Better Quality Control: Stochastic Approaches to Optimize Properties and Performance of Plasma-Sprayed Coatings
Abstract Statistical design of experiment (SDE) methodology applied to design and performance testing of plasma-sprayed coatings follows an evolutionary path, usually starting with classic multiparameter screening designs (Plackett-Burman), and progressing through factorial (Taguchi) to limited resp...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Heimann, Robert B. [verfasserIn] |
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| Format: |
Artikel |
|---|---|
| Sprache: |
Englisch |
| Erschienen: |
2009 |
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| Schlagwörter: |
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| Anmerkung: |
© ASM International 2009 |
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| Übergeordnetes Werk: |
Enthalten in: Journal of thermal spray technology - Springer US, 1992, 19(2009), 4 vom: 03. Sept., Seite 765-778 |
|---|---|
| Übergeordnetes Werk: |
volume:19 ; year:2009 ; number:4 ; day:03 ; month:09 ; pages:765-778 |
| Links: |
|---|
| DOI / URN: |
10.1007/s11666-009-9385-3 |
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OLC2060558263 |
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| 520 | |a Abstract Statistical design of experiment (SDE) methodology applied to design and performance testing of plasma-sprayed coatings follows an evolutionary path, usually starting with classic multiparameter screening designs (Plackett-Burman), and progressing through factorial (Taguchi) to limited response surface designs (Box-Behnken). Modern designs of higher dimensionality, such as central composite and D-optimal designs, will provide results with higher predictive power. Complex theoretical models relying on evolutionary algorithms, and application of artificial neuronal networks (ANNs) and fuzzy logic control (FLC) allow estimating the behavior of the complex plasma spray environment through validation either by key experiments or first-principle calculations. In this review, paper general principles of SDE will be discussed and examples be given that underscore the different powers of prediction of individual statistical designs. Basic rules of ANN and FLC will be briefly touched on, and their potential for increased reliability of coating performance through stringent quality control measures assessed. Salient features will be reviewed of studies performed to optimize thermal coating properties and processes reported in the pertinent literature between 2000 and the present. | ||
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10.1007/s11666-009-9385-3 doi (DE-627)OLC2060558263 (DE-He213)s11666-009-9385-3-p DE-627 ger DE-627 rakwb eng 670 VZ Heimann, Robert B. verfasserin aut Better Quality Control: Stochastic Approaches to Optimize Properties and Performance of Plasma-Sprayed Coatings 2009 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © ASM International 2009 Abstract Statistical design of experiment (SDE) methodology applied to design and performance testing of plasma-sprayed coatings follows an evolutionary path, usually starting with classic multiparameter screening designs (Plackett-Burman), and progressing through factorial (Taguchi) to limited response surface designs (Box-Behnken). Modern designs of higher dimensionality, such as central composite and D-optimal designs, will provide results with higher predictive power. Complex theoretical models relying on evolutionary algorithms, and application of artificial neuronal networks (ANNs) and fuzzy logic control (FLC) allow estimating the behavior of the complex plasma spray environment through validation either by key experiments or first-principle calculations. In this review, paper general principles of SDE will be discussed and examples be given that underscore the different powers of prediction of individual statistical designs. Basic rules of ANN and FLC will be briefly touched on, and their potential for increased reliability of coating performance through stringent quality control measures assessed. Salient features will be reviewed of studies performed to optimize thermal coating properties and processes reported in the pertinent literature between 2000 and the present. Artificial Neuronal Networks D-optimal designs fuzzy logic control statistical design of experiments Taguchi designs Enthalten in Journal of thermal spray technology Springer US, 1992 19(2009), 4 vom: 03. Sept., Seite 765-778 (DE-627)131101544 (DE-600)1118266-0 (DE-576)038867699 1059-9630 nnns volume:19 year:2009 number:4 day:03 month:09 pages:765-778 https://doi.org/10.1007/s11666-009-9385-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC GBV_ILN_60 GBV_ILN_70 AR 19 2009 4 03 09 765-778 |
