30 Years of Nanoindentation with the Oliver-Pharr Method and Beyond: On-Demand Poster Session
Sponsored by: TMS Materials Processing and Manufacturing Division, TMS: Nanomechanical Materials Behavior Committee
Program Organizers: Verena Maier-Kiener, Montanuniversitaet Leoben; Benoit Merle, University of Kassel; Erik Herbert, Michigan Technological University; Samantha Lawrence, Los Alamos National Laboratory; Nigel Jennett, Coventry University

Monday 8:00 AM
March 14, 2022
Room: Nanostructured Materials
Location: On-Demand Poster Hall

On the Plastic Deformation Mechanisms Operating in High Purity Indium at Small Length Scales and High Homologous Temperatures: Fereshteh Mallakpour1; Stephen Hackney1; Erik Herbert1; 1Michigan Tech University
    Nanoindentation and electron microscopy have been used to examine the plastic deformation mechanisms operating in high purity indium at high homologous temperatures and length scales where the probability of finding mobile dislocations and/or active dislocation multiplication sources is low. Load-displacement data obtained using the Berkovich and a spherical (ellipsoid-like) indenter tip show a distinctive and abrupt transition in the flow mechanism. Before the transition, the residual hardness impressions from both indenter tip geometries exhibit significant pile-up and the stress exponent for creep is found to be approximately 3.5. Post-transition, the residual hardness impressions show no significant pile-up and the stress exponent increases from 3.5 to approximately 9 for the Berkovich and 13 for the sphere. A simple model is introduced to rationalize the stress exponent of 3.5 based on stress directed interface diffusion. Post-transition, the higher stress exponent is rationalized by dislocation mediated flow.

Simultaneous Nanoindentation and Acoustic Monitoring Enhanced by the Deep Learning Methodology: Antanas Daugela1; Jurgis Daugela2; 1Nanometronix LLC; 2Johns Hopkins University
    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.