The cold angular rolling process (CARP) is being developed as a continuous severe plastic deformation technique, which can process metal sheets without any length limitations at room temperature. CARP contains cold rolling and equal‐channel angular process components. The sheet thickness is kept consistent before and after CARP, allowing multiple passes of the sheet. The desired microstructure and mechanical properties can be achieved in the processed metallic sheets. The current study is aimed to evaluate the capability of CARP by processing copper sheets with different sheet widths for repetitive passes. The CARP‐treated sheets are examined by lab‐scale X‐ray and high‐energy synchrotron X‐ray diffraction to investigate the evolution in dislocation density, texture, and strain anisotropy, and by tensile testing to identify the bulk mechanical properties. The digital image correlation method is applied to tensile testing so that strain localization within the sample gauge is visualized and deformation behavior is evaluated after yielding till postnecking by estimating the hardening exponent and strain hardening rate of the CARP‐treated sheet. Comparing the reported continuous and multiple‐step processes on Cu and its alloys, the present study confirms that the CARP is potentially a useful sheet process for strengthening ductile metals.
Supervised learning through physical changes in a mechanical system
Mechanical metamaterials are usually designed to show desired responses to prescribed forces. In some applications, the desired force–response relationship is hard to specify exactly, but examples of forces and desired responses are easily available. Here, we propose a framework for supervised learning in thin, creased sheets that learn the desired force–response behavior by physically experiencing training examples and then, crucially, respond correctly (generalize) to previously unseen test forces. During training, we fold the sheet using training forces, prompting local crease stiffnesses to change in proportion to their experienced strain. We find that this learning process reshapes nonlinearities inherent in folding a sheet so as to show the correct response for previously unseen test forces. We show the relationship between training error, test error, and sheet size (model complexity) in learning sheets and compare them to counterparts in machine-learning algorithms. Our framework shows how the rugged energy landscape of disordered mechanical materials can be sculpted to show desired force–response behaviors by a local physical learning process.
more » « less- PAR ID:
- 10162498
- Publisher / Repository:
- Proceedings of the National Academy of Sciences
- Date Published:
- Journal Name:
- Proceedings of the National Academy of Sciences
- Volume:
- 117
- Issue:
- 26
- ISSN:
- 0027-8424
- Page Range / eLocation ID:
- p. 14843-14850
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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