Evaluation and characterization of deterministic laser textured surfaces using machine vision

Texturing of surfaces is often done to improve the surface properties. The purpose of this study is to develop a methodology for two dimensional inspection of the quality of textured surfaces automatically using machine vision techniques. The quality is measured in an absolute sense, using the value...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Sreedath, Panat [verfasserIn] Bhat, Sudhanva [verfasserIn] Arunachalam, N. [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2018

Schlagwörter:	Laser texturing Feature isolation Feature identification Radial length function Least-square fitting

Übergeordnetes Werk:	Enthalten in: Measurement - Amsterdam [u.a.] : Elsevier Science, 1983, 135, Seite 537-546
Übergeordnetes Werk:	volume:135 ; pages:537-546

DOI / URN:	10.1016/j.measurement.2018.11.049

Katalog-ID:	ELV002304228

Internformat


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520			\|a Texturing of surfaces is often done to improve the surface properties. The purpose of this study is to develop a methodology for two dimensional inspection of the quality of textured surfaces automatically using machine vision techniques. The quality is measured in an absolute sense, using the value of offset between ideal shapes that inscribe and circumscribe the boundary of a machined feature. This gives an idea about machining accuracy rather than similarity between the machined texture shape and intended ideal shape which most of the existing methods try to measure. The techniques developed are tested on textures created on polished titanium surface using laser micro machining technique. In this study the textured surfaces made up of four different shapes namely circle, ellipse, triangle and square are considered. The automatic classification of texture shapes into the mentioned categories is achieved by finding their correlation with ideal shapes. For the purpose of measuring the machining accuracy, this paper extends the ISO definition of circularity error to ellipse, triangle and square shapes and proposes a method to fit least square triangle and square shapes to a given two dimensional point clouds. The error and the average dimension of the features are calculated from circumscribing, inscribing and least square fit shapes. The results generated by the proposed algorithms are found to be in good agreement with the known dimension values set during machining of the samples and manual measurements done using a stereo-microscope images. This indicate the suitability of this procedure for characterization of textured surfaces for different applications.
650		4	\|a Laser texturing
650		4	\|a Feature isolation
650		4	\|a Feature identification
650		4	\|a Radial length function
650		4	\|a Least-square fitting
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700	1		\|a Arunachalam, N. \|e verfasserin \|4 aut
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936	b	k	\|a 50.21 \|j Messtechnik
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matchkey_str	sreedathpanatbhatsudhanvaarunachalamn:2018----:vlainncaatrztoodtriitcaetxuesr
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publishDate	2018
allfields	10.1016/j.measurement.2018.11.049 doi (DE-627)ELV002304228 (ELSEVIER)S0263-2241(18)31103-5 DE-627 ger DE-627 rda eng 660 DE-600 50.21 bkl Sreedath, Panat verfasserin aut Evaluation and characterization of deterministic laser textured surfaces using machine vision 2018 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Texturing of surfaces is often done to improve the surface properties. The purpose of this study is to develop a methodology for two dimensional inspection of the quality of textured surfaces automatically using machine vision techniques. The quality is measured in an absolute sense, using the value of offset between ideal shapes that inscribe and circumscribe the boundary of a machined feature. This gives an idea about machining accuracy rather than similarity between the machined texture shape and intended ideal shape which most of the existing methods try to measure. The techniques developed are tested on textures created on polished titanium surface using laser micro machining technique. In this study the textured surfaces made up of four different shapes namely circle, ellipse, triangle and square are considered. The automatic classification of texture shapes into the mentioned categories is achieved by finding their correlation with ideal shapes. For the purpose of measuring the machining accuracy, this paper extends the ISO definition of circularity error to ellipse, triangle and square shapes and proposes a method to fit least square triangle and square shapes to a given two dimensional point clouds. The error and the average dimension of the features are calculated from circumscribing, inscribing and least square fit shapes. The results generated by the proposed algorithms are found to be in good agreement with the known dimension values set during machining of the samples and manual measurements done using a stereo-microscope images. This indicate the suitability of this procedure for characterization of textured surfaces for different applications. Laser texturing Feature isolation Feature identification Radial length function Least-square fitting Bhat, Sudhanva verfasserin aut Arunachalam, N. verfasserin aut Enthalten in Measurement Amsterdam [u.a.] : Elsevier Science, 1983 135, Seite 537-546 Online-Ressource (DE-627)320404927 (DE-600)2000550-7 (DE-576)259484342 nnns volume:135 pages:537-546 GBV_USEFLAG_U SYSFLAG_U GBV_ELV SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 50.21 Messtechnik AR 135 537-546
