Roughness Parameters for Classification of As-Built AM Surfaces

One of the challenges facing the industrial adoption of additively manufactured parts is the surface roughness on the as-built part. The surface roughness of parts is frequently characterized by metrics specified by international standards organizations. However, these standards list many surface metrics that can make it unclear which to use to best describe the surface. In this work, the ability of the various surface metrics to successfully classify the as-built and post-processed surfaces is studied using linear classification models. Laser polishing via remelting and manual grinding are the post-processing techniques used to smooth the as-built surface. The ability of the linear classifier to successfully categorize the various surfaces is demonstrated, and the various surface metrics are ranked according to the strength of their individual ability to classify the surfaces. This work promotes the method as a potential way to autonomously classify as-built and laser polished surfaces.

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Objective quantification of surface roughness parameters affecting superhydrophobicity

RSC Advances ◽

10.1039/d0ra03137b ◽

2020 ◽

Vol 10 (52) ◽

pp. 31251-31260

Author(s):

Yoonkyung Cho ◽

Chung Hee Park

Keyword(s):

Image Processing ◽

Surface Roughness ◽

Roughness Parameters ◽

Image Processing Techniques ◽

Surface Roughness Parameters ◽

Processing Techniques ◽

Objective Quantification

This study proposes new optical roughness parameters that can be objectively quantified using image processing techniques, and presents an analysis of how these parameters are correlated with the degree of superhydrophobicity.

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Determination of Acrylic Denture Resin Surface Roughness by Different Processing Techniques

Iraqi Dental Journal ◽

10.26477/idj.v36i3.26 ◽

2014 ◽

Vol 36 (3) ◽

pp. 121

Author(s):

Akhlas Zeid Abood

Keyword(s):

Surface Roughness ◽

Processing Techniques

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Effectiveness of Supervised Classification Models for Hate Speech on Twitter

10.36227/techrxiv.13140281 ◽

2020 ◽

Author(s):

Kunal Srivastava ◽

Ryan Tabrizi ◽

Ayaan Rahim ◽

Lauryn Nakamitsu

Keyword(s):

Supervised Classification ◽

Hate Speech ◽

The Internet ◽

Classification Models ◽

Personal Attack ◽

Political Beliefs ◽

The Many ◽

Primary Medium ◽

Offensive Speech

<div> <div> <div> <p>Abstract </p> <p>The ceaseless connectivity imposed by the internet has made many vulnerable to offensive comments, be it their physical appearance, political beliefs, or religion. Some define hate speech as any kind of personal attack on one’s identity or beliefs. Of the many sites that grant the ability to spread such offensive speech, Twitter has arguably become the primary medium for individuals and groups to spread these hurtful comments. Such comments typically fail to be detected by Twitter’s anti-hate system and can linger online for hours before finally being taken down. Through sentiment analysis, this algorithm is able to distinguish hate speech effectively through the classification of sentiment. </p> </div> </div> </div>

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Improving surface quality in selective laser melting based tool making

Journal of Intelligent Manufacturing ◽

10.1007/s10845-021-01744-9 ◽

2021 ◽

Author(s):

Filippo Simoni ◽

Andrea Huxol ◽

Franz-Josef Villmer

Keyword(s):

Surface Roughness ◽

Additive Manufacturing ◽

Surface Quality ◽

Selective Laser Melting ◽

Melting Process ◽

Laser Remelting ◽

Laser Melting ◽

Great Cost ◽

Starting Point ◽

Processing Techniques

AbstractIn the last years, Additive Manufacturing, thanks to its capability of continuous improvements in performance and cost-efficiency, was able to partly replace and redefine well-established manufacturing processes. This research is based on the idea to achieve great cost and operational benefits especially in the field of tool making for injection molding by combining traditional and additive manufacturing in one process chain. Special attention is given to the surface quality in terms of surface roughness and its optimization directly in the Selective Laser Melting process. This article presents the possibility for a remelting process of the SLM parts as a way to optimize the surfaces of the produced parts. The influence of laser remelting on the surface roughness of the parts is analyzed while varying machine parameters like laser power and scan settings. Laser remelting with optimized parameter settings considerably improves the surface quality of SLM parts and is a great starting point for further post-processing techniques, which require a low initial value of surface roughness.

