Modeling the sound absorption behavior of carpets using artificial intelligence

Paknejad, Seyed Hassan; Vadood, Morteza; Soltani, Parham; Ghane, Mohammad

doi:10.1080/00405000.2020.1841954

Cited by 25 publications

(6 citation statements)

References 39 publications

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“…Figure 2 schematically shows a chromosome that includes the mentioned items. According to the literature [14][15][16][17][18][19], in this study, it was decided to use a neural network with a maximum of two hidden layers. The number of neurons in each layer was considered up to 10 neurons.…”

Section: Optimizing the Ann With Nsgaiimentioning

confidence: 99%

“…Artificial intelligence is currently used in numerous fields to find the nonlinear and complex relationships between the independent and dependent parameters of engineering systems. In the textile industry, many works have been published reporting the success of using artificial intelligence to predict or optimize the physical and mechanical properties of textiles [10][11][12][13][14][15][16][17][18][19]. However, to the best of the authors' knowledge, no paper has been published regarding the prediction of bending rigidity and formability under the influence of plasma treatment.…”

Section: Introductionmentioning

confidence: 99%

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Predicting the Bending Rigidity and Formability of Plasma-Treated Spunbond Nonwoven Fabrics Using Artificial Intelligence

Sharifi,

Vadood,

Haji

2024

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Nonwoven fabrics are used in many industries, and surface treatment by plasma can significantly change their physical and mechanical properties by changing the surface chemistry and morphology. In this paper, oxygen/argon plasma has been applied to the spunbond nonwoven fabrics to predict the obtained properties. Therefore, by using the central composite design and considering 4 independent factors including the fabric weight, fabric direction, plasma treatment duration and oxygen ratio, 51 various samples were prepared and their bending rigidity and formability were measured. SEM images showed that the surface roughness increases due to the plasma treatment. Statistical analysis revealed that all the mentioned independent factors have a significant effect on the measured parameters directly or reciprocally. Also, the use of a neural network model with two hidden layers optimized with a method according to the genetic algorithm can predict the bending rigidity and formability based on the independent factors with errors of less than 7% and 9%, respectively. The errors resulting from the surface response method for the same parameters with the same order are about 19% and 17%. The introduced method can be used as a tool to help researchers in the field of mechanical properties.

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Section: Optimizing the Ann With Nsgaiimentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Predicting the Bending Rigidity and Formability of Plasma-Treated Spunbond Nonwoven Fabrics Using Artificial Intelligence

Sharifi,

Vadood,

Haji

2024

Text Leather Rev

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“…Sonuç olarak kalınlık kaybının istatistiksel olarak anlamlı olduğu ve hareket sayısı arttıkça kalınlık kaybının olumsuz yönde etkilendiği sonucuna varmıştır [8]. Paknejad (2021), yaptığı çalışmada sıkıştırma ve sıkıştırma sonrasında elde edilen geri kazanım değerlerinin temel olarak zemin tekstilinin yapısına ve ipliklerin fiziksel ve mekanik özelliklerine bağlı olduğunu ifade etmektedir [9]. Akgün (2021), yaptığı çalışmada farklı özellikte atkı iplikleri kullanarak jakarlı dokunmuş tekstil materyalleri üretmiş ve bu ipliklerin aşınma dayanımı üzerindeki etkilerini incelemişlerdir.…”

Section: Introductionunclassified

Farkli Fi̇lament Sayisina Sahi̇p Polyester İpli̇kleri̇n Dokuma Zemi̇n Kaplamalari Üzeri̇ndeki̇ Etki̇si̇ni̇n İncelenmesi̇

