Determination of material properties of bulk metallic glass using nanoindentation and artificial neural network

Park, Soowan; Fonseca, João Henrique; Marimuthu, Karuppasamy Pandian; Jeong, Chanyoung; Lee, Sihyung; Lee, Hyungyil

doi:10.1016/j.intermet.2022.107492

Cited by 13 publications

(4 citation statements)

References 74 publications

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“…Recently, Park et al [32] and Lu et al [33] developed an ANN-based inverse analysis methodology to identify the mechanical properties of bulk metallic glass materials and metals through ML from instrumented nanoindentation using specific parameters such as total indentation energy, material stiffness, and maximum load. Meanwhile, Jeong et al [34] developed an ANN-based inverse method to determine the stress-strain curves for various bulk metallic materials using finite element simulation datasets of load-depth (P-h) curves.…”

Section: Introductionmentioning

confidence: 99%

Identification of Elastoplastic Constitutive Model of GaN Thin Films Using Instrumented Nanoindentation and Machine Learning Technique

Khalfallah,

Benzarti

2024

Coatings

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This study presents a novel inverse identification approach to determine the elastoplastic parameters of a 2 µm thick GaN semiconductor thin film deposited on a sapphire substrate. This approach combines instrumented nanoindentation with finite element (FE) simulations and an artificial neural network (ANN) model. Experimental load–depth curves were obtained using a Berkovich indenter. To generate a comprehensive database for the inverse analysis, FE models were constructed to simulate load–depth responses across a wide range of GaN thin film properties. The accuracy of both 2D and 3D simulations was compared to select the optimal model for database generation. The Box–Behnken design-based data sampling method was used to define the number of simulations and input variables for the FE models. The ANN technique was then employed to establish the complex mapping between the simulated load–depth curves (input) and the corresponding stress–strain curve (output). The generated database was used to train and test the ANN model. Then, the learned ANN model was used to achieve high accuracy in identifying the stress–strain curve of the GaN thin film from the experimental load–depth data. This work demonstrates the successful application of an inverse analysis framework, combining experimental nanoindentation tests, FE modeling, and an ANN model, for the characterization of the elastoplastic behavior of GaN thin films.

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Section: Introductionmentioning

confidence: 99%

Identification of Elastoplastic Constitutive Model of GaN Thin Films Using Instrumented Nanoindentation and Machine Learning Technique

Khalfallah,

Benzarti

2024

Coatings

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“…Finite element method (FEM) can be advantageous in this situation for the back analysis of the experimental results and to obtain the coated material parameters. Some of the authors have used FEM to generate the load-depth curves and the inverse analysis has been performed using artificial neural network [ [39] , [40] , [41] ].Lee et al have used the FE modelling for nanoindentation simulation. An axis symmetric model with pressure dependent material behavior using Drucker-Prager yield criterion was used to predict the thin film metallic glass properties [ 37 ].…”

Section: Introductionmentioning

confidence: 99%

Nanomechanical inhomogeneities in CVA-deposited titanium nitride thin films: Nanoindentation and finite element method investigations

Sharma,

Rana,

Panwar

et al. 2024

Heliyon

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“…Recently, machine learning (ML) methods have been developed to evaluate mechanical properties from mechanical test data 34–36 and predict mechanical parameters from mechanical test data 37–39 . In many studies, the combination of ML methods and CZM methods has shown great potential to effectively determine the CZM traction separation law or fracture energy of different materials.…”

Section: Introductionmentioning

confidence: 99%

“…[31][32][33] Recently, machine learning (ML) methods have been developed to evaluate mechanical properties from mechanical test data [34][35][36] and predict mechanical parameters from mechanical test data. [37][38][39] In many studies, the combination of ML methods and CZM methods has shown great potential to effectively determine the CZM traction separation law or fracture energy of different materials. For example, Su et al 40 used a FE model of CZM to simulate the interfacial debonding process and combined it with an artificial neural network (ANN) to identify the interfacial CZM parameters of EB CFRP-concrete joints according to the observed loaddisplacement response.…”

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confidence: 99%

Deep neural network aided cohesive zone parameter identifications through die shear test in electronic packaging

Zhao,

Dai,

Wei

et al. 2023

Fatigue Fract Eng Mat Struct

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The die shear test is a feasible and conventional method to characterize the shear strength of die‐attaching layer materials in electronic packaging. A new method for determining cohesive zone model (CZM) parameters using deep neural networks (DNN) and die shear tests is proposed, different from classical fracture framework or lap shear test‐based methods. With the sintered nano‐silver die shear test, the results show that the bilinear CZM inversion results agree well with the experimental results. It is found that the DNN model has high accuracy in predicting and identifying the maximum shear traction strength τmax, separation displacement of the interface δf, and the interface stiffness k1 of CZM parameters for sintered nano‐silver adhesive layer through die shear test load versus displacement curves. The presented DNN‐aided inverse identifying method through the die shear test in this paper could provide an alternative and convenient method for extracting CZM parameters of various kinds of adhesive materials in electronic packaging.

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Determination of material properties of bulk metallic glass using nanoindentation and artificial neural network

Cited by 13 publications

References 74 publications

Identification of Elastoplastic Constitutive Model of GaN Thin Films Using Instrumented Nanoindentation and Machine Learning Technique

Identification of Elastoplastic Constitutive Model of GaN Thin Films Using Instrumented Nanoindentation and Machine Learning Technique

Nanomechanical inhomogeneities in CVA-deposited titanium nitride thin films: Nanoindentation and finite element method investigations

Deep neural network aided cohesive zone parameter identifications through die shear test in electronic packaging

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