Title: Extraction of mechanical properties of materials through deep learning from instrumented indentation

Abstract

Instrumented indentation has been developed and widely utilized as one of the most versatile and practical means of extracting mechanical properties of materials. This method is particularly desirable for those applications where it is difficult to experimentally determine the mechanical properties using stress–strain data obtained from coupon specimens. Such applications include material processing and manufacturing of small and large engineering components and structures involving the following: three-dimensional (3D) printing, thin-film and multilayered structures, and integrated manufacturing of materials for coupled mechanical and functional properties. Here, we utilize the latest developments in neural networks, including a multifidelity approach whereby deep-learning algorithms are trained to extract elastoplastic properties of metals and alloys from instrumented indentation results using multiple datasets for desired levels of improved accuracy. We have established algorithms for solving inverse problems by recourse to single, dual, and multiple indentation and demonstrate that these algorithms significantly outperform traditional brute force computations and function-fitting methods. Moreover, we present several multifidelity approaches specifically for solving the inverse indentation problem which 1) significantly reduce the number of high-fidelity datasets required to achieve a given level of accuracy, 2) utilize known physical and scaling laws to improve training efficiency and accuracy, and 3) integrate simulationmore » and experimental data for training disparate datasets to learn and minimize systematic errors. The predictive capabilities and advantages of these multifidelity methods have been assessed by direct comparisons with experimental results for indentation for different commercial alloys, including two wrought aluminum alloys and several 3D printed titanium alloys.« less

Authors:

Lu, Lu  ^[1];

• Search DOE PAGES for author "Lu, Lu"
• Search DOE PAGES for ORCID "0000-0002-5476-5768"

Dao, Ming  ^[2];

• Search DOE PAGES for author "Dao, Ming"
• Search DOE PAGES for ORCID "0000-0001-5372-385X"

Kumar, Punit ^[3]; Ramamurty, Upadrasta ^[3]; Karniadakis, George Em  ^[1];

• Search DOE PAGES for author "Karniadakis, George Em"
• Search DOE PAGES for ORCID "0000-0002-9713-7120"

Suresh, Subra ^[4]

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+ Show Author Affiliations

1. Division of Applied Mathematics, Brown University, Providence, RI 02912,
2. Department of Materials Science and Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139,
3. School of Mechanical and Aerospace Engineering, Nanyang Technological University, 639798 Singapore,
4. Nanyang Technological University, 639798 Singapore

Publication Date:

Mon Mar 16 00:00:00 EDT 2020

Research Org.:

Brown Univ., Providence, RI (United States)

Sponsoring Org.:

USDOE Office of Science (SC); US Army Research Office (ARO)

OSTI Identifier:

1605031

Alternate Identifier(s):

OSTI ID: 1625065

Grant/Contract Number:  

SC0019453; W911NF-12-2-0023

Resource Type:

Published Article

Journal Name:

Proceedings of the National Academy of Sciences of the United States of America

Additional Journal Information:

Journal Name: Proceedings of the National Academy of Sciences of the United States of America Journal Volume: 117 Journal Issue: 13; Journal ID: ISSN 0027-8424

Publisher:

Proceedings of the National Academy of Sciences

Country of Publication:

United States

Language:

English

Subject:

36 MATERIALS SCIENCE; Science & Technology - Other Topics; 3D Printed Materials; Stress-strain Behavior; Multifidelity Modeling; Transfer Learning; Machine Learning