Improvement of instrumented indentation test accuracy by data augmentation with electrical contact resistance

Galetto, Maurizio; Kholkhujaev, Jasurkhuja; Maculotti, Giacomo

doi:10.1016/j.cirp.2023.03.034

Cited by 2 publications

(4 citation statements)

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“…Thus, knowing the standard error of the intercept (

), the uncertainty can be evaluated accordingly by applying Equation (27). Conversely, for the ECR-based approach, the measurement uncertainty is simply the prediction interval of the scaled and normalized regression [ 79 , 86 ].…”

Section: Methodsmentioning

confidence: 99%

“…The approach based on ECR is the most recently introduced [ 79 ]. ECR was originally conceived to detect phase transformation in semiconductors and different phases in composites [ 83 ].…”

Section: Introductionmentioning

confidence: 99%

“…The parameters of Equation (24) can be obtained by calibration on a material that, at the characterization scale, does not present a significant edge effect, e.g., aluminum alloys or brass [ 79 ]. Indeed, the calibrated parameters are material-dependent.…”

Section: Introductionmentioning

confidence: 99%

“…Normalized data for the material needing correction (indicated with subscript

) are obtained:

where the subscript

indicates the reference material,

is the calibrated relationship in Equation (24), and the asterisk (*) indicates quantities collected at scales unaffected by edge effect. Because the approximation holds in small intervals

, from the normalized data, a new regression for the material needing correction can be re-evaluated to predict edge-effect correction [ 79 ].…”

Section: Introductionmentioning

confidence: 99%

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Metrological Comparison of Available Methods to Correct Edge-Effect Local Plasticity in Instrumented Indentation Test

et al. 2023

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The Instrumented Indentation Test (IIT) mechanically characterizes materials from the nano to the macro scale, enabling the evaluation of microstructure and ultra-thin coatings. IIT is a non-conventional technique applied in strategic sectors, e.g., automotive, aerospace and physics, to foster the development of innovative materials and manufacturing processes. However, material plasticity at the indentation edge biases the characterization results. Correcting such effects is extremely challenging, and several methods have been proposed in the literature. However, comparisons of these available methods are rare, often limited in scope, and neglect metrological performance of the different methods. After reviewing the main available methods, this work innovatively proposes a performance comparison within a metrological framework currently missing in the literature. The proposed framework for performance comparison is applied to some available methods, i.e., work-based, topographical measurement of the indentation to evaluate the area and the volume of the pile-up, Nix–Gao model and the electrical contact resistance (ECR) approach. The accuracy and measurement uncertainty of the correction methods is compared considering calibrated reference materials to establish traceability of the comparison. Results, also discussed in light of the practical convenience of the methods, show that the most accurate method is the Nix–Gao approach (accuracy of 0.28 GPa, expanded uncertainty of 0.57 GPa), while the most precise is the ECR (accuracy of 0.33 GPa, expanded uncertainty of 0.37 GPa), which also allows for in-line and real-time corrections.

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“…Thus, knowing the standard error of the intercept (

Section: Methodsmentioning

confidence: 99%

“…The approach based on ECR is the most recently introduced [ 79 ]. ECR was originally conceived to detect phase transformation in semiconductors and different phases in composites [ 83 ].…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…Normalized data for the material needing correction (indicated with subscript

) are obtained:

where the subscript

indicates the reference material,

is the calibrated relationship in Equation (24), and the asterisk (*) indicates quantities collected at scales unaffected by edge effect. Because the approximation holds in small intervals

, from the normalized data, a new regression for the material needing correction can be re-evaluated to predict edge-effect correction [ 79 ].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations