Optical roughness measurements on specially designed roughness standards

Danzl, Reinhard; Helmli, Franz; Rubert, Paul; Prantl, Manfred

doi:10.1117/12.798729

Cited by 9 publications

(4 citation statements)

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“…3D surface measurements using an optical metrology device (Infinite‐Focus, Alicona) illustrate the surface topography of a tested surface. Figures 3 and 4 show Alicona 3D surface topography of the notch surface of the tested specimen for both the nondamaged state and damaged state, respectively 31,32 . In this study, the measured surface topography parameters are the arithmetical mean height (

S_{a}

), root‐mean‐square height (

S_{q}

), maximum peak height (

S_{p}

), maximum valley depth (

S_{v}

), maximum height (

S_{z}

), and 10‐point height (

S_{z 10}

).…”

Section: Description Of the Experimental Apparatusmentioning

confidence: 99%

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Feature selection of surface topography parameters for fatigue‐damage detection using Pearson correlation method and neural network analysis

Alqahtani

Ray

2023

Fatigue Fract Eng Mat Struct

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The global objective of this study was to investigate the best features of the surface topography for fatigue‐damage detection and classification. The presence of the stress concentration in valleys of the surface topography causes a grain slip and a crack initiation at the surface of the machined structure and finally leads to fatigue failures. Therefore, the surface topography has a major influence on the fatigue strength of the machined structure. An optical confocal measurement system (Alicona) was applied to measure six surface topography parameters. In this paper, feature selection using the Pearson correlation method was adopted to select the best surface textures that provide best the neural network (NN) model performance. The NN model is capable of detecting and classifying the damage with an accuracy of up to ∼ 94.4%.

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S_{a}

), root‐mean‐square height (

S_{q}

), maximum peak height (

S_{p}

), maximum valley depth (

S_{v}

), maximum height (

S_{z}

), and 10‐point height (

S_{z 10}

).…”

Section: Description Of the Experimental Apparatusmentioning

confidence: 99%

“…In the Focus‐Variation system of Alicona, the topographical and color information are created from the variation of focus where the small depth of focus of an optical system is combined with vertical scanning. The vertical resolution of the Infinite‐Focus system reaches 20 nm 31,32 …”

Section: Description Of the Experimental Apparatusmentioning

confidence: 99%

Feature selection of surface topography parameters for fatigue‐damage detection using Pearson correlation method and neural network analysis

Alqahtani

Ray

2023

Fatigue Fract Eng Mat Struct

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“…In the experiments, Alicona images have been taken (approximately) synchronously with ultrasonic testing (UT) data in order to provide a ground truth for the results of analysis from UT signals. Since the Alicona metrology also provides information on surface topography, they have been used to measure both the surface average roughness (S α , the arithmetical mean height of a surface) and crack tip opening displacement (CTOD) [15,16].…”

Section: Optical Metrology Devicementioning

confidence: 99%

Neural Network-Based Automated Assessment of Fatigue Damage in Mechanical Structures

Alqahtani

Ray

2020

Machines

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This paper proposes a methodology for automated assessment of fatigue damage, which has been tested and validated with polycrystalline-alloy (Aℓ7075-T6) specimens on an experimental apparatus. Based on an ensemble of time series of ultrasonic test (UT) data, the proposed procedure is found to be capable of detecting fatigue-damage (at an early stage) in mechanical structures, which is followed by online evaluation of the associated risk. The underlying concept is built upon two neural network (NN)-based models, where the first NN model identifies the feature of the UT data belonging to one of the two classes: undamaged structure and damaged structure, and the second NN model further classifies an identified damaged structure into three classes: low-risk, medium-risk, and high-risk. The input information to the second NN model is the crack tip opening displacement (CTOD), which is computed by the first NN model via linear regression from an ensemble of optical data, acquired from the experiments. Both NN models have been trained by using scaled conjugate gradient algorithms. The results show that the first NN model classifies the energy of UT signals with (up to) 98.5% accuracy, and that the accuracy of the second NN model is 94.6%.

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“…As a result, it faithfully reproduces the amplitude of the primary profile. Moreover, the optical measurement device InfiniteFocus by Alicona generates very few discontinuities as it measured a 3D dataset using the focus variation technology, and the larger amount of data used for calculation gives representative and repeatable results (Danzl et al 2008).…”

Section: Topographic Parameters Explaining the Tactile Sensation Of Rmentioning

confidence: 99%

Visuo-tactile and topographic characterizations of finished wood surface quality by French consumers and industrials: acceptability thresholds for raised grain

Ramanakoto

Ramananantoandro

Eyma

et al. 2019

Annals of Forest Science

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& Key message Raised grain occurring on wood surfaces after the application of a waterborne varnish was felt by human touch because of protruding peaks and a certain amount of materials in the core of the roughness profile. This tactile sensation was correlated with specific roughness parameters. Characteristics of a finished surface quality that is acceptable to consumers were determined. & Context Raised grain occurs on wood surfaces after the application of a waterborne varnish and forces manufacturers to sand the surfaces between coats. Actually, little research has characterised this phenomenon and no techniques have been discovered to avoid its occurrence. & Aims This study aims to identify the topographic parameters that explain the visuo-tactile sensation of raised grain and to define a finished surface quality acceptable to consumers and industry. & Methods Oak (Quercus robur L.) and beech (Fagus sylvatica L.) wood surfaces were planed and sanded in order to have various levels of raised grain. Visuo-tactile analyses were carried out on surfaces having received one coat of varnish to characterise raised grain and having two coats to characterise the acceptable finished surface quality without sanding. Topographic parameters were measured on each type of varnished surface and correlated with the visuo-tactile scores. & Results Raised grain was characterised by the visuo-tactile sensation of protruding peaks and a certain amount of material in the core of the roughness profile for both wood species. Industrials overestimated the surface quality required by consumers. Thresholds of topographic parameters were determined to define acceptable finished surface quality. & Conclusion These findings allowed objective criteria to be defined for describing raised grain and to help industries to optimise their wood machining and finishing processes.

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Optical roughness measurements on specially designed roughness standards

Cited by 9 publications

References 0 publications

Feature selection of surface topography parameters for fatigue‐damage detection using Pearson correlation method and neural network analysis

Feature selection of surface topography parameters for fatigue‐damage detection using Pearson correlation method and neural network analysis

Neural Network-Based Automated Assessment of Fatigue Damage in Mechanical Structures

Visuo-tactile and topographic characterizations of finished wood surface quality by French consumers and industrials: acceptability thresholds for raised grain

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