skip to main content
DOE PAGES title logo U.S. Department of Energy
Office of Scientific and Technical Information

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:
ORCiD logo; ORCiD logo; ; ; ORCiD logo;
Publication Date:
Sponsoring Org.:
USDOE
OSTI Identifier:
1605031
Grant/Contract Number:  
[SC0019453]
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 ID: ISSN 0027-8424
Publisher:
Proceedings of the National Academy of Sciences
Country of Publication:
United States
Language:
English

Citation Formats

Lu, Lu, Dao, Ming, Kumar, Punit, Ramamurty, Upadrasta, Karniadakis, George Em, and Suresh, Subra. Extraction of mechanical properties of materials through deep learning from instrumented indentation. United States: N. p., 2020. Web. doi:10.1073/pnas.1922210117.
Lu, Lu, Dao, Ming, Kumar, Punit, Ramamurty, Upadrasta, Karniadakis, George Em, & Suresh, Subra. Extraction of mechanical properties of materials through deep learning from instrumented indentation. United States. doi:10.1073/pnas.1922210117.
Lu, Lu, Dao, Ming, Kumar, Punit, Ramamurty, Upadrasta, Karniadakis, George Em, and Suresh, Subra. Mon . "Extraction of mechanical properties of materials through deep learning from instrumented indentation". United States. doi:10.1073/pnas.1922210117.
@article{osti_1605031,
title = {Extraction of mechanical properties of materials through deep learning from instrumented indentation},
author = {Lu, Lu and Dao, Ming and Kumar, Punit and Ramamurty, Upadrasta and Karniadakis, George Em and Suresh, Subra},
abstractNote = {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 simulation 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.},
doi = {10.1073/pnas.1922210117},
journal = {Proceedings of the National Academy of Sciences of the United States of America},
number = ,
volume = ,
place = {United States},
year = {2020},
month = {3}
}

Journal Article:
Free Publicly Available Full Text
Publisher's Version of Record
DOI: 10.1073/pnas.1922210117

Save / Share:

Works referenced in this record:

Improvement of predicting mechanical properties from spherical indentation test
journal, October 2016


A composite neural network that learns from multi-fidelity data: Application to function approximation and inverse PDE problems
journal, January 2020


Identification of Plastic Properties From Conical Indentation Using a Bayesian-Type Statistical Approach
journal, October 2018

  • Zhang, Yupeng; Hart, Jeffrey D.; Needleman, Alan
  • Journal of Applied Mechanics, Vol. 86, Issue 1
  • DOI: 10.1115/1.4041352

Inverse molecular design using machine learning: Generative models for matter engineering
journal, July 2018


Electrical response during indentation of a 1-3 piezoelectric ceramic-polymer composite
journal, July 1999

  • Saigal, A.; Giannakopoulos, A. E.; Pettermann, H. E.
  • Journal of Applied Physics, Vol. 86, Issue 1
  • DOI: 10.1063/1.370773

General relationship between strength and hardness
journal, November 2011


Mechanics of indentation of plastically graded materials—I: Analysis
journal, January 2008


Deep elastic strain engineering of bandgap through machine learning
journal, February 2019

  • Shi, Zhe; Tsymbalov, Evgenii; Dao, Ming
  • Proceedings of the National Academy of Sciences, Vol. 116, Issue 10
  • DOI: 10.1073/pnas.1818555116

Determination of elastoplastic properties by instrumented sharp indentation
journal, April 1999


Indentation across size scales and disciplines: Recent developments in experimentation and modeling
journal, July 2007


Depth-sensing instrumented indentation with dual sharp indenters
journal, August 2003


Nonlinear constitutive models from nanoindentation tests using artificial neural networks
journal, March 2008


Identification of viscoplastic material parameters from spherical indentation data: Part I. Neural networks
journal, March 2006

  • Tyulyukovskiy, E.; Huber, N.
  • Journal of Materials Research, Vol. 21, Issue 3
  • DOI: 10.1557/jmr.2006.0076

An improved technique for determining hardness and elastic modulus using load and displacement sensing indentation experiments
journal, June 1992

  • Oliver, W. C.; Pharr, G. M.
  • Journal of Materials Research, Vol. 7, Issue 06, p. 1564-1583
  • DOI: 10.1557/JMR.1992.1564

Inverse scaling functions in nanoindentation with sharp indenters: Determination of material properties
journal, April 2005

  • Wang, Lugen; Ganor, M.; Rokhlin, S. I.
  • Journal of Materials Research, Vol. 20, Issue 4
  • DOI: 10.1557/JMR.2005.0124