spelling	10.1016/j.measurement.2018.11.049 doi (DE-627)ELV002304228 (ELSEVIER)S0263-2241(18)31103-5 DE-627 ger DE-627 rda eng 660 DE-600 50.21 bkl Sreedath, Panat verfasserin aut Evaluation and characterization of deterministic laser textured surfaces using machine vision 2018 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Texturing of surfaces is often done to improve the surface properties. The purpose of this study is to develop a methodology for two dimensional inspection of the quality of textured surfaces automatically using machine vision techniques. The quality is measured in an absolute sense, using the value of offset between ideal shapes that inscribe and circumscribe the boundary of a machined feature. This gives an idea about machining accuracy rather than similarity between the machined texture shape and intended ideal shape which most of the existing methods try to measure. The techniques developed are tested on textures created on polished titanium surface using laser micro machining technique. In this study the textured surfaces made up of four different shapes namely circle, ellipse, triangle and square are considered. The automatic classification of texture shapes into the mentioned categories is achieved by finding their correlation with ideal shapes. For the purpose of measuring the machining accuracy, this paper extends the ISO definition of circularity error to ellipse, triangle and square shapes and proposes a method to fit least square triangle and square shapes to a given two dimensional point clouds. The error and the average dimension of the features are calculated from circumscribing, inscribing and least square fit shapes. The results generated by the proposed algorithms are found to be in good agreement with the known dimension values set during machining of the samples and manual measurements done using a stereo-microscope images. This indicate the suitability of this procedure for characterization of textured surfaces for different applications. Laser texturing Feature isolation Feature identification Radial length function Least-square fitting Bhat, Sudhanva verfasserin aut Arunachalam, N. verfasserin aut Enthalten in Measurement Amsterdam [u.a.] : Elsevier Science, 1983 135, Seite 537-546 Online-Ressource (DE-627)320404927 (DE-600)2000550-7 (DE-576)259484342 nnns volume:135 pages:537-546 GBV_USEFLAG_U SYSFLAG_U GBV_ELV SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 50.21 Messtechnik AR 135 537-546
allfields_unstemmed	10.1016/j.measurement.2018.11.049 doi (DE-627)ELV002304228 (ELSEVIER)S0263-2241(18)31103-5 DE-627 ger DE-627 rda eng 660 DE-600 50.21 bkl Sreedath, Panat verfasserin aut Evaluation and characterization of deterministic laser textured surfaces using machine vision 2018 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Texturing of surfaces is often done to improve the surface properties. The purpose of this study is to develop a methodology for two dimensional inspection of the quality of textured surfaces automatically using machine vision techniques. The quality is measured in an absolute sense, using the value of offset between ideal shapes that inscribe and circumscribe the boundary of a machined feature. This gives an idea about machining accuracy rather than similarity between the machined texture shape and intended ideal shape which most of the existing methods try to measure. The techniques developed are tested on textures created on polished titanium surface using laser micro machining technique. In this study the textured surfaces made up of four different shapes namely circle, ellipse, triangle and square are considered. The automatic classification of texture shapes into the mentioned categories is achieved by finding their correlation with ideal shapes. For the purpose of measuring the machining accuracy, this paper extends the ISO definition of circularity error to ellipse, triangle and square shapes and proposes a method to fit least square triangle and square shapes to a given two dimensional point clouds. The error and the average dimension of the features are calculated from circumscribing, inscribing and least square fit shapes. The results generated by the proposed algorithms are found to be in good agreement with the known dimension values set during machining of the samples and manual measurements done using a stereo-microscope images. This indicate the suitability of this procedure for characterization of textured surfaces for different applications. Laser texturing Feature isolation Feature identification Radial length function Least-square fitting Bhat, Sudhanva verfasserin aut Arunachalam, N. verfasserin aut Enthalten in Measurement Amsterdam [u.a.] : Elsevier Science, 1983 135, Seite 537-546 Online-Ressource (DE-627)320404927 (DE-600)2000550-7 (DE-576)259484342 nnns volume:135 pages:537-546 GBV_USEFLAG_U SYSFLAG_U GBV_ELV SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 50.21 Messtechnik AR 135 537-546
allfieldsGer	10.1016/j.measurement.2018.11.049 doi (DE-627)ELV002304228 (ELSEVIER)S0263-2241(18)31103-5 DE-627 ger DE-627 rda eng 660 DE-600 50.21 bkl Sreedath, Panat verfasserin aut Evaluation and characterization of deterministic laser textured surfaces using machine vision 2018 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Texturing of surfaces is often done to improve the surface properties. The purpose of this study is to develop a methodology for two dimensional inspection of the quality of textured surfaces automatically using machine vision techniques. The quality is measured in an absolute sense, using the value of offset between ideal shapes that inscribe and circumscribe the boundary of a machined feature. This gives an idea about machining accuracy rather than similarity between the machined texture shape and intended ideal shape which most of the existing methods