Comparison of methods for management of measurement errors in surface topography measurements

Giacomo Maculotti; Gianfranco Genta; Danilo Quagliotti; Hans N. Hansen; Maurizio Galetto

doi:10.1016/j.procir.2023.06.186

Comparison of methods for management of measurement errors in surface topography measurements

Giacomo Maculotti^*, Gianfranco Genta, Danilo Quagliotti, Hans N. Hansen, Maurizio Galetto

^*Corresponding author for this work

Polytechnic University of Turin

Research output: Contribution to journal › Conference article › Research › peer-review

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Abstract

Surface technology is essential to engineer surface properties by topologically optimised design or machining and finishing treatments. Optical surface topography measuring instruments represent state-of-the-art solution to characterise technological surfaces. Topographies measured by optical instruments are affected by errors (non-measured points and spikes), due to complex interactions between the measurand (the topography) and the instrument, liable of poor measurement quality and biasing characterisation. The literature proposes several approaches to manage measurement errors basing on empirical approaches (thresholding, interpolation) and machine learning modelling. This work compares the methods performances applied to industrially relevant case studies (highly polished and native additive manufacturing surfaces).

Original language	English
Journal	Procedia CIRP
Volume	118
Pages (from-to)	1084-1089
ISSN	2212-8271
DOIs	https://doi.org/10.1016/j.procir.2023.06.186
Publication status	Published - 2023
Event	16th CIRP Conference on Intelligent Computation in Manufacturing Engineering: Innovative and Cognitive Production Technology and Systems - Naples, Italy Duration: 13 Jul 2022 → 15 Jul 2022

Conference

Conference	16th CIRP Conference on Intelligent Computation in Manufacturing Engineering
Country/Territory	Italy
City	Naples
Period	13/07/2022 → 15/07/2022

Keywords

Machine Learning
Surface Analysis
Topography

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title = "Comparison of methods for management of measurement errors in surface topography measurements",

abstract = "Surface technology is essential to engineer surface properties by topologically optimised design or machining and finishing treatments. Optical surface topography measuring instruments represent state-of-the-art solution to characterise technological surfaces. Topographies measured by optical instruments are affected by errors (non-measured points and spikes), due to complex interactions between the measurand (the topography) and the instrument, liable of poor measurement quality and biasing characterisation. The literature proposes several approaches to manage measurement errors basing on empirical approaches (thresholding, interpolation) and machine learning modelling. This work compares the methods performances applied to industrially relevant case studies (highly polished and native additive manufacturing surfaces).",

keywords = "Machine Learning, Surface Analysis, Topography",

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note = "Publisher Copyright: {\textcopyright} 2023 Elsevier B.V.. All rights reserved.; 16th CIRP Conference on Intelligent Computation in Manufacturing Engineering : Innovative and Cognitive Production Technology and Systems, CIRP ICME '22 ; Conference date: 13-07-2022 Through 15-07-2022",

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language = "English",

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AU - Maculotti, Giacomo

AU - Genta, Gianfranco

AU - Quagliotti, Danilo

AU - Hansen, Hans N.

AU - Galetto, Maurizio

PY - 2023

Y1 - 2023

N2 - Surface technology is essential to engineer surface properties by topologically optimised design or machining and finishing treatments. Optical surface topography measuring instruments represent state-of-the-art solution to characterise technological surfaces. Topographies measured by optical instruments are affected by errors (non-measured points and spikes), due to complex interactions between the measurand (the topography) and the instrument, liable of poor measurement quality and biasing characterisation. The literature proposes several approaches to manage measurement errors basing on empirical approaches (thresholding, interpolation) and machine learning modelling. This work compares the methods performances applied to industrially relevant case studies (highly polished and native additive manufacturing surfaces).

AB - Surface technology is essential to engineer surface properties by topologically optimised design or machining and finishing treatments. Optical surface topography measuring instruments represent state-of-the-art solution to characterise technological surfaces. Topographies measured by optical instruments are affected by errors (non-measured points and spikes), due to complex interactions between the measurand (the topography) and the instrument, liable of poor measurement quality and biasing characterisation. The literature proposes several approaches to manage measurement errors basing on empirical approaches (thresholding, interpolation) and machine learning modelling. This work compares the methods performances applied to industrially relevant case studies (highly polished and native additive manufacturing surfaces).

KW - Machine Learning

KW - Surface Analysis

KW - Topography

U2 - 10.1016/j.procir.2023.06.186

DO - 10.1016/j.procir.2023.06.186

M3 - Conference article

AN - SCOPUS:85173579210

SN - 2212-8271

VL - 118

SP - 1084

EP - 1089

JO - Procedia CIRP

JF - Procedia CIRP

T2 - 16th CIRP Conference on Intelligent Computation in Manufacturing Engineering

Y2 - 13 July 2022 through 15 July 2022

ER -

Comparison of methods for management of measurement errors in surface topography measurements

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Keywords

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