Measurement of hardness and elastic modulus by instrumented indentation: Advances in understanding and refinements to methodology
journal, January 2004


Identification of material properties using nanoindentation and surrogate modeling
journal, March 2016


Relationships between hardness, elastic modulus, and the work of indentation
journal, August 1998

  • Cheng, Yang-Tse; Cheng, Che-Min
  • Applied Physics Letters, Vol. 73, Issue 5
  • DOI: 10.1063/1.121873

A Neural Networks approach to characterize material properties using the spherical indentation test
journal, January 2011


Mean Absolute Percentage Error for regression models
journal, June 2016


Graded Materials for Resistance to Contact Deformation and Damage
journal, June 2001


Determination of Poisson’s Ratio by Spherical Indentation Using Neural Networks—Part I: Theory
journal, November 2000

  • Huber, N.; Konstantinidis, A.; Tsakmakis, Ch.
  • Journal of Applied Mechanics, Vol. 68, Issue 2
  • DOI: 10.1115/1.1354624

Determination of plastic properties of metals by instrumented indentation using different sharp indenters
journal, April 2003


Methodology for the evaluation of yield strength and hardening behavior of metallic materials by indentation with spherical tip
journal, July 2003

  • Ma, Dejun; Ong, Chung Wo; Lu, Jian
  • Journal of Applied Physics, Vol. 94, Issue 1
  • DOI: 10.1063/1.1579862

An energy-based method for analyzing instrumented spherical indentation experiments
journal, January 2004

  • Ni, Wangyang; Cheng, Yang-Tse; Cheng, Che-Min
  • Journal of Materials Research, Vol. 19, Issue 1
  • DOI: 10.1557/jmr.2004.19.1.149

Recent Trends in Deep Learning Based Natural Language Processing [Review Article]
journal, August 2018

  • Young, Tom; Hazarika, Devamanyu; Poria, Soujanya
  • IEEE Computational Intelligence Magazine, Vol. 13, Issue 3
  • DOI: 10.1109/MCI.2018.2840738

Computational modeling of the forward and reverse problems in instrumented sharp indentation
journal, November 2001


Advances in Computer Vision-Based Civil Infrastructure Inspection and Monitoring
journal, April 2019


Micro-and meso-structures and their influence on mechanical properties of selectively laser melted Ti-6Al-4V
journal, August 2018


Surgical robotics beyond enhanced dexterity instrumentation: a survey of machine learning techniques and their role in intelligent and autonomous surgical actions
journal, October 2015

  • Kassahun, Yohannes; Yu, Bingbin; Tibebu, Abraham Temesgen
  • International Journal of Computer Assisted Radiology and Surgery, Vol. 11, Issue 4
  • DOI: 10.1007/s11548-015-1305-z

Validity of the reduced modulus concept to describe indentation loading response for elastoplastic materials with sharp indenters
journal, March 2009

  • Choi, In-suk; and Ruth Schwaiger, Oliver Kraft
  • Journal of Materials Research, Vol. 24, Issue 3
  • DOI: 10.1557/jmr.2009.0120

Material characterization by instrumented spherical indentation
journal, March 2012


Scaling, dimensional analysis, and indentation measurements
journal, August 2004

  • Cheng, Yang-Tse; Cheng, Che-Min
  • Materials Science and Engineering: R: Reports, Vol. 44, Issue 4-5
  • DOI: 10.1016/j.mser.2004.05.001

A guide to deep learning in healthcare
journal, January 2019


Determination of Poisson’s Ratio by Spherical Indentation Using Neural Networks—Part II: Identification Method
journal, November 2000

  • Huber, N.; Tsakmakis, Ch.
  • Journal of Applied Mechanics, Vol. 68, Issue 2
  • DOI: 10.1115/1.1355032

A new method for estimating residual stresses by instrumented sharp indentation
journal, October 1998


Electrical response during indentation of piezoelectric materials: A new method for material characterization
journal, January 1999

  • Sridhar, S.; Giannakopoulos, A. E.; Suresh, S.
  • Journal of Applied Physics, Vol. 85, Issue 1
  • DOI: 10.1063/1.369459

Spherical indentation of composite laminates with controlled gradients in elastic anisotropy
journal, December 1998

  • Jørgensen, O.; Giannakopoulos, A. E.; Suresh, S.
  • International Journal of Solids and Structures, Vol. 35, Issue 36
  • DOI: 10.1016/S0020-7683(97)00209-6