Process Diagnostics and Control in Thermal Spray
Abstract This perspective paper summarizes the authors’ view on how process diagnostics and control can help to gain a deeper insight into thermal spray processes and to better understand the underlying mechanisms. The current situation in terms of available process control strategies and suitable s...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Mauer, Georg [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2022 |
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| Schlagwörter: |
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| Anmerkung: |
© The Author(s) 2022 |
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| Übergeordnetes Werk: |
Enthalten in: Journal of thermal spray technology - Springer US, 1992, 31(2022), 4 vom: 17. Feb., Seite 818-828 |
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| Übergeordnetes Werk: |
volume:31 ; year:2022 ; number:4 ; day:17 ; month:02 ; pages:818-828 |
| Links: |
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| DOI / URN: |
10.1007/s11666-022-01341-z |
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| Katalog-ID: |
OLC2078627615 |
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| 520 | |a Abstract This perspective paper summarizes the authors’ view on how process diagnostics and control can help to gain a deeper insight into thermal spray processes and to better understand the underlying mechanisms. The current situation in terms of available process control strategies and suitable sensors is described. In perspective, it is assumed that with suitable models, sensors and machine learning tools, it will be possible to perform a smaller number of experiments to develop coatings with specific target characteristics. In addition, trained machine learning tools can be used to implement an efficient control strategy to produce coatings with high reproducibility and reliability. The corresponding existing knowledge gaps are analyzed to identify needs for future research. | ||
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| 650 | 4 | |a reliability | |
| 700 | 1 | |a Moreau, Christian |4 aut | |
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10.1007/s11666-022-01341-z doi (DE-627)OLC2078627615 (DE-He213)s11666-022-01341-z-p DE-627 ger DE-627 rakwb eng 670 VZ Mauer, Georg verfasserin (orcid)0000-0002-0840-8006 aut Process Diagnostics and Control in Thermal Spray 2022 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s) 2022 Abstract This perspective paper summarizes the authors’ view on how process diagnostics and control can help to gain a deeper insight into thermal spray processes and to better understand the underlying mechanisms. The current situation in terms of available process control strategies and suitable sensors is described. In perspective, it is assumed that with suitable models, sensors and machine learning tools, it will be possible to perform a smaller number of experiments to develop coatings with specific target characteristics. In addition, trained machine learning tools can be used to implement an efficient control strategy to produce coatings with high reproducibility and reliability. The corresponding existing knowledge gaps are analyzed to identify needs for future research. processing, reproducibility processing, stability of TS process processing reliability Moreau, Christian aut Enthalten in Journal of thermal spray technology Springer US, 1992 31(2022), 4 vom: 17. Feb., Seite 818-828 (DE-627)131101544 (DE-600)1118266-0 (DE-576)038867699 1059-9630 nnns volume:31 year:2022 number:4 day:17 month:02 pages:818-828 https://doi.org/10.1007/s11666-022-01341-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC AR 31 2022 4 17 02 818-828 |
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10.1007/s11666-022-01341-z doi (DE-627)OLC2078627615 (DE-He213)s11666-022-01341-z-p DE-627 ger DE-627 rakwb eng 670 VZ Mauer, Georg verfasserin (orcid)0000-0002-0840-8006 aut Process Diagnostics and Control in Thermal Spray 2022 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s) 2022 Abstract This perspective paper summarizes the authors’ view on how process diagnostics and control can help to gain a deeper insight into thermal spray processes and to better understand the underlying mechanisms. The current situation in terms of available process control strategies and suitable sensors is described. In perspective, it is assumed that with suitable models, sensors and machine learning tools, it will be possible to perform a smaller number of experiments to develop coatings with specific target characteristics. In addition, trained machine learning tools can be used to implement an efficient control strategy to produce coatings with high reproducibility and reliability. The corresponding existing knowledge gaps are analyzed to identify needs for future research. processing, reproducibility processing, stability of TS process processing reliability Moreau, Christian aut Enthalten in Journal of thermal spray technology Springer US, 1992 31(2022), 4 vom: 17. Feb., Seite 818-828 (DE-627)131101544 (DE-600)1118266-0 (DE-576)038867699 1059-9630 nnns volume:31 year:2022 number:4 day:17 month:02 pages:818-828 https://doi.org/10.1007/s11666-022-01341-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC AR 31 2022 4 17 02 818-828 |
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10.1007/s11666-022-01341-z doi (DE-627)OLC2078627615 (DE-He213)s11666-022-01341-z-p DE-627 ger DE-627 rakwb eng 670 VZ Mauer, Georg verfasserin (orcid)0000-0002-0840-8006 aut Process Diagnostics and Control in Thermal Spray 2022 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s) 2022 Abstract This perspective paper summarizes the authors’ view on how process diagnostics and control can help to gain a deeper insight into thermal spray processes and to better understand the underlying mechanisms. The current situation in terms of available process control strategies and suitable sensors is described. In perspective, it is assumed that with suitable models, sensors and machine learning tools, it will be possible to perform a smaller number of experiments to develop coatings with specific target characteristics. In addition, trained machine learning tools can be used to implement an efficient control strategy to produce coatings with high reproducibility and reliability. The corresponding existing knowledge gaps are analyzed to identify needs for future research. processing, reproducibility processing, stability of TS process processing reliability Moreau, Christian aut Enthalten in Journal of thermal spray technology Springer US, 1992 31(2022), 4 vom: 17. Feb., Seite 818-828 (DE-627)131101544 (DE-600)1118266-0 (DE-576)038867699 1059-9630 nnns volume:31 year:2022 number:4 day:17 month:02 pages:818-828 https://doi.org/10.1007/s11666-022-01341-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC AR 31 2022 4 17 02 818-828 |
