About this Abstract |
Meeting |
2022 TMS Annual Meeting & Exhibition
|
Symposium
|
30 Years of Nanoindentation with the Oliver-Pharr Method and Beyond
|
Presentation Title |
Simultaneous Nanoindentation and Acoustic Monitoring Enhanced by the Deep Learning Methodology |
Author(s) |
Antanas Daugela, Jurgis Daugela |
On-Site Speaker (Planned) |
Antanas Daugela |
Abstract Scope |
Simultaneous nanoindentation and passive monitoring of acoustic waves has been attracting the attention of material scientists since the inception of nanomechanical test instruments. The conventional acoustic wave signal treatment via RMS or integrated energy values proved that quantitative acoustic wave properties correlate well with the local contact materials‘ phenomena such as yield point initiation for W(100), Sapphire, phase transformations on SMA, and differentiating of thin film fracture modes. Thus, the resulting nanoindentation loading-unloading curves and post test imaging helps in identifying materials‘ phenomena. However, the true potential of the method is unleashed in a synergy of wavelet based signal decomposition and machine learning. In this work, a deep learning based signal processing of nanoindentation induced passive and active acoustic events is explored. Both passive and active acoustic monitoring can be conducted during nanoindentation with the integrated utrasonic tip. The proposed deep learning technique yields a reliable classification of acoustic signatures. |
Proceedings Inclusion? |
Planned: |
Keywords |
Mechanical Properties, Characterization, Machine Learning |