Toward the correlation of indentation hardness in micro- and nano-scale: understanding of indentation edge behaviors in Fe–Cr alloys

Geng, Diancheng; Yu, Hao; Kondo, Sosuke; Kasada, Ryuta

doi:10.1007/s10853-022-07461-9

Cited by 5 publications

(4 citation statements)

References 36 publications

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“…This adaptability to enhance precision with additional data input demonstrates the neural networks' robustness in modeling complex relationships, even when they deviate from linearity. Previous studies posited that the relationship between nanoindentation hardness and Vickers hardness is generally linear [59][60][61]. Observations from Figure 6 also suggest an approximately linear relationship.…”

Section: Validation Of Nanoindentation-vickers Hardness Dnn Modelmentioning

confidence: 54%

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Leveraging Deep Neural Networks for Estimating Vickers Hardness from Nanoindentation Hardness

Niu,

Miao,

Guo

et al. 2023

Materials

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This research presents a comprehensive analysis of deep neural network models (DNNs) for the precise prediction of Vickers hardness (HV) in nitrided and carburized M50NiL steel samples, with hardness values spanning from 400 to 1000 HV. By conducting rigorous experimentation and obtaining corresponding nanoindentation data, we evaluated the performance of four distinct neural network architectures: Multilayer Perceptron (MLP), Convolutional Neural Network (CNN), Long Short-Term Memory network (LSTM), and Transformer. Our findings reveal that MLP and LSTM models excel in predictive accuracy and efficiency, with MLP showing exceptional iteration efficiency and predictive precision. The study validates models for broad application in various steel types and confirms nanoindentation as an effective direct measure for HV hardness in thin films and gradient-variable regions. This work contributes a validated and versatile approach to the hardness assessment of thin-film materials and those with intricate microstructures, enhancing material characterization and potential application in advanced material engineering.

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Section: Validation Of Nanoindentation-vickers Hardness Dnn Modelmentioning

confidence: 54%

“…Previous studies posited that the relationship between nanoindentation hardness and Vickers hardness is generally linear [ 59 , 60 , 61 ]. Observations from Figure 6 also suggest an approximately linear relationship.…”

Section: Resultsmentioning

confidence: 99%

Leveraging Deep Neural Networks for Estimating Vickers Hardness from Nanoindentation Hardness

Niu,

Miao,

Guo

et al. 2023

Materials

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“…Predicting strength from morphological observations could be of technological importance. Our third goal is to verify consistency of experimental results in comparison to the calculated coe cients based on data available in literature [25,27,30,[33][34][35][36][37][38][39][40][41][42][43][44].…”

Section: Introductionmentioning

confidence: 99%

“…The reported values for C 1 could be categorized as following a) C 1 > 1.13 for single crystals (6) b) C 1 > 1.3 for polycrystalline metals and alloys (7) Elastic anisotropy, polycrystallinity, imperfections, morphology of microstructure, chemical composition, mechanical and heat treatment [26][27][28][29][30][31][32] can in uence the actual value of C 1 , relation (5). Geng's theoretical model connecting micro and nanohardness [30] supported by experimental data performed on Fe-Cr alloys, without specifying the microstructure of the alloy under study, concludes that quantitative corrections for pileups are similar in nano and micro-scale measurements and suggests the possibility of a universal linear relationship for all metallic materials. Our goal in this paper is to determine coe cients, C 1 and k, for eutectic SnBi alloy in correspondence to microstructure, composition without mechanical or heat treatment.…”

Section: Introductionmentioning

confidence: 99%

Nanoindentation to microhardness correlation coefficient and Tabor factor determination in relation to microstructure in eutectic SnBi alloy

Starostina,

Hartman,

Cole

et al. 2024

Preprint

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Determining strength to hardness correlation coefficients from localized hardness measurements in correspondence to microstructure and composition leads the way to inexpensive, non-destructive ways to predict tensile properties of bulk materials which is important for developing preventive maintenance procedures in the semiconductor industry. Nanoindentation and microhardness tests were performed on an in-house prepared eutectic SnBi alloy. Both linear correlation coefficients, C1, between nanoindentation and Vickers microhardness, and k, between Vickers microhardness and ultimate tensile strength, were determined based on experimental measurements. Elemental composition and eutectic morphology were verified by scanning electron microscopy and energy dispersive spectroscopy to emphasize the importance of considering composition, microstructure and strengthening mechanisms when estimating correlations. The correlation coefficients, C1 and k, were found to be ~ 1.7 and ~ 4.0 respectively indicating that composition, microstructure and strengthening play important role in hardness-strength relation. Comparisons to coefficients estimated from data available for SnBi alloy and other alloy systems are discussed.

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Investigating the nanoscale hardness/strength properties of high-entropy alloy particles using the nanoindentation technique

Custodio,

Lindquist,

Tolentino

et al. 2023

Journal of Alloys and Metallurgical Systems

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Toward the correlation of indentation hardness in micro- and nano-scale: understanding of indentation edge behaviors in Fe–Cr alloys

Cited by 5 publications

References 36 publications

Leveraging Deep Neural Networks for Estimating Vickers Hardness from Nanoindentation Hardness

Leveraging Deep Neural Networks for Estimating Vickers Hardness from Nanoindentation Hardness

Nanoindentation to microhardness correlation coefficient and Tabor factor determination in relation to microstructure in eutectic SnBi alloy

Investigating the nanoscale hardness/strength properties of high-entropy alloy particles using the nanoindentation technique

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