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Multiscale Material Characterization Based on Single Particle Impact Utilizing Particle-Oriented Peening and
Nicole Wielki1,2, Matthias Steinbacher1,2, Daniel Meyer1,2
1Faculty of Production Engineering, University of Bremen and MAPEX Center for Materials and Processes, Badgasteiner Straße 1, 28359 Bremen, Germany.
Materials (Basel, Switzerland)
|February 23, 2020
Summary
Particle-oriented peening enables rapid material characterization. This method allows predicting material properties from small samples, reducing development time and cost for new structural materials.
Area of Science:
- Materials Science
- Mechanical Engineering
- Surface Engineering
Background:
- Conventional material development is time-consuming, costly, and experience-based.
- New methods are needed for efficient material development and characterization.
- Particle-oriented peening offers a novel approach to material analysis.
Purpose of the Study:
- To investigate the transferability of material property data from microscopic to macroscopic samples.
- To compare particle-oriented peening with single-impact peening for cross-scale analysis.
- To establish a predictive method for material behavior using small-scale samples.
Main Methods:
- Utilized particle-oriented peening on microscopic samples (d = 0.8 mm) of 100Cr6 (AISI 52100) in five states.
- Employed single-impact peening for macroscopic sample deformation.
- Analyzed plastic deformation (∆l, rf, rc) and particle velocities post-impact.
Main Results:
- Demonstrated comparable dynamic material behavior across different sample dimensions.
- Successfully correlated micro-scale peening data with macro-scale material responses.
- Validated the use of particle velocities as indicators of elasto-plastic properties.
Conclusions:
- Particle-oriented peening is a viable method for characterizing material behavior at micro and macro scales.
- This technique facilitates accurate prediction of material properties for new and unknown materials.
- Accelerated material development is achievable through rapid analysis of small samples.