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Raw Data Processing Techniques for Material Classification of Objects in Dual Energy X-ray Baggage Inspection Systems

Radiation Physics and Chemistry ◽

10.1016/j.radphyschem.2021.109512 ◽

2021 ◽

pp. 109512

Author(s):

R. Kayalvizhi ◽

Amit kumar ◽

S. Malarvizhi ◽

Anita Topkar ◽

P. Vijayakumar

Keyword(s):

Data Processing ◽

Dual Energy ◽

Raw Data ◽

X Ray ◽

Material Classification ◽

Inspection Systems ◽

Processing Techniques

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Multivariate Analysis for the Classification of Chocolate According to its Percentage of Cocoa by Using Terahertz Time-Domain Spectroscopy (THz-TDS)

Proceedings ◽

10.3390/foods_2020-08029 ◽

2020 ◽

Vol 70 (1) ◽

pp. 109

Author(s):

Jimy Oblitas ◽

Jorge Ruiz

Keyword(s):

Machine Learning ◽

Time Domain ◽

Electromagnetic Pulse ◽

Machine Learning Algorithms ◽

Classification Models ◽

Terahertz Time Domain Spectroscopy ◽

Time Domain Spectroscopy ◽

Svm Algorithm ◽

Classification Of Images

Terahertz time-domain spectroscopy is a useful technique for determining some physical characteristics of materials, and is based on selective frequency absorption of a broad-spectrum electromagnetic pulse. In order to investigate the potential of this technology to classify cocoa percentages in chocolates, the terahertz spectra (0.5–10 THz) of five chocolate samples (50%, 60%, 70%, 80% and 90% of cocoa) were examined. The acquired data matrices were analyzed with the MATLAB 2019b application, from which the dielectric function was obtained along with the absorbance curves, and were classified by using 24 mathematical classification models, achieving differentiations of around 93% obtained by the Gaussian SVM algorithm model with a kernel scale of 0.35 and a one-against-one multiclass method. It was concluded that the combined processing and classification of images obtained from the terahertz time-domain spectroscopy and the use of machine learning algorithms can be used to successfully classify chocolates with different percentages of cocoa.

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Assessing the Functional Properties of TiZr Nanotubular Structures for Biomedical Applications, through Nano-Scratch Tests and Adhesion Force Maps

Molecules ◽

10.3390/molecules26040900 ◽

2021 ◽

Vol 26 (4) ◽

pp. 900

Author(s):

Maria Vardaki ◽

Aida Pantazi ◽

Ioana Demetrescu ◽

Marius Enachescu

Keyword(s):

Surface Roughness ◽

Functional Properties ◽

Bacterial Adhesion ◽

Biomedical Applications ◽

Adhesion Force ◽

Roughness Parameters ◽

Force Microscopy ◽

Atomic Force ◽

The Mean ◽

Scratch Tests

In this work we present the results of a functional properties assessment via Atomic Force Microscopy (AFM)-based surface morphology, surface roughness, nano-scratch tests and adhesion force maps of TiZr-based nanotubular structures. The nanostructures have been electrochemically prepared in a glycerin + 15 vol.% H2O + 0.2 M NH4F electrolyte. The AFM topography images confirmed the successful preparation of the nanotubular coatings. The Root Mean Square (RMS) and average (Ra) roughness parameters increased after anodizing, while the mean adhesion force value decreased. The prepared nanocoatings exhibited a smaller mean scratch hardness value compared to the un-coated TiZr. However, the mean hardness (H) values of the coatings highlight their potential in having reliable mechanical resistances, which along with the significant increase of the surface roughness parameters, which could help in improving the osseointegration, and also with the important decrease of the mean adhesion force, which could lead to a reduction in bacterial adhesion, are providing the nanostructures with a great potential to be used as a better alternative for Ti implants in dentistry.

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Effect of Surface Roughness on Slip Resistance of Rubber

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.189-193.1538 ◽

2011 ◽

Vol 189-193 ◽

pp. 1538-1542

Author(s):

Li Xiao Jia ◽

Yong Zhen Zhang ◽

Yong Ping Niu ◽

San Ming Du ◽

Jian Li

Keyword(s):

Surface Roughness ◽

Correlation Coefficient ◽

Correlation Coefficients ◽

Roughness Parameters ◽

Test Conditions ◽

Slip Resistance ◽

Materials Used ◽

Almost All ◽

Effect Of Surface

In order to decrease accidents of slips and falls, COFs of rubber samples with different surface roughness were measured by Brungraber Mark II. And the correlation coefficients between roughness parameters and COF were calculated. The rusults have shown that the COF increases with surface roughness and the correlation coefficient between Sq and COF is highest. In general, almost all the roughness parameters used in the study have high correlation with COF. Parameters had the highest correlation with COF depends on the materials used and test conditions.

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