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Plastic Material Classification using Neural Network based Audio Signal Analysis

: Grollmisch, Sascha; Johnson, David; Krüger, Tobias; Liebetrau, Judith

Fulltext ()

SMSI 2020 - Sensor and Measurement Science International : Proceedings, 22-25 June 2020, Nuremberg, Germany, SMSI 2020 is cancelled due to COVID-19
Wunstorf: AMA Service, 2020
ISBN: 978-3-9819376-2-6
ISBN: 978-3-9819376-3-3
ISBN: 3-9819376-3-5
Conference "Sensor and Measurement Science International" (SMSI) <2020, Nuremberg/cancelled>
Conference Paper, Electronic Publication
Fraunhofer IDMT ()

Analyzing the acoustic response of products being struck is a potential method to detect material deviations or faults for automated quality control. To evaluate this, we implement a material detection system by equipping an air hockey table with two microphones and plastic pucks 3D printed using different materials. Using this setup, a dataset of the acoustic response of impacts on plastic materials was developed and published. A convolutional neural network trained on this data, achieved high classification accuracy even under noisy conditions demonstrating the potential of this approach.