Inverse engineering of micro-perforated plates for targeted acoustic characteristics.
Saved in:
| Title: | Inverse engineering of micro-perforated plates for targeted acoustic characteristics. |
|---|---|
| Authors: | Yuan, Binxia1 yuanbinxia100@163.com, Li, Xiangyang1, You, Tianqi1,2, Chen, Tianzhong3, Zhu, Rui1 zhuruish@163.com |
| Source: | Sound & Vibration. 2026, Vol. 60 Issue 3, p1-17. 17p. |
| Subjects: | Inverse problems, Artificial neural networks, Machine learning, Deep learning, Absorption of sound |
| Abstract: | The inverse design of micro-perforated panels (MPPs) for target sound absorption remains challenging due to the complex nonlinear relationship between structural parameters and acoustic performance. This study proposes a tandem neural network (TNN) framework to achieve efficient inverse design of single-layer MPPs. A forward multi-layer perceptron (MLP) is first trained to accurately predict the absorption coefficient curve from three key structural parameters: perforation diameter, panel thickness, and cavity depth. The forward model achieves superior accuracy compared to GA-SVR, GridSearch-SVR, and random forest models, with an R2 of 0.999 and MAE of 0.007. Subsequently, an inverse design network is connected in series with the frozen forward network, taking a target absorption curve as input and outputting the corresponding structural parameters. The activation function of the output layer constrains the parameters within physically feasible ranges. The framework is validated by designing an MPP with a distinct absorption peak in the 300-600 Hz range. The predicted parameters (diameter 0.93 mm, thickness 0.9 mm, cavity depth 9.9 mm) yield an absorption curve that matches the target with an R2 of 0.997. This work demonstrates that deep learning can effectively automate the inverse design of MPPs, offering a flexible and efficient alternative to traditional trial-and-error methods. [ABSTRACT FROM AUTHOR] |
| Copyright of Sound & Vibration is the property of Academic Publishing and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
|---|---|
| Header | DbId: egs DbLabel: Engineering Source An: 195090138 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
| IllustrationInfo | |
| Items | – Name: Title Label: Title Group: Ti Data: Inverse engineering of micro-perforated plates for targeted acoustic characteristics. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Yuan%2C+Binxia%22">Yuan, Binxia</searchLink><relatesTo>1</relatesTo><i> yuanbinxia100@163.com</i><br /><searchLink fieldCode="AR" term="%22Li%2C+Xiangyang%22">Li, Xiangyang</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22You%2C+Tianqi%22">You, Tianqi</searchLink><relatesTo>1,2</relatesTo><br /><searchLink fieldCode="AR" term="%22Chen%2C+Tianzhong%22">Chen, Tianzhong</searchLink><relatesTo>3</relatesTo><br /><searchLink fieldCode="AR" term="%22Zhu%2C+Rui%22">Zhu, Rui</searchLink><relatesTo>1</relatesTo><i> zhuruish@163.com</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Sound+%26+Vibration%22">Sound & Vibration</searchLink>. 2026, Vol. 60 Issue 3, p1-17. 17p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Inverse+problems%22">Inverse problems</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+neural+networks%22">Artificial neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink><br /><searchLink fieldCode="DE" term="%22Absorption+of+sound%22">Absorption of sound</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: The inverse design of micro-perforated panels (MPPs) for target sound absorption remains challenging due to the complex nonlinear relationship between structural parameters and acoustic performance. This study proposes a tandem neural network (TNN) framework to achieve efficient inverse design of single-layer MPPs. A forward multi-layer perceptron (MLP) is first trained to accurately predict the absorption coefficient curve from three key structural parameters: perforation diameter, panel thickness, and cavity depth. The forward model achieves superior accuracy compared to GA-SVR, GridSearch-SVR, and random forest models, with an R2 of 0.999 and MAE of 0.007. Subsequently, an inverse design network is connected in series with the frozen forward network, taking a target absorption curve as input and outputting the corresponding structural parameters. The activation function of the output layer constrains the parameters within physically feasible ranges. The framework is validated by designing an MPP with a distinct absorption peak in the 300-600 Hz range. The predicted parameters (diameter 0.93 mm, thickness 0.9 mm, cavity depth 9.9 mm) yield an absorption curve that matches the target with an R2 of 0.997. This work demonstrates that deep learning can effectively automate the inverse design of MPPs, offering a flexible and efficient alternative to traditional trial-and-error methods. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Sound & Vibration is the property of Academic Publishing and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=195090138 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.59400/sv3908 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 17 StartPage: 1 Subjects: – SubjectFull: Inverse problems Type: general – SubjectFull: Artificial neural networks Type: general – SubjectFull: Machine learning Type: general – SubjectFull: Deep learning Type: general – SubjectFull: Absorption of sound Type: general Titles: – TitleFull: Inverse engineering of micro-perforated plates for targeted acoustic characteristics. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Yuan, Binxia – PersonEntity: Name: NameFull: Li, Xiangyang – PersonEntity: Name: NameFull: You, Tianqi – PersonEntity: Name: NameFull: Chen, Tianzhong – PersonEntity: Name: NameFull: Zhu, Rui IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 05 Text: 2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 15410161 Numbering: – Type: volume Value: 60 – Type: issue Value: 3 Titles: – TitleFull: Sound & Vibration Type: main |
| ResultId | 1 |