SEKMEN

2023

Tekstil Ve Mühendis

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Geleceğe yönelik zemin kaplamaları olarak anılmaya başlanan hafif dokuma zemin kaplamaları özellikle tozu içine hapsedecek hav tabakası bulundurmadığı için alerjenler/ev tozu akarları bulundurmazlar. Bu çalışmada atkı ipliği olarak kullanılacak polyester ipliğin filament sayısını belirleyebilmek ve bu parametrenin dokuma tipi zemin kaplamaları üzerindeki mekanik etkilerini incelemek amacı ile aynı üretim parametrelerinde numuneler ürettirilmiştir. Bu numunelerden, test numuneleri alınarak her birine sıkıştırılabilirlik ve rezilyans tayini, dinamik yük altında kalınlık kaybı tayini, uzun süreli statik yük altında kalınlık kaybı tayini, aşınma dayanımı tayini, kopma mukavemeti ve uzaması tayini testleri yapılmıştır. Çalışma sonuçlarına göre, atkı ipliği olarak farklı filament sayılarına sahip atkı iplikleri ile dokunan özdeş zemin kaplamaları dokunmuştur. İplikteki filament değeri 384 filament sayısından 96 filament sayısına düştükçe dokunun mekanik performansının arttığı görülmüştür.

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“…Ciaburro et al [33] predicted the sound absorption coefficient of electrospun polyvinylpyrrolidone/silica composites based on an ANN. Paknejad et al [34] used an ANN, the adaptive neural-fuzzy inference system (ANFIS) and a genetic algorithm (GA) to predict the sound absorption coefficient of acrylic carpets at different frequencies, and the applicability and performance of the ANN-GA hybrid model in the prediction of carpet piles were verified. Nevertheless, although deep learning has achieved certain results in the field of acoustic research, there are still few studies on the application of the acoustic performance of cavity sound-absorbing overlays; in particular, research on the inversion of sound-absorbing coating parameters and the prediction of acoustic performance according to the expected acoustic performance targets has not yet been published.…”

Section: Introductionmentioning

confidence: 99%

A High-Efficiency Inversion Method for the Material Parameters of an Alberich-Type Sound Absorption Coating Based on a Deep Learning Model

Sun¹,

Chen²,

Bai³

et al. 2022

Computer Modeling in Engineering &Amp; Sciences

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Research on the acoustic performance of an anechoic coating composed of cavities in a viscoelastic material has recently become an area of great interest. Traditional forward research methods are unable to manipulate sound waves accurately and effectively, are difficult to analyse, have time-consuming solution processes, and have large optimization search spaces. To address these issues, this paper proposes a deep learning-based inverse research method to efficiently invert the material parameters of Alberich-type sound absorption coatings and rapidly predict their acoustic performance. First, an autoencoder (AE) model is pretrained to reconstruct the viscoelastic material parameters of an Alberich-type sound absorption coating, the material parameters are extracted into the latent feature space by the encoder, and the decoder model is saved. The internal relationship between the reflection coefficient and latent feature space is trained to establish a multilayer perceptron (MLP). Then, the reflection coefficients in the test set are input to the trained MLP and decoder models to automatically invert the material parameters. The accuracy of the inversion result is 95.08%. Finally, a predictive model is trained to rapidly predict the acoustic performance of the inverted material parameters. The speed of a single test target is 80 times faster than that of the finite element method (FEM). Furthermore, sound absorber material parameters with the best sound absorption performance and a three-band sound absorber are inverted, and their actual sound absorption performance is predicted by the proposed method. The proposed deep learning-based inversion research method provides a solution for low-frequency, wide-band, strong attenuation, and precisely controlled sound waves. It achieves an efficient inversion of material parameters and the rapid forecasting of acoustic performance. The training model can be used for a sound absorbing coating composed of irregular cavities in a viscoelastic material and predict its acoustic performance by only modifying the dataset.

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Modeling the sound absorption behavior of carpets using artificial intelligence

Cited by 25 publications

References 39 publications

Predicting the Bending Rigidity and Formability of Plasma-Treated Spunbond Nonwoven Fabrics Using Artificial Intelligence

Predicting the Bending Rigidity and Formability of Plasma-Treated Spunbond Nonwoven Fabrics Using Artificial Intelligence

Farkli Fi̇lament Sayisina Sahi̇p Polyester İpli̇kleri̇n Dokuma Zemi̇n Kaplamalari Üzeri̇ndeki̇ Etki̇si̇ni̇n İncelenmesi̇

A High-Efficiency Inversion Method for the Material Parameters of an Alberich-Type Sound Absorption Coating Based on a Deep Learning Model

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