Identification of Mechanical Properties of Thin-Film Elastoplastic Materials by Machine Learning

Long, Xu; Lu, Changheng; Shen, Ziyi; Su, Yuezeng

doi:10.1007/s10338-022-00340-5

Cited by 17 publications

(2 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Further studies have demonstrated that complicated relationships exist between the indentation response and stress-strain curves of elastoplastic materials with thin-film substrates, posing challenges for conventional calculation methods. To overcome this problem, Long et al [50] proposed a machine learning-based method, namely convolutional neural network (CNN), to rapidly obtain the mechanical properties of thin-film elastoplastic materials. Compared with the traditional reverse algorithm, CNN excels in 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

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Section: Machine Learningmentioning

confidence: 99%

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

Long,

Dong,

et al. 2023

Coatings

Self Cite

View full text Add to dashboard Cite

show abstract

“…LSTM is frequently used in text recognition, audio processing, and video processing due to its high effectiveness with time series data [ 32 , 33 , 34 ]. For indentation problems, Long et al [ 35 ] adopted the P – h curves of metal materials to train the CNN network; some constitutive parameters are regarded as the training objective. Their datasets are generated by means of FE modeling, and the stress–strain relationship of the metallic material is described by a power-law equation [ 36 , 37 ].…”

Section: Introductionmentioning

confidence: 99%

Indentation Reverse Algorithm of Mechanical Response for Elastoplastic Coatings Based on LSTM Deep Learning

Long

Ding

et al. 2023

Materials

Self Cite

View full text Add to dashboard Cite

The load-penetration depth (P–h) curves of different metallic coating materials can be determined by nanoindentation experiments, and it is a challenge to obtain stress–strain response and elastoplastic properties directly using P–h curves. These problems can be solved by means of finite element (FE) simulation along with reverse analyses and methods, which, however, typically occupy a lengthy time, in addition to the low generality of FE methodologies for different metallic materials. To eliminate the challenges that exist in conventional FE simulations, a long short-term memory (LSTM) neural network is proposed in this study and implemented to deep learn the time series of P–h curves, which is capable of mapping P–h curves to the corresponding stress–strain responses for elastoplastic materials. Prior to the operation of the neural network, 1000 sets of indentation data of metallic coating materials were generated using the FE method as the training and validating sets. Each dataset contains a set of P–h curves as well as the corresponding stress–strain curves, which are used as input data for the network and as training targets. The proposed LSTM neural networks, with various numbers of hidden layers and hidden units, are evaluated to determine the optimal hyperparameters by comparing their loss curves. Based on the analysis of the prediction results of the network, it is concluded that the relationship between the P–h curves of metallic coating materials and their stress–strain responses is well predicted, and this relationship basically coincides with the power-law equation. Furthermore, the deep learning method based on LSTM is advantageous to interpret the elastoplastic behaviors of coating materials from indentation measurement, making the predictions of stress–strain responses much more efficient than FE analysis. The established LSTM neural network exhibits the prediction accuracy up to 97%, which is proved to reliably satisfy the engineering requirements in practice.

show abstract

Determination of Elastoplastic Properties of 2024 Aluminum Alloy Using Deep Learning and Instrumented Nanoindentation Experiment

Wang

Zhang

Liu

et al. 2023

Acta Mech. Solida Sin.

View full text Add to dashboard Cite

Identification of Mechanical Properties of Thin-Film Elastoplastic Materials by Machine Learning

Cited by 17 publications

References 26 publications

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

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

Indentation Reverse Algorithm of Mechanical Response for Elastoplastic Coatings Based on LSTM Deep Learning

Determination of Elastoplastic Properties of 2024 Aluminum Alloy Using Deep Learning and Instrumented Nanoindentation Experiment

Contact Info

Product

Resources

About