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10.1007/s11666-009-9385-3 doi (DE-627)OLC2060558263 (DE-He213)s11666-009-9385-3-p DE-627 ger DE-627 rakwb eng 670 VZ Heimann, Robert B. verfasserin aut Better Quality Control: Stochastic Approaches to Optimize Properties and Performance of Plasma-Sprayed Coatings 2009 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © ASM International 2009 Abstract Statistical design of experiment (SDE) methodology applied to design and performance testing of plasma-sprayed coatings follows an evolutionary path, usually starting with classic multiparameter screening designs (Plackett-Burman), and progressing through factorial (Taguchi) to limited response surface designs (Box-Behnken). Modern designs of higher dimensionality, such as central composite and D-optimal designs, will provide results with higher predictive power. Complex theoretical models relying on evolutionary algorithms, and application of artificial neuronal networks (ANNs) and fuzzy logic control (FLC) allow estimating the behavior of the complex plasma spray environment through validation either by key experiments or first-principle calculations. In this review, paper general principles of SDE will be discussed and examples be given that underscore the different powers of prediction of individual statistical designs. Basic rules of ANN and FLC will be briefly touched on, and their potential for increased reliability of coating performance through stringent quality control measures assessed. Salient features will be reviewed of studies performed to optimize thermal coating properties and processes reported in the pertinent literature between 2000 and the present. Artificial Neuronal Networks D-optimal designs fuzzy logic control statistical design of experiments Taguchi designs Enthalten in Journal of thermal spray technology Springer US, 1992 19(2009), 4 vom: 03. Sept., Seite 765-778 (DE-627)131101544 (DE-600)1118266-0 (DE-576)038867699 1059-9630 nnns volume:19 year:2009 number:4 day:03 month:09 pages:765-778 https://doi.org/10.1007/s11666-009-9385-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC GBV_ILN_60 GBV_ILN_70 AR 19 2009 4 03 09 765-778 |
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10.1007/s11666-009-9385-3 doi (DE-627)OLC2060558263 (DE-He213)s11666-009-9385-3-p DE-627 ger DE-627 rakwb eng 670 VZ Heimann, Robert B. verfasserin aut Better Quality Control: Stochastic Approaches to Optimize Properties and Performance of Plasma-Sprayed Coatings 2009 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © ASM International 2009 Abstract Statistical design of experiment (SDE) methodology applied to design and performance testing of plasma-sprayed coatings follows an evolutionary path, usually starting with classic multiparameter screening designs (Plackett-Burman), and progressing through factorial (Taguchi) to limited response surface designs (Box-Behnken). Modern designs of higher dimensionality, such as central composite and D-optimal designs, will provide results with higher predictive power. Complex theoretical models relying on evolutionary algorithms, and application of artificial neuronal networks (ANNs) and fuzzy logic control (FLC) allow estimating the behavior of the complex plasma spray environment through validation either by key experiments or first-principle calculations. In this review, paper general principles of SDE will be discussed and examples be given that underscore the different powers of prediction of individual statistical designs. Basic rules of ANN and FLC will be briefly touched on, and their potential for increased reliability of coating performance through stringent quality control measures assessed. Salient features will be reviewed of studies performed to optimize thermal coating properties and processes reported in the pertinent literature between 2000 and the present. Artificial Neuronal Networks D-optimal designs fuzzy logic control statistical design of experiments Taguchi designs Enthalten in Journal of thermal spray technology Springer US, 1992 19(2009), 4 vom: 03. Sept., Seite 765-778 (DE-627)131101544 (DE-600)1118266-0 (DE-576)038867699 1059-9630 nnns volume:19 year:2009 number:4 day:03 month:09 pages:765-778 https://doi.org/10.1007/s11666-009-9385-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC GBV_ILN_60 GBV_ILN_70 AR 19 2009 4 03 09 765-778 |
