Pulkit Kapur ◽  
Swee Hock Yeo
Keyword(s):  
Surface Texture ◽  
Maximum Diameter ◽  
Random Roughness ◽  
Standard Values ◽  
Texture Characterization ◽  
Areal Surface ◽  
Measurement Area ◽  
Metal Additive ◽  
Sem Analyses

Surface texture characterization of components built using additive manufacturing (AM) remains a challenge. The presence of various asperities and random roughness distributions across a surface poses several challenges to users in selecting an appropriate cut-off wavelength (λc), evaluation length (ln), and measurement area. This paper investigates a modified framework for surface texture characterization of AM components. First, the surface asperities in an AM component were identified through scanning electron microscope (SEM) analyses. The maximum diameter (φm) of the surface asperities were determined through image processing and were used as cut-off for surface texture evaluation. Second, another set of surface texture results were extracted using standard measurement procedures per ISO 4287, 4288, 25178-1, -2, and -3. Third, the investigative measurement framework’s effectiveness and suitability were explored by comparing the results with ISO standard results. Last, the effects of using non-standard cut-off wavelength, evaluation length, and measurement area during surface texture characterization were studied, and their percentage deviations from the standard values were discussed. The key findings prove that (a) the evaluation length could be compromised instead of cut-off, (b) measurement area must be 2.5 times the maximum asperity size present in the surface, and (c) it is possible to identify, distinguish, and evaluate specific features from the AM surface by selecting appropriate filters, thereby characterizing them specifically. The investigations and the obtained results serve as valuable data for users to select appropriate measurement settings for surface texture evaluation of AM components.

A Study on Surface Material Measures for Areal Surface Texture Measuring Instruments: Measuring Conditions for the Areal Profiling

2008 ◽  
Vol 381-382 ◽  
pp. 241-244 ◽  
Author(s):  
Kentaro Nemoto ◽  
Kazuhisa Yanagi ◽  
Masato Aketagawa ◽  
D. Kanda ◽  
I. Yoshida ◽  
...  
Keyword(s):  
Surface Texture ◽  
Measuring Instruments ◽  
Surface Material ◽  
Sampling Area ◽  
Texture Parameters ◽  
Gauge Data ◽  
Auto Regressive ◽  
Areal Surface ◽  
Measurement Area ◽  
Area Data

This paper describes the software gauge data for surface texture standard using the non-causal 2D auto-regressive model (A-R model). This model can provide with 3D irregular surface topography and intentional geometrical characteristics from specified surface texture parameters. The measurement area consists of a periodical combination of the generated sampling area data. The surface roughness parameters introduced from the gauge data on a defined evaluation area can be insensitive to size and location of the evaluation area size. Adequate measuring conditions to utilize the surface material measures were investigated and then the evaluation area and sampling distance for areal profiling by a stylus instrument were clarified.

Surface texture characterization of injection-molded pigmented plastics

2004 ◽  
Vol 44 (9) ◽  
pp. 1615-1626 ◽  
Author(s):  
Ingrid Ariño ◽  
Ulf Kleist ◽  
Gustavo Gil Barros ◽  
Per-Åke Johansson ◽  
Mikael Rigdahl
Keyword(s):  
Surface Texture ◽  
Injection Molded ◽  
Texture Characterization

Three-Dimensional Surface Texture Characterization of In Situ Simulated Erosive Tooth Wear

2021 ◽  
pp. 002203452110056
Author(s):  
A.T. Hara ◽  
D. Elkington-Stauss ◽  
P.S. Ungar ◽  
F. Lippert ◽  
G.J. Eckert ◽  
...  
Keyword(s):  
Surface Texture ◽  
Tooth Wear ◽  
Model Complexity ◽  
Erosive Tooth Wear ◽  
Surface Loss ◽  
3D Surface ◽  
Texture Characterization ◽  
3D Surface Texture

This in situ erosive tooth wear (ETW) study tested enamel 3-dimensional (3D) surface texture outcomes for the detection and differentiation of ETW lesions simulated in clinically relevant conditions. Twenty participants enrolled in this 3-arm crossover intraoral ETW simulation and wore their own partial denture for 14 d holding 2 human enamel specimens (per arm). In each arm, participants were assigned to 1 of 3 different dental erosion protocols: severe (lemon juice/pH 2.5), moderate (grapefruit juice/pH 3.5), and no erosion (bottled drinking water, control). Enamel specimens were evaluated by white-light scanning confocal profilometry for 3D surface texture and surface loss (ETW model validation). Individual point clouds were analyzed using standard dental microwear texture characterization protocols for surface roughness and anisotropy. Fractal complexity ( Asfc), texture aspect ratio ( Str), and arithmetical mean height ( Sa) values were generated at baseline, 7 d, and 14 d. Data were analyzed by analysis of variance models suitable for the crossover design with repeated measurements, and correlation coefficients were used to examine the relationship between outcomes. Asfc and Sa differentiated ETW severity (no erosion < moderate < severe, P < 0.001) at days 7 and 14. Asfc and Sa were lower at baseline compared to days 7 and 14 ( P < 0.001) for moderate and severe challenges. Asfc increased from day 7 to 14 ( P = 0.042) for the severe challenge. For Str, ETW severity did not have a significant effect overall ( P = 0.15). Asfc and Sa were highly positively correlated ( r = 0.89, P < 0.001), while Asfc and Sa were not correlated overall with Str ( r < 0.1, P ≥ 0.25). Enamel surface loss increased with ETW severity (no erosion < moderate < severe, P < 0.001) at days 7 and 14, validating the ETW simulation model. Complexity ( Asfc) and roughness ( Sa) outcomes were able to detect and differentiate ETW levels, with Asfc being able to monitor the progression of severe lesions. No clear characterization of ETW lesions could be provided by the anisotropy ( Str) parameter.

Micro scale surface texture characterization of technical structures by computer vision

Measurement ◽  
2013 ◽  
Vol 46 (6) ◽  
pp. 2022-2028 ◽  
Author(s):  
Pinar Demircioglu ◽  
Ismail Bogrekci ◽  
Numan M. Durakbasa
Keyword(s):  
Computer Vision ◽  
Surface Texture ◽  
Micro Scale ◽  
Texture Characterization ◽  
Scale Surface

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

2021 ◽  
Vol 143 (5) ◽  
Author(s):  
Shubhavardhan Ramadurga Narasimharaju ◽  
Weidong Liu ◽  
Wenhan Zeng ◽  
Tian Long See ◽  
Paul Scott ◽  
...  
Keyword(s):  
Surface Texture ◽  
Measurement Data ◽  
Particle Analysis ◽  
Inclined Surfaces ◽  
Staircase Effect ◽  
Inclination Angles ◽  
Surface Inclination ◽  
Texture Characterization ◽  
The Impact ◽  
Areal Surface

Abstract 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 artifact with inclination angles varying from 0 deg to 180 deg was built by selective laser melting. Focus variation microscopy was used to measure the topography of inclined surfaces with a tilt angle of up to 132 deg. The measurement data were then analyzed to characterize the staircase effect and the particles adherent to the artifact 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 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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