Design, Experimental and Numerical Characterization of 3D-Printed Porous Absorbers

Ring, Tobias P.; Langer, Sabine

doi:10.3390/ma12203397

Cited by 12 publications

(15 citation statements)

References 35 publications

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“…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: 95%

“…In this way, for example, undercuts, mesoscopic lattice structures, or free-form surfaces can be realized, making additive manufacturing particularly suitable for the production of acoustically effective structures [ 25 ]. The specimens investigated in this contribution consist of a micro-lattice of parallel bars as described in [ 26 ]. Hence, AM is employed within this contribution for manufacturing the required amount of specimens.…”

Section: Introductionmentioning

confidence: 99%

“…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%

“…Additionally, the authors were able to investigate relationships between the geometry and acoustic properties, the Biot parameters (tortuosity, porosity, etc.) [ 26 , 41 ]. 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 ].…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Introductionmentioning

confidence: 95%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

et al. 2021

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“…Setaki et al [ 1 ] design absorber duct lengths in such a way that the sound travelling through them receives a phase shift of 180° due to the different lengths and interferes destructively. Ring and Langer [ 2 ] link the geometric parameters of lattice structures with the resulting BIOT parameters. This allows the microstructure to be designed to achieve a specific absorber behavior.…”

Section: Introductionmentioning

confidence: 99%

Material Parameter Identification for Acoustic Simulation of Additively Manufactured Structures

et al. 2020

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One possibility in order to manufacture products with very few restrictions in design freedom is additive manufacturing. For advanced acoustic design measures like Acoustic Black Holes (ABH), the layer-wise material deposition allows the continuous alignment of the mechanical impedance by different filling patterns and degrees of filling. In order to explore the full design potential, mechanical models are indispensable. In dependency on process parameters, the resulting homogenized material parameters vary. In previous investigations, especially for ABH structures, a dependency of the material parameters on the structure’s thickness can be observed. In this contribution, beams of different thicknesses are investigated experimentally and numerically in order to identify the material parameters in dependency on the frequency and the thickness. The focused material is polyactic acid (PLA). A parameter fitting is conducted by use of a 3D finite element model and it’s reduced version in a Krylov subspace. The results yield homogenized material parameters for the PLA stack as a function of frequency and thickness. An increasing Young’s modulus with increasing frequency and increasing thickness is observed. This observed effect has considerable influence and has not been considered so far. With the received parameters, more reliable results can be obtained.

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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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Design, Experimental and Numerical Characterization of 3D-Printed Porous Absorbers

Cited by 12 publications

References 35 publications

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

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

Material Parameter Identification for Acoustic Simulation of Additively Manufactured Structures

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

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