Micro- and Nano-Mechanical Behavior of Materials: Micro/Nano-Mechanics I
Program Organizers: Sundeep Mukherjee, University Of North Texas; Mahmoud Baniasadi, Georgia Southern University; Meysam Haghshenas, University of Toledo
Monday 8:00 AM
November 2, 2020
Room: Virtual Meeting Room 36
Location: MS&T Virtual
Session Chair: Michael Atzmon, University of Michigan; Sundeep Mukherjee, University of North Texas
8:00 AM
Introductory Comments: Micro- and Nano-Mechanical Behavior of Materials: Sundeep Mukherjee1; 1University of North Texas
Introductory Comments
8:05 AM Invited
Microparticle Impacts at Supersonic Speeds, and the Role of Surface Layers: Ahmed Tiamiyu1; Jasper Lienhard1; Christopher Schuh1; 1Massachusetts Institute of Technology
At extreme impact velocities, metal particles not only deform when they strike a metallic substrate, they can also produce jets of ejecta and adhere. Our recent research has employed a single-particle impact imaging approach to study these extreme phenomena, and this talk will summarize our recent efforts to understand the role of native oxides, hydroxides, and other surface films. In aluminum, we find a correlation between the thickness and structure of the surface passivation layer with its effect on the critical adhesion velocity. In copper, we explore the fate of surface layers after an impact and bonding event, using cross-sectional microscopy of bonded particles. In both cases, we consider the importance of jet formation and the role that surface layers have on this process. Our results have direct implications for the industrial process of cold spray coating, suggesting directions for improved powder handling and management.
8:25 AM Invited
Spatial-temporal Investigation of Shear Bands in Bulk Metallic Glasses: Xie Xie1; Yu-Chieh Luo2; Yang Tong3; Junwei Qiao4; Gongyao Wang1; Shigenobu Ogata5; Hairong Qi1; Karin Dahmen6; Yanfei Gao1; Peter Liaw1; 1University of Tennessee; 2National Chiao Tung University; 3City University of Hong Kong; 4Taiyuan University of Technology; 5Osaka University; 6University of Illinois at Urbana-Champaign
Recent research suggests that temperature is an important factor for the shear-band dynamics in bulk metallic glasses (BMGs). Thus, the characterization of shear bands through thermal imaging provides a way to investigate the BMG deformation behavior. The current work develops a kinetic Monte Carlo (kMC) model to investigate and simulate the initiation and growth characteristics of shear bands, which can be related well with the thermograph-imaging results. These findings are expected to reveal the hidden statistical features during the BMG deformation and advance the fundamental understanding of the BMG deformation mechanism.
8:45 AM
An Integrated In Situ Solution for Automated Material Testing in SEM: William Harris1; Fang Zhou1; Luyang Han1; Kyle Crosby1; Hrishikesh Bale1; 1Carl Zeiss Microscopy
In situ nano/micro-mechanical testing in SEM, especially combined with analytical techniques such as EDS and EBSD, offers a powerful method for understanding the response of material microstructure to load or stimulus. However, running in situ experiments isn't trivial, often involving manual coordination of hardware and software dispersed across numerous platforms and vendors (electron imaging, analytics acquisition, in situ stage control). Further complications arise in data interpretation, with the challenge of correlating results in space and time originating from multiple sources. This contribution will present a fully integrated solution to mitigate these problems, combining a tensile-compression stage, heating unit, dedicated high temperature detectors, and EDS/EBSD sub-units controlled from a unified software environment. The user can prescribe automated runs with multiple ROI’s, implement automated feature tracking and autofocus, and define different imaging conditions or EDS/EBSD acquisition per ROI and load. Examples will be presented including copper, steel, and nickel alloy systems.
9:05 AM
Computer Vision Approach to Study Surface Deformation of Materials: Chaoyi Zhu1; Haoren Wang2; Kevin Kaufmann2; Kenneth Vecchio2; 1Carnegie Mellon University; 2University of California, San Diego
Characterization of the deformation of materials across different length scales has continuously attracted enormous attention from the mechanics and materials communities. In this study, the possibility of utilizing a computer vision algorithm to extract deformation information of materials has been explored, which greatly expands the use of computer vision approaches to studying mechanics of materials and potentially opens new dialogues between the two communities. The computer vision algorithm is first developed and tested on computationally deformed images before evaluating experimentally collected images on speckle painted samples before and after deformation. Experimental validation experiments include evaluating the performance of strain mapping in a uniaxial tensile test and a three-point bending test, compared with extensometer reading and digital image correlation respectively.
9:25 AM Invited
Characterization of Electroless Copper Deposits on Electrospun PAN Fibers in Aligned and Random Configurations: Temitope Aminu1; Molly Brockway2; Jack Skinner2; David Bahr1; 1Purdue University; 2Montana Technological University
We have electrolessly deposited copper on random and aligned PAN fibers utilizing silver nanoparticles as catalytic seeds. Fiber sizes ranges from diameters of hundreds of nanometers to a few microns. Prior work has established that coating conformity is strongly modulated by density of catalytic seeds.We investigate the changes in the chemistry of the fibers due to the exposure to the electroless plating solutions using Raman spectroscopy. In addition, tensile tests are carried out on the metallized aligned fibers at distinct strain levels to investigate possible delamination events. Previous strain-to-failure tests on conformally coated, randomly aligned fibers mats showed good adhesion of the copper particles on the fibers. In parallel, we examine the mechanical behavior of the metallized fibers under equi-biaxial stress state utilizing a novel “leaky” bulge testing system, and demonstrate the links between the mechanics of coated fiber mats in aligned and random configurations.
9:45 AM Invited
Time-resolved Atomic-scale Observations of Deformation and Fracture of Nanostructured Materials: Pan Liu1; Mingwei Chen2; 1Shanghai Jiao Tong University; 2Johns Hopkins University
Revealing atomic-scale processes of deformation and failure is the ultimate goal of understanding the micromechanisms of mechanical behaviors of materials. The combination of aberration-corrected transmission electron microscopy and fast direct electron detection camera enables the direct observations of atom motions under external stress fields in a fast time domain. By utilizing the state-of-the-art experimental technique, we investigated the dislocation/twin nucleation, lattice shearing, dislocation climbing, and stress-induced surface diffusion and grain boundary migration in nanostructured metals. These real-time atomic-scale observations provide the sought-after details of deformation processes of nanostructured materials and unveil the underlying micromechanisms of their mechanical properties.
10:05 AM
Quantitative Evaluation of Large Nanoindentation Data Sets: Bernard Becker1; Benjamin Stadnick1; Eric Hintsala1; Ude Hangen1; Douglas Stauffer1; 1Bruker Nano
Nanoindentation methods have progressed to the point where extremely large data sets, up to 106, are now practical. This then requires automated and statistical analysis, including clustering and the use of machine learning. This talk focuses on how to extract materials information from large arrays of nanoindentation data. Determining the necessary number of data points, how many clusters should be used, what cluster models, and the related questions related to data bootstrapping are discussed. A framework is presented that can be used to describe nanoindentation data with a vector, modeling that data, then simulating data of the model, and re-clustering of the simulated data to provide information related to the original clustering. The resulting properties are found to have a low bias (<1%) and relative uncertainty (<1%), indicating sufficiency in comparison to that of the original datasets.