This paper aims to evaluate the effectiveness of different Machine Learning algorithms for the estimation of Particle Size Distribution (PSD) of powder by means of Acoustic Emissions (AE). In industrial plants it is very useful to use non-invasive and adaptable systems for monitoring the particle size, for this reason the AE represents an important mean for detecting the particle size. To create a model that relates the AE with the powder size, Machine Learning is a viable approach to model a complex system without knowing all the variables in details. The test results show a good estimation accuracy for the various Machine Learning algorithms employed in this study.
Machine learning techniques for the estimation of particle size distribution in industrial plants / Rossetti, Damiano; Squartini, Stefano; Collura, Stefano. - 54:(2016), pp. 335-343. [10.1007/978-3-319-33747-0_33]
Machine learning techniques for the estimation of particle size distribution in industrial plants
ROSSETTI, DAMIANO;SQUARTINI, Stefano;
2016-01-01
Abstract
This paper aims to evaluate the effectiveness of different Machine Learning algorithms for the estimation of Particle Size Distribution (PSD) of powder by means of Acoustic Emissions (AE). In industrial plants it is very useful to use non-invasive and adaptable systems for monitoring the particle size, for this reason the AE represents an important mean for detecting the particle size. To create a model that relates the AE with the powder size, Machine Learning is a viable approach to model a complex system without knowing all the variables in details. The test results show a good estimation accuracy for the various Machine Learning algorithms employed in this study.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
