Estimation of online particle size distribution of a particle mixture in free fall with acoustic emission

• Published in: Particulate science and technology Vol. 37; no. 8; pp. 953 - 963
• Main Authors: Nsugbe, E., Starr, A., Jennions, I., Ruiz-Carcel, C.
• Format: Journal Article
• Language: English
• Published: Philadelphia Taylor & Francis 17.11.2019; Taylor & Francis Ltd
• Subjects: Acoustic emission; Acoustic emissions; Algorithms; amplitude threshold; Beads; Data acquisition; Free fall; Particle size; Particle size distribution; Quality management; Signal analysis; signal processing; time domain; Wavelet analysis; wavelet transform
• ISSN: 0272-6351; 1548-0046
• DOI: 10.1080/02726351.2018.1473540

For many powder processes, the particle size distribution (PSD) is a key quality attribute of the flow properties of the process powders. This paper presents a method for estimating the PSD with acoustic emissions (AE) by implementing a time domain-based threshold approach followed by the extraction of the amplitude mean from each set threshold and correlation to particle size. The experiments were carried out using a powder-free fall experimental rig, and a set of glass beads, while the acquired data were analyzed with a designed signal analysis method. The results of the experiments showed that the PSD of the particle mixtures being investigated could be identified with an average absolute error of 10%. The main advantage of the designed signal analysis method was identified as the requirement for a low hardware complexity due to a simpler algorithm than its predecessors. In an attempt to benchmark the performance of the system, the performance of the designed approach was compared to a wavelet-based analysis designed by Ren et al. From this it was seen that the approach used by Ren et al. is reliant on a tuning process to aid the algorithm in making the size estimation, suggesting that Ren's approach would prove to be inefficient in a process where powder segregation occurred due to poor mixing.