A machine learning based approach for determining the stress-strain relation of grey cast iron from nanoindentation

Weng, Jian; Lindvall, Rebecka; Zhuang, Kejia; Ståhl, Jan Eric; Ding, Han; Zhou, Jinming

doi:10.1016/j.mechmat.2020.103522

Cited by 24 publications

(7 citation statements)

References 33 publications

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“…19 Moreover, for foam-type materials, the application of neural networks architectures to address their stress response have been reported where no mechanical models exist before: in their work, Sun-Woh and Somchai (1997) used a two hidden layer neural network model to predict the cushioning curves of EPS foams covering two different supplier materials and densities ranging from 18 to 30 kg/normalm3. 20 On foam materials, a more recent study by Weng et al., 21 presents the application of feed-forward neural network models to describe the stress/strain relation in grey cast iron during nanoindentation. Examples of the use of ANNs on modeling different material parameters than the stress/strain relationship, within the mechanics and materials science topics, can be found in the works by Mortazavi and Ince (mechanical fatigue), 22 Setti and Rao, Egala et al., (tribological behavior), 23,24 and Tabaza et al.…”

Section: Introductionmentioning

confidence: 99%

A machine learning approach to estimate the strain energy absorption in expanded polystyrene foams

Rodríguez-Sánchez

Mora

2021

Journal of Cellular Plastics

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Traditional modeling of mechanical energy absorption due to compressive loadings in expanded polystyrene foams involves mathematical descriptions that are derived from stress/strain continuum mechanics models. Nevertheless, most of those models are either constrained using the strain as the only variable to work at large deformation regimes and usually neglect important parameters for energy absorption properties such as the material density or the rate of the applying load. This work presents a neural-network-based approach that produces models that are capable to map the compressive stress response and energy absorption parameters of an expanded polystyrene foam by considering its deformation, compressive loading rates, and different densities. The models are trained with ground-truth data obtained in compressive tests. Two methods to select neural network architectures are also presented, one of which is based on a Design of Experiments strategy. The results show that it is possible to obtain a single artificial neural networks model that can abstract stress and energy absorption solution spaces for the conditions studied in the material. Additionally, such a model is compared with a phenomenological model, and the results show than the neural network model outperforms it in terms of prediction capabilities, since errors around 2% of experimental data were obtained. In this sense, it is demonstrated that by following the presented approach is possible to obtain a model capable to reproduce compressive polystyrene foam stress/strain data, and consequently, to simulate its energy absorption parameters.

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Section: Introductionmentioning

confidence: 99%

A machine learning approach to estimate the strain energy absorption in expanded polystyrene foams

Rodríguez-Sánchez

Mora

2021

Journal of Cellular Plastics

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“…With the development of computer technology and artificial intelligence, combining machine learning and nanoindentation also inspires motivation to study material properties. Recently, Weng et al [45] studied the material properties of cast iron based on machine learning and FE nanoindentation simulation and extracted the sharp stress-strain curve of cast iron by proposing the optimization algorithm particle swarm optimization (PSO) and the detailed steps of the stress-strain relationship inversion method are summarized in Figure 5. Laxmikant et al [46] found that during the fabrication of electronic components, the mismatch in lattice and thermal expansion coefficients between the film and the substrate can lead to misfit strain.…”

Section: Machine Learningmentioning

confidence: 99%

Critical Review of Nanoindentation-Based Numerical Methods for Evaluating Elastoplastic Material Properties

Long,

Dong,

et al. 2023

Coatings

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It is well known that the elastoplastic properties of materials are important indicators to characterize their mechanical behaviors and are of guiding significance in the field of materials science and engineering. In recent years, the rapidly developing nanoindentation technique has been widely used to evaluate various intrinsic information regarding the elastoplastic properties and hardness of various materials such as metals, ceramics, and composites due to its high resolution, versatility, and applicability. However, the nanoindentation process of indenting materials on the nanoscale provides the measurement results, such as load-displacement curves and contact stiffness, which is challenging to analyze and interpret, especially if contained in a large amount of data. Many numerical methods, such as dimensionless analysis, machine learning, and the finite element model, have been recently proposed with the indentation techniques to further reveal the mechanical behavior of materials during nanoindentation and provide important information for material design, property optimization, and engineering applications. In addition, with the continuous development of science and technology, automation and high-throughput processing of nanoindentation experiments have become a future trend, further improving testing efficiency and data accuracy. This paper critically reviewed various numerical methods for evaluating elastoplastic constitutive properties of materials based on nanoindentation technology, which aims to provide a comprehensive understanding of the application and development trend of the nanoindentation technique and to provide guidance and reference for further research and applications.

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“…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

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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.

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A machine learning based approach for determining the stress-strain relation of grey cast iron from nanoindentation

Cited by 24 publications

References 33 publications

A machine learning approach to estimate the strain energy absorption in expanded polystyrene foams

A machine learning approach to estimate the strain energy absorption in expanded polystyrene foams

Critical Review of Nanoindentation-Based Numerical Methods for Evaluating Elastoplastic Material Properties

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

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