A Methodology Using Monte-Carlo Simulation on Nanoindentation Deconvolution for Metal Classification
Bello bermejo, Juan manuel LU
; Windmark, Christina
LU
; Gutnichenko, Oleksandr
LU
and Ståhl, Jan-Eric
LU
(2024)
11th Swedish Production Symposium
In Sustainable Production through Advanced Manufacturing, Intelligent Automation and Work Integrated Learning
52.
p.613-627
- Abstract
- This paper investigates a methodology for the precise statistical deconvolution of hardness properties within various metallic matrix multiphase materials. The central focus is on accurately characterizing mechanical behaviour in the context of complex materials. To meet these objectives, we implemented an approach involving nanoindentation analyses of the selected materials. This technique allowed for the creation of material profiles based on micromechanical properties. Statistical cumulative density function (CDF) deconvolution was employed to disentangle the complex distributions of multiphase material hardness using cross-validation. Throughout the course of this study, several multicomponent CDF combinations were tested, including... (More)
- This paper investigates a methodology for the precise statistical deconvolution of hardness properties within various metallic matrix multiphase materials. The central focus is on accurately characterizing mechanical behaviour in the context of complex materials. To meet these objectives, we implemented an approach involving nanoindentation analyses of the selected materials. This technique allowed for the creation of material profiles based on micromechanical properties. Statistical cumulative density function (CDF) deconvolution was employed to disentangle the complex distributions of multiphase material hardness using cross-validation. Throughout the course of this study, several multicomponent CDF combinations were tested, including Weibull, Exponential, and Gaussian distributions. This approach challenges the conventional practice of assuming multiple Gaussian distributions of hardness, revealing the limitations of this approach. In addition, Monte-Carlo simulations were harnessed to generate probability density functions (PDFs) that capture the intricate footprint variations in hardness profiles. By implementing our methodology, we strive to offer a comprehensive and refined approach to materials analysis. This potential for differentiation has the significant implication of investigating the impact of impurities and trace elements on the mechanical properties and thus, machinability of metal materials. The ultimate aim is to enhance their recyclability, thereby advancing the principles of the circular economy and contributing to the sustainable development goals. Our study thus underscores the profound impact of material analysis on environmental sustainability and the efficient use of resources, while offering a fresh perspective on the role of statistics in materials science. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/0df2101d-4caa-46fa-a7b0-7fa2c3d7a917
- author
- Bello bermejo, Juan manuel
LU
; Windmark, Christina
LU
; Gutnichenko, Oleksandr
LU
and Ståhl, Jan-Eric
LU
- organization
- publishing date
- 2024-04
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- keywords
- Nanoindentation, Monte-Carlo simulation, Deconvolution, Machinability, Material model
- host publication
- Proceedings of the 11th Swedish Production Symposium (SPS2024)
- series title
- Sustainable Production through Advanced Manufacturing, Intelligent Automation and Work Integrated Learning
- editor
- Andersson, Joel ; Joshi, Shrikant ; Malmsköld, Lennart and Hanning, Fabian
- volume
- 52
- pages
- 613 - 627
- publisher
- IOS Press
- conference name
- 11th Swedish Production Symposium
- conference location
- Trollhätan, Sweden
- conference dates
- 2024-04-23 - 2024-04-26
- external identifiers
-
- scopus:85191305596
- ISSN
- 23527528
- 2352751X
- ISBN
- 978-1-64368-511-3
- 978-1-64368-510-6
- DOI
- 10.3233/ATDE240203
- language
- English
- LU publication?
- yes
- id
- 0df2101d-4caa-46fa-a7b0-7fa2c3d7a917
- date added to LUP
- 2024-05-03 10:23:17
- date last changed
- 2024-05-18 06:26:01
@inbook{0df2101d-4caa-46fa-a7b0-7fa2c3d7a917,
abstract = {{This paper investigates a methodology for the precise statistical deconvolution of hardness properties within various metallic matrix multiphase materials. The central focus is on accurately characterizing mechanical behaviour in the context of complex materials. To meet these objectives, we implemented an approach involving nanoindentation analyses of the selected materials. This technique allowed for the creation of material profiles based on micromechanical properties. Statistical cumulative density function (CDF) deconvolution was employed to disentangle the complex distributions of multiphase material hardness using cross-validation. Throughout the course of this study, several multicomponent CDF combinations were tested, including Weibull, Exponential, and Gaussian distributions. This approach challenges the conventional practice of assuming multiple Gaussian distributions of hardness, revealing the limitations of this approach. In addition, Monte-Carlo simulations were harnessed to generate probability density functions (PDFs) that capture the intricate footprint variations in hardness profiles. By implementing our methodology, we strive to offer a comprehensive and refined approach to materials analysis. This potential for differentiation has the significant implication of investigating the impact of impurities and trace elements on the mechanical properties and thus, machinability of metal materials. The ultimate aim is to enhance their recyclability, thereby advancing the principles of the circular economy and contributing to the sustainable development goals. Our study thus underscores the profound impact of material analysis on environmental sustainability and the efficient use of resources, while offering a fresh perspective on the role of statistics in materials science.}},
author = {{Bello bermejo, Juan manuel and Windmark, Christina and Gutnichenko, Oleksandr and Ståhl, Jan-Eric}},
booktitle = {{Proceedings of the 11th Swedish Production Symposium (SPS2024)}},
editor = {{Andersson, Joel and Joshi, Shrikant and Malmsköld, Lennart and Hanning, Fabian}},
isbn = {{978-1-64368-511-3}},
issn = {{23527528}},
keywords = {{Nanoindentation; Monte-Carlo simulation; Deconvolution; Machinability; Material model}},
language = {{eng}},
pages = {{613--627}},
publisher = {{IOS Press}},
series = {{Sustainable Production through Advanced Manufacturing, Intelligent Automation and Work Integrated Learning}},
title = {{A Methodology Using Monte-Carlo Simulation on Nanoindentation Deconvolution for Metal Classification}},
url = {{http://dx.doi.org/10.3233/ATDE240203}},
doi = {{10.3233/ATDE240203}},
volume = {{52}},
year = {{2024}},
}