try to measure. The techniques developed are tested on textures created on polished titanium surface using laser micro machining technique. In this study the textured surfaces made up of four different shapes namely circle, ellipse, triangle and square are considered. The automatic classification of texture shapes into the mentioned categories is achieved by finding their correlation with ideal shapes. For the purpose of measuring the machining accuracy, this paper extends the ISO definition of circularity error to ellipse, triangle and square shapes and proposes a method to fit least square triangle and square shapes to a given two dimensional point clouds. The error and the average dimension of the features are calculated from circumscribing, inscribing and least square fit shapes. The results generated by the proposed algorithms are found to be in good agreement with the known dimension values set during machining of the samples and manual measurements done using a stereo-microscope images. This indicate the suitability of this procedure for characterization of textured surfaces for different applications. Laser texturing Feature isolation Feature identification Radial length function Least-square fitting Bhat, Sudhanva verfasserin aut Arunachalam, N. verfasserin aut Enthalten in Measurement Amsterdam [u.a.] : Elsevier Science, 1983 135, Seite 537-546 Online-Ressource (DE-627)320404927 (DE-600)2000550-7 (DE-576)259484342 nnns volume:135 pages:537-546 GBV_USEFLAG_U SYSFLAG_U GBV_ELV SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 50.21 Messtechnik AR 135 537-546
allfieldsSound	10.1016/j.measurement.2018.11.049 doi (DE-627)ELV002304228 (ELSEVIER)S0263-2241(18)31103-5 DE-627 ger DE-627 rda eng 660 DE-600 50.21 bkl Sreedath, Panat verfasserin aut Evaluation and characterization of deterministic laser textured surfaces using machine vision 2018 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Texturing of surfaces is often done to improve the surface properties. The purpose of this study is to develop a methodology for two dimensional inspection of the quality of textured surfaces automatically using machine vision techniques. The quality is measured in an absolute sense, using the value of offset between ideal shapes that inscribe and circumscribe the boundary of a machined feature. This gives an idea about machining accuracy rather than similarity between the machined texture shape and intended ideal shape which most of the existing methods try to measure. The techniques developed are tested on textures created on polished titanium surface using laser micro machining technique. In this study the textured surfaces made up of four different shapes namely circle, ellipse, triangle and square are considered. The automatic classification of texture shapes into the mentioned categories is achieved by finding their correlation with ideal shapes. For the purpose of measuring the machining accuracy, this paper extends the ISO definition of circularity error to ellipse, triangle and square shapes and proposes a method to fit least square triangle and square shapes to a given two dimensional point clouds. The error and the average dimension of the features are calculated from circumscribing, inscribing and least square fit shapes. The results generated by the proposed algorithms are found to be in good agreement with the known dimension values set during machining of the samples and manual measurements done using a stereo-microscope images. This indicate the suitability of this procedure for characterization of textured surfaces for different applications. Laser texturing Feature isolation Feature identification Radial length function Least-square fitting Bhat, Sudhanva verfasserin aut Arunachalam, N. verfasserin aut Enthalten in Measurement Amsterdam [u.a.] : Elsevier Science, 1983 135, Seite 537-546 Online-Ressource (DE-627)320404927 (DE-600)2000550-7 (DE-576)259484342 nnns volume:135 pages:537-546 GBV_USEFLAG_U SYSFLAG_U GBV_ELV SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 50.21 Messtechnik AR 135 537-546
language	English
source	Enthalten in Measurement 135, Seite 537-546 volume:135 pages:537-546
sourceStr	Enthalten in Measurement 135, Seite 537-546 volume:135 pages:537-546
format_phy_str_mv	Article
bklname	Messtechnik
institution	findex.gbv.de
topic_facet	Laser texturing Feature isolation Feature identification Radial length function Least-square fitting
dewey-raw	660
isfreeaccess_bool	false
container_title	Measurement
authorswithroles_txt_mv	Sreedath, Panat @@aut@@ Bhat, Sudhanva @@aut@@ Arunachalam, N. @@aut@@
publishDateDaySort_date	2018-01-01T00:00:00Z
hierarchy_top_id	320404927
dewey-sort	3660
id	ELV002304228
language_de	englisch
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author	Sreedath, Panat
spellingShingle	Sreedath, Panat ddc 660 bkl 50.21 misc Laser texturing misc Feature isolation misc Feature identification misc Radial length function misc Least-square fitting Evaluation and characterization of deterministic laser textured surfaces using machine vision
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topic_title	660 DE-600 50.21 bkl Evaluation and characterization of deterministic laser textured surfaces using machine vision Laser texturing Feature isolation Feature identification Radial length function Least-square fitting
topic	ddc 660 bkl 50.21 misc Laser texturing misc Feature isolation misc Feature identification misc Radial length function misc Least-square fitting
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title	Evaluation and characterization of deterministic laser textured surfaces using machine vision
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title_full	Evaluation and characterization of deterministic laser textured surfaces using machine vision
