Acoustic modeling of micro-lattices obtained by additive manufacturing

Boulvert, Jean; Costa-Baptista, Josué; Cavalieri, Théo; Perna, Maxime; Fotsing, Edith Roland; Romero‐García, Vicente; Gabard, Gwénaël; Ross, Annie; Mardjono, Jacky; Groby, Jean‐Philippe

doi:10.1016/j.apacoust.2020.107244

Cited by 49 publications

(46 citation statements)

References 15 publications

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“…The measurements are preformed between 500 Hz and 6000 Hz. Because of the printing inherent defects, the JCAL parameters dependence on the infill factor were obtained by inverse characterization of a set of eight homogeneous samples followed by an interpolation over the infill factor scope 37 . The JCAL parameters of the characterized samples along with their interpolation (infill factor ∈ [10; 70]%), are presented in Appendix C.…”

Section: E Constraint To Be Monotonic Gradient Optimizationmentioning

confidence: 99%

Optimally graded porous material for broadband perfect absorption of sound

Boulvert

Cavalieri

Costa-Baptista

et al. 2019

Journal of Applied Physics

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This article presents a numerical optimization procedure of continuous gradient porous layer properties to achieve perfect absorption under normal incidence. This design tool is applied on a graded porous medium composed of a periodic arrangement of ordered unit cells allowing to link the effective acoustic properties to its geometry. The best microgeometry continuous gradient providing the optimal acoustic reflection and/or transmission is designed via a nonlinear conjugate gradient algorithm. The acoustic performances of the so-designed continuous graded material are discussed with respect to the optimized homogeneous, i.e. non-graded, and monotonically graded material. The numerical results show a shifting of the perfect absorption peak to lower frequencies or a widening of the perfect absorption frequency range for graded materials when compared to uniform ones. The results are validated experimentally on 3D-printed samples therefore confirming the relevance of such gradient along with the efficiency of the control of the entire design process.

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Section: E Constraint To Be Monotonic Gradient Optimizationmentioning

confidence: 99%

Optimally graded porous material for broadband perfect absorption of sound

Boulvert

Cavalieri

Costa-Baptista

et al. 2019

Journal of Applied Physics

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“…Moreover, new acoustic materials (or their prototypes) are already being developed using AM technologies, e.g. : 3D printed periodic foams [13], optimally graded porous materials [2], adaptable sound absorbers [14], acoustic metamaterials based on the Kelvin cell [15], soundabsorbing micro-lattices [16], metallic phononic crystals for use in water [17], periodic acoustic structures composed of rigid micro-rods [18] or micro-bars [19], and even micro-perforated panels [20,21] and plates with complex patterns of micro-slits [22]. Most of this research indicates great potential for the development of new acoustic materials using AM technology.…”

Section: Introductionmentioning

confidence: 99%

Reproducibility of sound-absorbing periodic porous materials using additive manufacturing technologies: Round robin study

Zieliński

Opiela

Pawłowski

et al. 2020

Additive Manufacturing

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The purpose of this work is to check if additive manufacturing technologies are suitable for reproducing porous samples designed for sound absorption. The work is an inter-laboratory test, in which the production of samples and their acoustic measurements are carried out independently by different laboratories, sharing only the same geometry codes describing agreed periodic cellular designs. Different additive manufacturing technologies and equipment are used to make samples. Although most of the results obtained from measurements performed on samples with the same cellular design are very close, it is shown that some discrepancies are due to shape and surface imperfections, or microporosity, induced by the manufacturing process. The proposed periodic cellular designs can be easily reproduced and are suitable for further benchmarking of additive manufacturing techniques for rapid prototyping of acoustic materials and metamaterials.

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“…In the past, AM techniques have been used to produce a variety of sound-absorbing materials, including Helmholtz resonators [ 29 ], straight [ 30 ], inclined [ 31 ], angled tubes [ 29 ], sound crystals [ 32 ]. Moreover, microperforated plates [ 33 ] and sounds absorbers made of micro-grids [ 34 , 35 , 36 ] have been manufactured (see also [ 26 , 34 , 37 , 38 , 39 , 40 ]). The additive manufacturing of porous absorbers has also been addressed in a rudimentary way in the literature.…”

Section: Introductionmentioning

confidence: 99%

“…Zielinski et al have performed a 3D printing process comparison with different materials using porous absorbers, revealing that the process parameters have a crucial influence on the reproducibility of acoustically effective structures [ 37 ]. Boulvert et al have presented studies on geometrical factors influencing the acoustic properties, in which mainly the gradation of porosity is realized via the infill of the porous absorbers [ 38 ]. This approach is similar to the one shown in [ 26 ], where grading the porosity via the infill alone does not fully exploit the design potential of additive manufacturing.…”

Section: Introductionmentioning

confidence: 99%

Design and Additive Manufacturing of Porous Sound Absorbers—A Machine-Learning Approach

et al. 2021

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Additive manufacturing (AM), widely known as 3D-printing, builds parts by adding material in a layer-by-layer process. This tool-less procedure enables the manufacturing of porous sound absorbers with defined geometric features, however, the connection of the acoustic behavior and the material’s micro-scale structure is only known for special cases. To bridge this gap, the work presented here employs machine-learning techniques that compute acoustic material parameters (Biot parameters) from the material’s micro-scale geometry. For this purpose, a set of test specimens is used that have been developed in earlier studies. The test specimens resemble generic absorbers by a regular lattice structure based on a bar design and allow a variety of parameter variations, such as bar width, or bar height. A set of 50 test specimens is manufactured by material extrusion (MEX) with a nozzle diameter of 0.2 and a targeted under extrusion to represent finer structures. For the training of the machine learning models, the Biot parameters are inversely identified from the manufactured specimen. Therefore, laboratory measurements of the flow resistivity and absorption coefficient are used. The resulting data is used for training two different machine learning models, an artificial neural network and a k-nearest neighbor approach. It can be shown that both models are able to predict the Biot parameters from the specimen’s micro-scale with reasonable accuracy. Moreover, the detour via the Biot parameters allows the application of the process for application cases that lie beyond the scope of the initial database, for example, the material behavior for other sound fields or frequency ranges can be predicted. This makes the process particularly useful for material design and takes a step forward in the direction of tailoring materials specific to their application.

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Acoustic modeling of micro-lattices obtained by additive manufacturing

Cited by 49 publications

References 15 publications

Optimally graded porous material for broadband perfect absorption of sound

Optimally graded porous material for broadband perfect absorption of sound

Reproducibility of sound-absorbing periodic porous materials using additive manufacturing technologies: Round robin study

Design and Additive Manufacturing of Porous Sound Absorbers—A Machine-Learning Approach

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