This work reports the results of experimental measurements of the sound absorption coefficient of ceramic materials using the principle of acoustic resonators. Subsequently, the values obtained from the measurements were used to train a simulation model of the acoustic behavior of the analyzed material based on artificial neural networks. The possible applications of sound-absorbing materials made with ceramic can derive from aesthetic or architectural needs or from functional needs, as ceramic is a fireproof material resistant to high temperatures. The results returned by the simulation model based on the artificial neural networks algorithm are particularly significant. This result suggests the adoption of this technology to find the finest possible configuration that allows the best sound absorption performance of the material.
Artificial neural networks (ANNs) have been used in the solution of a variety of mechanical system design, analysis, and control problems. This paper describes the ANNs that have been most frequently used in mechanical system applications. It also summarizes some of the applications that have been developed for ANNs, and briefly reviews the literature where descriptions of the developments and applications can be found. Some recommendations regarding ANN applications in mechanical system simulation, identification, and assessment are provided.
Numerical Simulation for the Sound Absorption Properties of Ceramic Resonators
