Identification of material properties using nanoindentation and surrogate modeling

Li, Han; Gutierrez, Leonardo; Toda, Hiroyuki; Kuwazuru, Osamu; Liu, Wenli; Hangai, Yoshihiko; Kobayashi, Masakazu; Batres, Rafael

doi:10.1016/j.ijsolstr.2015.11.022

Cited by 30 publications

(5 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Consequently, applying these simulation-based approaches to various building categories becomes challenging when dealing with complex case studies and scenarios due to the significant computational burden. In this regard, surrogate models, also called metamodels, can reduce the computational burden of optimization procedures [21].…”

Section: Literature Reviewmentioning

confidence: 99%

MEVO: A Metamodel-Based Evolutionary Optimizer for Building Energy Optimization

Batres,

Dadras,

Mostafazadeh

et al. 2023

Energies

Self Cite

View full text Add to dashboard Cite

A deep energy retrofit of building envelopes is a vital strategy to reduce final energy use in existing buildings towards their net-zero emissions performance. Building energy modeling is a reliable technique that provides a pathway to analyze and optimize various energy-efficient building envelope measures. However, conventional optimization analyses are time-consuming and computationally expensive, especially for complex buildings and many optimization parameters. Therefore, this paper proposed a novel optimization algorithm, MEVO (metamodel-based evolutionary optimizer), developed to efficiently identify optimal retrofit solutions for building envelopes while minimizing the need for extensive simulations. The key innovation of MEVO lies in its integration of evolutionary techniques with design-of-computer experiments, machine learning, and metaheuristic optimization. This approach continuously refined a machine learning model through metaheuristic optimization, crossover, and mutation operations. Comparative assessments were conducted against four alternative metaheuristic algorithms and Bayesian optimization, demonstrating MEVO’s effectiveness in reliably finding the best solution within a reduced computation time. A hypothesis test revealed that the proposed algorithm is significantly better than Bayesian optimization in finding the best cost values. Regarding computation time, the proposed algorithm is 4–7 times faster than the particle swarm optimization algorithm and has a similar computational speed as Bayesian Optimization.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

MEVO: A Metamodel-Based Evolutionary Optimizer for Building Energy Optimization

Batres,

Dadras,

Mostafazadeh

et al. 2023

Energies

Self Cite

View full text Add to dashboard Cite

show abstract

“…Huber et al [127,128] used FEM-trained NNs to identify the Poisson's ratio of materials exhibiting plasticity with isotropic hardening, something not easily obtained before. Since then, FEM-trained NNs have been widely applied as an inverse algorithm to identify material properties from nanoindentation [222,223,224,225,226,227,228,229,230,231].…”

Section: Micro and Nano-mechanicsmentioning

confidence: 99%

Recent Advances and Applications of Machine Learning in Experimental Solid Mechanics: A Review

Jin¹,

Zhang²,

Espinosa³

2023

Preprint

View full text Add to dashboard Cite

For many decades, experimental solid mechanics has played a crucial role in characterizing and understanding the mechanical properties of natural and novel materials. Recent advances in machine learning (ML) provide new opportunities for the field, including experimental design, data analysis, uncertainty quantification, and inverse problems. As the number of papers published in recent years in this emerging field is exploding, it is timely to conduct a comprehensive and up-to-date review of recent ML applications in experimental solid mechanics. Here, we first provide an overview of common ML algorithms and terminologies that are pertinent to this review, with emphasis placed on physics-informed and physics-based ML methods. Then, we provide thorough coverage of recent ML applications in traditional and emerging areas of experimental mechanics, including fracture mechanics, biomechanics, nano-and micro-mechanics, architected materials, and 2D material. Finally, we highlight some current challenges of applying ML to multi-modality and multi-fidelity experimental datasets and propose several future research directions. This review aims to provide valuable insights into the use of ML methods as well as a variety of examples for researchers in solid mechanics to integrate into their experiments.

show abstract

“…Trained NNs were generated to reproduce the loading portion of sharp nanoindentation load-displacement curves (39). A NNbased surrogate model was used in order to reduce the number of finite-element method (FEM) conical indentation simulations to extract material properties (40). Besides NNs, other ML approaches have also been employed to solve the indentation problems, such as identification of plastic properties from conical indentation using Bayesian-type analysis (41).…”

Section: Prior Work On ML For Computational Mechanics and Inverse Indmentioning

confidence: 99%

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

Dao

Kumar

et al. 2020

Proc. Natl. Acad. Sci. U.S.A.

250

View full text Add to dashboard Cite

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.

show abstract

Identification of material properties using nanoindentation and surrogate modeling

Cited by 30 publications

References 36 publications

MEVO: A Metamodel-Based Evolutionary Optimizer for Building Energy Optimization

MEVO: A Metamodel-Based Evolutionary Optimizer for Building Energy Optimization

Recent Advances and Applications of Machine Learning in Experimental Solid Mechanics: A Review

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

Contact Info

Product

Resources

About