New framework for nanoindentation curve fitting and measurement uncertainty estimation

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00177016%3A_____%2F23%3AN0000102" target="_blank" >RIV/00177016:_____/23:N0000102 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/00216224:14310/24:00139805

Výsledek na webu

<a href="https://www.sciencedirect.com/science/article/pii/S0141635923001848" target="_blank" >https://www.sciencedirect.com/science/article/pii/S0141635923001848</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.precisioneng.2023.10.001" target="_blank" >10.1016/j.precisioneng.2023.10.001</a>

Jazyk výsledku

angličtina

Název v původním jazyce

New framework for nanoindentation curve fitting and measurement uncertainty estimation

Popis výsledku v původním jazyce

Uncertainty quantification is a vital component of any measurement process and is indispensable for comparing results obtained by different methods, instruments, or laboratories. The processing of the measured data often relies on fitting the data by a given function. Common methods such as ordinary nonlinear least squares are not capable of treating general uncertainties and correlations in both dependent and independent variables. A new computation method for nonlinear curve fitting to data with a general covariance structure is introduced. This method is applied to the Oliver-Pharr analysis of unloading curves and differences between different regression methods are addressed. Numerical simulations show that the new method yields parameter estimates in agreement with other methods for simple covariance structures. The obtained uncertainty estimates are in agreement with Monte Carlo studies.

Název v anglickém jazyce

New framework for nanoindentation curve fitting and measurement uncertainty estimation

Popis výsledku anglicky

Uncertainty quantification is a vital component of any measurement process and is indispensable for comparing results obtained by different methods, instruments, or laboratories. The processing of the measured data often relies on fitting the data by a given function. Common methods such as ordinary nonlinear least squares are not capable of treating general uncertainties and correlations in both dependent and independent variables. A new computation method for nonlinear curve fitting to data with a general covariance structure is introduced. This method is applied to the Oliver-Pharr analysis of unloading curves and differences between different regression methods are addressed. Numerical simulations show that the new method yields parameter estimates in agreement with other methods for simple covariance structures. The obtained uncertainty estimates are in agreement with Monte Carlo studies.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

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OECD FORD obor

10103 - Statistics and probability

Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2023

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Precision Engineering

ISSN

0141-6359

e-ISSN

1873-2372

Svazek periodika

85

Číslo periodika v rámci svazku

January 2024

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

8

Strana od-do

166-173

Kód UT WoS článku

001098374000001

EID výsledku v databázi Scopus

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