Frictional Sound Analysis by Simulating the Human Arm Movement

Yosouf, Khaldon; Latroch, Hadj; Schacher, Laurence; Adolphe, Dominique; Drean, Emilie; Zimpfer, Véronique

doi:10.1515/aut-2015-0033

Cited by 5 publications

(9 citation statements)

References 13 publications

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“…Two types of tests were conducted: acoustic measurements of the friction sound and measurements of the fabric properties thanks to the KES-FB, 11 limited to compression and surface characterizations in accordance with Yosouf. 14…”

Section: Materials and Methods Samplesmentioning

confidence: 99%

“…The motion device. To evaluate the intensity and the nature of the sound generated when two similar fabrics rub together, a device developed by Yosouf et al 11 in a previous study was used. It is composed of a mechanism reproducing the human arm motion when a person is walking and a system to record the friction sound.…”

Section: Acoustic Measurementmentioning

confidence: 99%

“…The duration of tests was around 20 seconds and some parameters, which were experimentally fixed according to human arm movement measured on volunteers, 11 are constant, such as the speed or the motion arm movement angle of scanning. The pressure is also fixed and controlled by a FlexiForce type A401 sensor with a circular sensing area of 25.4 mm in diameter and thickness of 0.21 mm.…”

Section: Acoustic Measurementmentioning

confidence: 99%

“…The influence of the weave pattern of unbleached cotton on the sound properties was analyzed as well as the correlation between some mechanical properties and the total sound level of the friction noise. 11 The mechanical properties represent the surface condition and the compression of fabrics. They were measured by the Kawabata Evaluation System (KES).…”

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confidence: 99%

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Modeling of wear sound production based on mechanical and friction properties of woven fabric

Leclinche

Adolphe²,

Drean³

et al. 2018

Textile Research Journal

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The objective of this study is to analyze the characteristics of frictional sounds and to determine correlations between sound parameters and mechanical properties of fabrics classically used for protective garments. The set of samples includes six fabrics whose surface was previously worn out at different degrees using a Martindale abrasion tester. The frictional sounds were obtained by a device reproducing the friction engendered under arm during walking and were recorded and analyzed using Fast Fourier Transform (FFT) analysis. In addition, the mechanical properties of samples were measured using the Kawabata Evaluation System. The FFT spectra of fabrics showed different shapes and different noise levels according to the degree of wear. Several correlations between sound parameters and mechanical properties were highlighted. It was shown that the garment's wear increases the sound level of rustling sounds.

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Section: Materials and Methods Samplesmentioning

confidence: 99%

Section: Acoustic Measurementmentioning

confidence: 99%

Section: Acoustic Measurementmentioning

confidence: 99%

mentioning

confidence: 99%

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Modeling of wear sound production based on mechanical and friction properties of woven fabric

Leclinche

Adolphe²,

Drean³

et al. 2018

Textile Research Journal

Self Cite

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“…13 Others found a correlation between Zwicker's psychoacoustic or physiological responses and the mechanical properties of the fabric when rubbing the fabric or the garment fabrics rubbed during walking, jogging, or running. 14,15 Kim et al. 15 compared the loudness of friction sounds based on the fabric weave and yarn type through the FFT sound spectrums of seven types of silk fabrics in association with Zwicker's psychoacoustic loudness and roughness.…”

mentioning

confidence: 99%

Indexing surface smoothness and fiber softness by sound frequency analysis for textile clustering and classification

Kim

Youn²,

Choi

et al. 2020

Textile Research Journal

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Cutting-edge technology is being used in the fashion industry for three-dimensional (3D) virtual fitting programs to meet the demand for clothing manufacturing as well as textile simulating. For expanding the textile choices of the program users, this research looks at the indexation of tactile sensations, the texture of fabrics, which has been subjectively evaluated by the human hand. Firstly, this study objectively measured and indexed the surface smoothness and fiber softness of 749 fabrics through a tissue softness analyzer that mimics human hands. Secondly, after statistical analyses of the drape coefficient, each bending distance and Young's modulus for the initial tensile strength in the warp–weft directions, the thickness, and the weight of the fabrics, it was found that drape (Pearson coefficient = 0.532) and bending properties are the key factors in the fabric surface smoothness (TS750), while the fiber softness (TS7) showed a weak correlation with thickness (Pearson coefficient = 0.364), followed by the log value of the Young's modulus in the weft direction. Thirdly, we classified nine clusters for TS750 based on the 11 regression variables with significant Pearson coefficients, and characterized each cluster in order of surface smoothness (TS750) after Duncan post-hoc tests and analyses of variance (all statistically significant, p < 0.01) with microscopic surface images of one sample for each cluster. For precise TS750 classification, we finally trained the 267 samples with the same 11 variables, resulting in 93.3% prediction through an artificial neural network with multiple hidden layers. This prediction with Fisher discriminants for the clusters will enable the 3D virtual program users to predict further clustering of newly added fabrics.

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Particle release and transport from human skin and clothing: A CFD modeling methodology

2021

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Particle release from human skin and clothing has been identified as an important contributor to particulate matter burden indoors. However, knowledge about modeling the coarse particle release from skin and clothing is limited. This study developed a new empirically validated CFD modeling methodology for particle release and transport from seated occupants in an office setting. We tested three modeling approaches for particle emissions: Uniform; Uniform + Localized; and Uniform + Localized with Body Motion; applied to four office scenarios involving a single occupant and two occupants facing each other at 1‐ and 2‐m distances. Uniform particle emissions from skin and clothing underpredicted personal inhalation exposure by as much as 55%–80%. Combining uniform with localized emissions from the armpits drastically reduced the error margin to <10%. However, this modeling approach heavily underestimated particle mass exchange (cross‐contamination) between the occupants. Accounting for the occupant's body motion—by applying the momentum theory method—yielded the most accurate personal exposure and cross‐contamination results, with errors below 12%. The study suggests that for accurate modeling of particle release and transport from seated occupants indoors, localized body emissions in combination with simplified bodily movements need to be taken into account.

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Frictional Sound Analysis by Simulating the Human Arm Movement

Cited by 5 publications

References 13 publications

Modeling of wear sound production based on mechanical and friction properties of woven fabric

Modeling of wear sound production based on mechanical and friction properties of woven fabric

Indexing surface smoothness and fiber softness by sound frequency analysis for textile clustering and classification

Particle release and transport from human skin and clothing: A CFD modeling methodology

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