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Determining Surface Topography of a Dressed Grinding Wheel Using Bio-Inspired DNA-Based Computing

Grinding is commonly used for machining parts made of hard or brittle materials with the intent of ensuring a better surface finish. The material removal ability of a grinding wheel depends on whether the wheel surface is populated with a sufficiently high number of randomly distributed active abras...

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Autores principales: Kubo, Akihiko, Teti, Roberto, Ullah, AMM Sharif, Iwadate, Kenji, Segreto, Tiziana
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8069860/
https://www.ncbi.nlm.nih.gov/pubmed/33920335
http://dx.doi.org/10.3390/ma14081899
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author Kubo, Akihiko
Teti, Roberto
Ullah, AMM Sharif
Iwadate, Kenji
Segreto, Tiziana
author_facet Kubo, Akihiko
Teti, Roberto
Ullah, AMM Sharif
Iwadate, Kenji
Segreto, Tiziana
author_sort Kubo, Akihiko
collection PubMed
description Grinding is commonly used for machining parts made of hard or brittle materials with the intent of ensuring a better surface finish. The material removal ability of a grinding wheel depends on whether the wheel surface is populated with a sufficiently high number of randomly distributed active abrasive grains. This condition is ensured by performing dressing operations at regular time intervals. The effectiveness of a dressing operation is determined by measuring the surface topography of the wheel (regions and their distributions on the grinding wheel work surface where the active abrasive grains reside). In many cases, image processing methods are employed to determine the surface topography. However, such procedures must be able to remove the regions where the abrasive grains do not reside while keeping, at the same time, the regions where the abrasive grains reside. Thus, special kinds of image processing techniques are needed to distinguish the non-grain regions from the grain regions, which requires a heavy computing load and long duration. As an alternative, in the framework of the “Biologicalisation in Manufacturing” paradigm, this study employs a bio-inspiration-based computing method known as DNA-based computing (DBC). It is shown that DBC can eliminate non-grain regions while keeping grain regions with significantly lower computational effort and time. On a surface of size 706.5 μm in the circumferential direction and 530 μm in the width direction, there are about 7000 potential regions where grains might reside, as the image processing results exhibit. After performing DBC, this number is reduced to about 300 (representing a realistic estimate). Thus, the outcomes of this study can help develop an intelligent image processing system to optimize dressing operations and thereby, grinding operations.
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spelling pubmed-80698602021-04-26 Determining Surface Topography of a Dressed Grinding Wheel Using Bio-Inspired DNA-Based Computing Kubo, Akihiko Teti, Roberto Ullah, AMM Sharif Iwadate, Kenji Segreto, Tiziana Materials (Basel) Article Grinding is commonly used for machining parts made of hard or brittle materials with the intent of ensuring a better surface finish. The material removal ability of a grinding wheel depends on whether the wheel surface is populated with a sufficiently high number of randomly distributed active abrasive grains. This condition is ensured by performing dressing operations at regular time intervals. The effectiveness of a dressing operation is determined by measuring the surface topography of the wheel (regions and their distributions on the grinding wheel work surface where the active abrasive grains reside). In many cases, image processing methods are employed to determine the surface topography. However, such procedures must be able to remove the regions where the abrasive grains do not reside while keeping, at the same time, the regions where the abrasive grains reside. Thus, special kinds of image processing techniques are needed to distinguish the non-grain regions from the grain regions, which requires a heavy computing load and long duration. As an alternative, in the framework of the “Biologicalisation in Manufacturing” paradigm, this study employs a bio-inspiration-based computing method known as DNA-based computing (DBC). It is shown that DBC can eliminate non-grain regions while keeping grain regions with significantly lower computational effort and time. On a surface of size 706.5 μm in the circumferential direction and 530 μm in the width direction, there are about 7000 potential regions where grains might reside, as the image processing results exhibit. After performing DBC, this number is reduced to about 300 (representing a realistic estimate). Thus, the outcomes of this study can help develop an intelligent image processing system to optimize dressing operations and thereby, grinding operations. MDPI 2021-04-11 /pmc/articles/PMC8069860/ /pubmed/33920335 http://dx.doi.org/10.3390/ma14081899 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Kubo, Akihiko
Teti, Roberto
Ullah, AMM Sharif
Iwadate, Kenji
Segreto, Tiziana
Determining Surface Topography of a Dressed Grinding Wheel Using Bio-Inspired DNA-Based Computing
title Determining Surface Topography of a Dressed Grinding Wheel Using Bio-Inspired DNA-Based Computing
title_full Determining Surface Topography of a Dressed Grinding Wheel Using Bio-Inspired DNA-Based Computing
title_fullStr Determining Surface Topography of a Dressed Grinding Wheel Using Bio-Inspired DNA-Based Computing
title_full_unstemmed Determining Surface Topography of a Dressed Grinding Wheel Using Bio-Inspired DNA-Based Computing
title_short Determining Surface Topography of a Dressed Grinding Wheel Using Bio-Inspired DNA-Based Computing
title_sort determining surface topography of a dressed grinding wheel using bio-inspired dna-based computing
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8069860/
https://www.ncbi.nlm.nih.gov/pubmed/33920335
http://dx.doi.org/10.3390/ma14081899
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