P3.11 Plastic Material Classification using Neural Network based Audio Signal Analysis
- Event
- SMSI 2020
-
(did not take place because of Covid-19 virus pandemic) - Band
- SMSI 2020 - Measurement Science
- Chapter
- P3 Advanced Methods and Approaches in Measurement
- Author(s)
- S. Grollmisch - University of Technology, Ilmenau (Germany), D. Johnson, T. Krüger, J. Liebetrau - Fraunhofer Institute IDMT, Ilmenau (Germany)
- Pages
- 337 - 338
- DOI
- 10.5162/SMSI2020/P3.11
- ISBN
- 978-3-9819376-2-6
- Price
- free
Abstract
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.