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10.1007/s11666-009-9385-3 doi (DE-627)OLC2060558263 (DE-He213)s11666-009-9385-3-p DE-627 ger DE-627 rakwb eng 670 VZ Heimann, Robert B. verfasserin aut Better Quality Control: Stochastic Approaches to Optimize Properties and Performance of Plasma-Sprayed Coatings 2009 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © ASM International 2009 Abstract Statistical design of experiment (SDE) methodology applied to design and performance testing of plasma-sprayed coatings follows an evolutionary path, usually starting with classic multiparameter screening designs (Plackett-Burman), and progressing through factorial (Taguchi) to limited response surface designs (Box-Behnken). Modern designs of higher dimensionality, such as central composite and D-optimal designs, will provide results with higher predictive power. Complex theoretical models relying on evolutionary algorithms, and application of artificial neuronal networks (ANNs) and fuzzy logic control (FLC) allow estimating the behavior of the complex plasma spray environment through validation either by key experiments or first-principle calculations. In this review, paper general principles of SDE will be discussed and examples be given that underscore the different powers of prediction of individual statistical designs. Basic rules of ANN and FLC will be briefly touched on, and their potential for increased reliability of coating performance through stringent quality control measures assessed. Salient features will be reviewed of studies performed to optimize thermal coating properties and processes reported in the pertinent literature between 2000 and the present. Artificial Neuronal Networks D-optimal designs fuzzy logic control statistical design of experiments Taguchi designs Enthalten in Journal of thermal spray technology Springer US, 1992 19(2009), 4 vom: 03. Sept., Seite 765-778 (DE-627)131101544 (DE-600)1118266-0 (DE-576)038867699 1059-9630 nnns volume:19 year:2009 number:4 day:03 month:09 pages:765-778 https://doi.org/10.1007/s11666-009-9385-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC GBV_ILN_60 GBV_ILN_70 AR 19 2009 4 03 09 765-778 |
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10.1007/s11666-009-9385-3 doi (DE-627)OLC2060558263 (DE-He213)s11666-009-9385-3-p DE-627 ger DE-627 rakwb eng 670 VZ Heimann, Robert B. verfasserin aut Better Quality Control: Stochastic Approaches to Optimize Properties and Performance of Plasma-Sprayed Coatings 2009 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © ASM International 2009 Abstract Statistical design of experiment (SDE) methodology applied to design and performance testing of plasma-sprayed coatings follows an evolutionary path, usually starting with classic multiparameter screening designs (Plackett-Burman), and progressing through factorial (Taguchi) to limited response surface designs (Box-Behnken). Modern designs of higher dimensionality, such as central composite and D-optimal designs, will provide results with higher predictive power. Complex theoretical models relying on evolutionary algorithms, and application of artificial neuronal networks (ANNs) and fuzzy logic control (FLC) allow estimating the behavior of the complex plasma spray environment through validation either by key experiments or first-principle calculations. In this review, paper general principles of SDE will be discussed and examples be given that underscore the different powers of prediction of individual statistical designs. Basic rules of ANN and FLC will be briefly touched on, and their potential for increased reliability of coating performance through stringent quality control measures assessed. Salient features will be reviewed of studies performed to optimize thermal coating properties and processes reported in the pertinent literature between 2000 and the present. Artificial Neuronal Networks D-optimal designs fuzzy logic control statistical design of experiments Taguchi designs Enthalten in Journal of thermal spray technology Springer US, 1992 19(2009), 4 vom: 03. Sept., Seite 765-778 (DE-627)131101544 (DE-600)1118266-0 (DE-576)038867699 1059-9630 nnns volume:19 year:2009 number:4 day:03 month:09 pages:765-778 https://doi.org/10.1007/s11666-009-9385-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC GBV_ILN_60 GBV_ILN_70 AR 19 2009 4 03 09 765-778 |
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Better Quality Control: Stochastic Approaches to Optimize Properties and Performance of Plasma-Sprayed Coatings |
| abstract |