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title_sort	evaluation and characterization of deterministic laser textured surfaces using machine vision
title_auth	Evaluation and characterization of deterministic laser textured surfaces using machine vision
abstract	Texturing of surfaces is often done to improve the surface properties. The purpose of this study is to develop a methodology for two dimensional inspection of the quality of textured surfaces automatically using machine vision techniques. The quality is measured in an absolute sense, using the value of offset between ideal shapes that inscribe and circumscribe the boundary of a machined feature. This gives an idea about machining accuracy rather than similarity between the machined texture shape and intended ideal shape which most of the existing methods try to measure. The techniques developed are tested on textures created on polished titanium surface using laser micro machining technique. In this study the textured surfaces made up of four different shapes namely circle, ellipse, triangle and square are considered. The automatic classification of texture shapes into the mentioned categories is achieved by finding their correlation with ideal shapes. For the purpose of measuring the machining accuracy, this paper extends the ISO definition of circularity error to ellipse, triangle and square shapes and proposes a method to fit least square triangle and square shapes to a given two dimensional point clouds. The error and the average dimension of the features are calculated from circumscribing, inscribing and least square fit shapes. The results generated by the proposed algorithms are found to be in good agreement with the known dimension values set during machining of the samples and manual measurements done using a stereo-microscope images. This indicate the suitability of this procedure for characterization of textured surfaces for different applications.
abstractGer	Texturing of surfaces is often done to improve the surface properties. The purpose of this study is to develop a methodology for two dimensional inspection of the quality of textured surfaces automatically using machine vision techniques. The quality is measured in an absolute sense, using the value of offset between ideal shapes that inscribe and circumscribe the boundary of a machined feature. This gives an idea about machining accuracy rather than similarity between the machined texture shape and intended ideal shape which most of the existing methods try to measure. The techniques developed are tested on textures created on polished titanium surface using laser micro machining technique. In this study the textured surfaces made up of four different shapes namely circle, ellipse, triangle and square are considered. The automatic classification of texture shapes into the mentioned categories is achieved by finding their correlation with ideal shapes. For the purpose of measuring the machining accuracy, this paper extends the ISO definition of circularity error to ellipse, triangle and square shapes and proposes a method to fit least square triangle and square shapes to a given two dimensional point clouds. The error and the average dimension of the features are calculated from circumscribing, inscribing and least square fit shapes. The results generated by the proposed algorithms are found to be in good agreement with the known dimension values set during machining of the samples and manual measurements done using a stereo-microscope images. This indicate the suitability of this procedure for characterization of textured surfaces for different applications.
abstract_unstemmed	Texturing of surfaces is often done to improve the surface properties. The purpose of this study is to develop a methodology for two dimensional inspection of the quality of textured surfaces automatically using machine vision techniques. The quality is measured in an absolute sense, using the value of offset between ideal shapes that inscribe and circumscribe the boundary of a machined feature. This gives an idea about machining accuracy rather than similarity between the machined texture shape and intended ideal shape which most of the existing methods try to measure. The techniques developed are tested on textures created on polished titanium surface using laser micro machining technique. In this study the textured surfaces made up of four different shapes namely circle, ellipse, triangle and square are considered. The automatic classification of texture shapes into the mentioned categories is achieved by finding their correlation with ideal shapes. For the purpose of measuring the machining accuracy, this paper extends the ISO definition of circularity error to ellipse, triangle and square shapes and proposes a method to fit least square triangle and square shapes to a given two dimensional point clouds. The error and the average dimension of the features are calculated from circumscribing, inscribing and least square fit shapes. The results generated by the proposed algorithms are found to be in good agreement with the known dimension values set during machining of the samples and manual measurements done using a stereo-microscope images. This indicate the suitability of this procedure for characterization of textured surfaces for different applications.
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title_short	Evaluation and characterization of deterministic laser textured surfaces using machine vision
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up_date	2024-07-07T00:17:10.326Z
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Evaluation and characterization of deterministic laser textured surfaces using machine vision

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