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10.1007/s11666-022-01341-z doi (DE-627)OLC2078627615 (DE-He213)s11666-022-01341-z-p DE-627 ger DE-627 rakwb eng 670 VZ Mauer, Georg verfasserin (orcid)0000-0002-0840-8006 aut Process Diagnostics and Control in Thermal Spray 2022 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s) 2022 Abstract This perspective paper summarizes the authors’ view on how process diagnostics and control can help to gain a deeper insight into thermal spray processes and to better understand the underlying mechanisms. The current situation in terms of available process control strategies and suitable sensors is described. In perspective, it is assumed that with suitable models, sensors and machine learning tools, it will be possible to perform a smaller number of experiments to develop coatings with specific target characteristics. In addition, trained machine learning tools can be used to implement an efficient control strategy to produce coatings with high reproducibility and reliability. The corresponding existing knowledge gaps are analyzed to identify needs for future research. processing, reproducibility processing, stability of TS process processing reliability Moreau, Christian aut Enthalten in Journal of thermal spray technology Springer US, 1992 31(2022), 4 vom: 17. Feb., Seite 818-828 (DE-627)131101544 (DE-600)1118266-0 (DE-576)038867699 1059-9630 nnns volume:31 year:2022 number:4 day:17 month:02 pages:818-828 https://doi.org/10.1007/s11666-022-01341-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC AR 31 2022 4 17 02 818-828 |
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Abstract This perspective paper summarizes the authors’ view on how process diagnostics and control can help to gain a deeper insight into thermal spray processes and to better understand the underlying mechanisms. The current situation in terms of available process control strategies and suitable sensors is described. In perspective, it is assumed that with suitable models, sensors and machine learning tools, it will be possible to perform a smaller number of experiments to develop coatings with specific target characteristics. In addition, trained machine learning tools can be used to implement an efficient control strategy to produce coatings with high reproducibility and reliability. The corresponding existing knowledge gaps are analyzed to identify needs for future research. © The Author(s) 2022 |
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Abstract This perspective paper summarizes the authors’ view on how process diagnostics and control can help to gain a deeper insight into thermal spray processes and to better understand the underlying mechanisms. The current situation in terms of available process control strategies and suitable sensors is described. In perspective, it is assumed that with suitable models, sensors and machine learning tools, it will be possible to perform a smaller number of experiments to develop coatings with specific target characteristics. In addition, trained machine learning tools can be used to implement an efficient control strategy to produce coatings with high reproducibility and reliability. The corresponding existing knowledge gaps are analyzed to identify needs for future research. © The Author(s) 2022 |
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Abstract This perspective paper summarizes the authors’ view on how process diagnostics and control can help to gain a deeper insight into thermal spray processes and to better understand the underlying mechanisms. The current situation in terms of available process control strategies and suitable sensors is described. In perspective, it is assumed that with suitable models, sensors and machine learning tools, it will be possible to perform a smaller number of experiments to develop coatings with specific target characteristics. In addition, trained machine learning tools can be used to implement an efficient control strategy to produce coatings with high reproducibility and reliability. The corresponding existing knowledge gaps are analyzed to identify needs for future research. © The Author(s) 2022 |
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<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">OLC2078627615</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230506013924.0</controlfield><controlfield tag="007">tu</controlfield><controlfield tag="008">221220s2022 xx ||||| 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s11666-022-01341-z</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)OLC2078627615</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-He213)s11666-022-01341-z-p</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">670</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Mauer, Georg</subfield><subfield code="e">verfasserin</subfield><subfield code="0">(orcid)0000-0002-0840-8006</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Process Diagnostics and Control in Thermal Spray</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2022</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">ohne Hilfsmittel zu benutzen</subfield><subfield code="b">n</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Band</subfield><subfield code="b">nc</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© The Author(s) 2022</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract This perspective paper summarizes the authors’ view on how process diagnostics and control can help to gain a deeper insight into thermal spray processes and to better understand the underlying mechanisms. The current situation in terms of available process control strategies and suitable sensors is described. In perspective, it is assumed that with suitable models, sensors and machine learning tools, it will be possible to perform a smaller number of experiments to develop coatings with specific target characteristics. In addition, trained machine learning tools can be used to implement an efficient control strategy to produce coatings with high reproducibility and reliability. The corresponding existing knowledge gaps are analyzed to identify needs for future research.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">processing, reproducibility</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">processing, stability of TS process</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">processing</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">reliability</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Moreau, Christian</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Journal of thermal spray technology</subfield><subfield code="d">Springer US, 1992</subfield><subfield code="g">31(2022), 4 vom: 17. Feb., Seite 818-828</subfield><subfield code="w">(DE-627)131101544</subfield><subfield code="w">(DE-600)1118266-0</subfield><subfield code="w">(DE-576)038867699</subfield><subfield code="x">1059-9630</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:31</subfield><subfield code="g">year:2022</subfield><subfield code="g">number:4</subfield><subfield code="g">day:17</subfield><subfield code="g">month:02</subfield><subfield code="g">pages:818-828</subfield></datafield><datafield tag="856" ind1="4" ind2="1"><subfield code="u">https://doi.org/10.1007/s11666-022-01341-z</subfield><subfield code="z">lizenzpflichtig</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_OLC</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-TEC</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">31</subfield><subfield code="j">2022</subfield><subfield code="e">4</subfield><subfield code="b">17</subfield><subfield code="c">02</subfield><subfield code="h">818-828</subfield></datafield></record></collection>
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