Abstract Statistical design of experiment (SDE) methodology applied to design and performance testing of plasma-sprayed coatings follows an evolutionary path, usually starting with classic multiparameter screening designs (Plackett-Burman), and progressing through factorial (Taguchi) to limited response surface designs (Box-Behnken). Modern designs of higher dimensionality, such as central composite and D-optimal designs, will provide results with higher predictive power. Complex theoretical models relying on evolutionary algorithms, and application of artificial neuronal networks (ANNs) and fuzzy logic control (FLC) allow estimating the behavior of the complex plasma spray environment through validation either by key experiments or first-principle calculations. In this review, paper general principles of SDE will be discussed and examples be given that underscore the different powers of prediction of individual statistical designs. Basic rules of ANN and FLC will be briefly touched on, and their potential for increased reliability of coating performance through stringent quality control measures assessed. Salient features will be reviewed of studies performed to optimize thermal coating properties and processes reported in the pertinent literature between 2000 and the present. © ASM International 2009 |
| abstractGer |
Abstract Statistical design of experiment (SDE) methodology applied to design and performance testing of plasma-sprayed coatings follows an evolutionary path, usually starting with classic multiparameter screening designs (Plackett-Burman), and progressing through factorial (Taguchi) to limited response surface designs (Box-Behnken). Modern designs of higher dimensionality, such as central composite and D-optimal designs, will provide results with higher predictive power. Complex theoretical models relying on evolutionary algorithms, and application of artificial neuronal networks (ANNs) and fuzzy logic control (FLC) allow estimating the behavior of the complex plasma spray environment through validation either by key experiments or first-principle calculations. In this review, paper general principles of SDE will be discussed and examples be given that underscore the different powers of prediction of individual statistical designs. Basic rules of ANN and FLC will be briefly touched on, and their potential for increased reliability of coating performance through stringent quality control measures assessed. Salient features will be reviewed of studies performed to optimize thermal coating properties and processes reported in the pertinent literature between 2000 and the present. © ASM International 2009 |
| abstract_unstemmed |
Abstract Statistical design of experiment (SDE) methodology applied to design and performance testing of plasma-sprayed coatings follows an evolutionary path, usually starting with classic multiparameter screening designs (Plackett-Burman), and progressing through factorial (Taguchi) to limited response surface designs (Box-Behnken). Modern designs of higher dimensionality, such as central composite and D-optimal designs, will provide results with higher predictive power. Complex theoretical models relying on evolutionary algorithms, and application of artificial neuronal networks (ANNs) and fuzzy logic control (FLC) allow estimating the behavior of the complex plasma spray environment through validation either by key experiments or first-principle calculations. In this review, paper general principles of SDE will be discussed and examples be given that underscore the different powers of prediction of individual statistical designs. Basic rules of ANN and FLC will be briefly touched on, and their potential for increased reliability of coating performance through stringent quality control measures assessed. Salient features will be reviewed of studies performed to optimize thermal coating properties and processes reported in the pertinent literature between 2000 and the present. © ASM International 2009 |
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Better Quality Control: Stochastic Approaches to Optimize Properties and Performance of Plasma-Sprayed Coatings |
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https://doi.org/10.1007/s11666-009-9385-3 |
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| doi_str |
10.1007/s11666-009-9385-3 |
| up_date |
2024-07-04T01:38:28.042Z |
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1803610602123296768 |
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Modern designs of higher dimensionality, such as central composite and D-optimal designs, will provide results with higher predictive power. Complex theoretical models relying on evolutionary algorithms, and application of artificial neuronal networks (ANNs) and fuzzy logic control (FLC) allow estimating the behavior of the complex plasma spray environment through validation either by key experiments or first-principle calculations. In this review, paper general principles of SDE will be discussed and examples be given that underscore the different powers of prediction of individual statistical designs. Basic rules of ANN and FLC will be briefly touched on, and their potential for increased reliability of coating performance through stringent quality control measures assessed. 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