Rapid and visualized residual strength prediction of Scarf-repaired laminates using hierarchical surrogate model

Defects and/or damage of the bonding surfaces of scarf-repaired CFRP laminates can be detected with nondestructive testing technologies. However, it is difficult to judge whether the defect and/or damage is severe enough to affect the normal use of the repaired structure. Destructive testing is usua...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Xu, Yao [verfasserIn] Tao, Chongcong [verfasserIn] Zhang, Chao [verfasserIn] Ji, Hongli [verfasserIn] Qiu, Jinhao [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	Scarf-repaired laminates Residual strength Convolutional neural network Heat map

Übergeordnetes Werk:	Enthalten in: Composite structures - Amsterdam : Elsevier, 1983, 306
Übergeordnetes Werk:	volume:306

DOI / URN:	10.1016/j.compstruct.2022.116597

Katalog-ID:	ELV009033955

Internformat


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520			\|a Defects and/or damage of the bonding surfaces of scarf-repaired CFRP laminates can be detected with nondestructive testing technologies. However, it is difficult to judge whether the defect and/or damage is severe enough to affect the normal use of the repaired structure. Destructive testing is usually required to access the residual strength, which is not ideal for real world applications. In this paper, a nondestructive strength prediction approach is proposed that comprises of a high-fidelity finite element model, an efficient convolutional neural network model and visualized damage-strength heat map model to form a hierarchical surrogate model. The tensile strength of the structures can be rapidly obtained with an average error of 1.4% by comprehensively considering the angle of the scarf joints, the interfacial strength of the adhesive film, the stacking sequence of laminas, together with the size and location of the damage which are visualized by nondestructive testing. In addition, the residual tensile strength of repaired structures containing damage can be calculated more conveniently and intuitively with simple multiplication operation by the proposed heat map model without scarifies in accuracy.
650		4	\|a Scarf-repaired laminates
650		4	\|a Residual strength
650		4	\|a Convolutional neural network
650		4	\|a Heat map
700	1		\|a Tao, Chongcong \|e verfasserin \|4 aut
700	1		\|a Zhang, Chao \|e verfasserin \|4 aut
700	1		\|a Ji, Hongli \|e verfasserin \|4 aut
700	1		\|a Qiu, Jinhao \|e verfasserin \|4 aut
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Indexfelder

author_variant	y x yx c t ct c z cz h j hj j q jq
matchkey_str	article:02638223:2022----::aiadiulzdeiulteghrdcinfcrrpielmntssn
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publishDate	2022
allfields	10.1016/j.compstruct.2022.116597 doi (DE-627)ELV009033955 (ELSEVIER)S0263-8223(22)01329-0 DE-627 ger DE-627 rda eng 670 DE-600 51.75 bkl Xu, Yao verfasserin aut Rapid and visualized residual strength prediction of Scarf-repaired laminates using hierarchical surrogate model 2022 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Defects and/or damage of the bonding surfaces of scarf-repaired CFRP laminates can be detected with nondestructive testing technologies. However, it is difficult to judge whether the defect and/or damage is severe enough to affect the normal use of the repaired structure. Destructive testing is usually required to access the residual strength, which is not ideal for real world applications. In this paper, a nondestructive strength prediction approach is proposed that comprises of a high-fidelity finite element model, an efficient convolutional neural network model and visualized damage-strength heat map model to form a hierarchical surrogate model. The tensile strength of the structures can be rapidly obtained with an average error of 1.4% by comprehensively considering the angle of the scarf joints, the interfacial strength of the adhesive film, the stacking sequence of laminas, together with the size and location of the damage which are visualized by nondestructive testing. In addition, the residual tensile strength of repaired structures containing damage can be calculated more conveniently and intuitively with simple multiplication operation by the proposed heat map model without scarifies in accuracy. Scarf-repaired laminates Residual strength Convolutional neural network Heat map Tao, Chongcong verfasserin aut Zhang, Chao verfasserin aut Ji, Hongli verfasserin aut Qiu, Jinhao verfasserin aut Enthalten in Composite structures Amsterdam : Elsevier, 1983 306 (DE-627)320509044 (DE-600)2013177-X (DE-576)094531447 0263-8223 nnns volume:306 GBV_USEFLAG_U SYSFLAG_U GBV_ELV 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 51.75 Verbundwerkstoffe Schichtstoffe AR 306
spelling	10.1016/j.compstruct.2022.116597 doi (DE-627)ELV009033955 (ELSEVIER)S0263-8223(22)01329-0 DE-627 ger DE-627 rda eng 670 DE-600 51.75 bkl Xu, Yao verfasserin aut Rapid and visualized residual strength prediction of Scarf-repaired laminates using hierarchical surrogate model 2022 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Defects and/or damage of the bonding surfaces of scarf-repaired CFRP laminates can be detected with nondestructive testing technologies. However, it is difficult to judge whether the defect and/or damage is severe enough to affect the normal use of the repaired structure. Destructive testing is usually required to access the residual strength, which is not ideal for real world applications. In this paper, a nondestructive strength prediction approach is proposed that comprises of a high-fidelity finite element model, an efficient convolutional neural network model and visualized damage-strength heat map model to form a hierarchical surrogate model. The tensile strength of the structures can be rapidly obtained with an average error of 1.4% by comprehensively considering the angle of the scarf joints, the interfacial strength of the adhesive film, the stacking sequence of laminas, together with the size and location of the damage which are visualized by nondestructive testing. In addition, the residual tensile strength of repaired structures containing damage can be calculated more conveniently and intuitively with simple multiplication operation by the proposed heat map model without scarifies in accuracy. Scarf-repaired laminates Residual strength Convolutional neural network Heat map Tao, Chongcong verfasserin aut Zhang, Chao verfasserin aut Ji, Hongli verfasserin aut Qiu, Jinhao verfasserin aut Enthalten in Composite structures Amsterdam : Elsevier, 1983 306 (DE-627)320509044 (DE-600)2013177-X (DE-576)094531447 0263-8223 nnns volume:306 GBV_USEFLAG_U SYSFLAG_U GBV_ELV 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 51.75 Verbundwerkstoffe Schichtstoffe AR 306
allfields_unstemmed	10.1016/j.compstruct.2022.116597 doi (DE-627)ELV009033955 (ELSEVIER)S0263-8223(22)01329-0 DE-627 ger DE-627 rda eng 670 DE-600 51.75 bkl Xu, Yao verfasserin aut Rapid and visualized residual strength prediction of Scarf-repaired laminates using hierarchical surrogate model 2022 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Defects and/or damage of the bonding surfaces of scarf-repaired CFRP laminates can be detected with nondestructive testing technologies. However, it is difficult to judge whether the defect and/or damage is severe enough to affect the normal use of the repaired structure. Destructive testing is usually required to access the residual strength, which is not ideal for real world applications. In this paper, a nondestructive strength prediction approach is proposed that comprises of a high-fidelity finite element model, an efficient convolutional neural network model and visualized damage-strength heat map model to form a hierarchical surrogate model. The tensile strength of the structures can be rapidly obtained with an average error of 1.4% by comprehensively considering the angle of the scarf joints, the interfacial strength of the adhesive film, the stacking sequence of laminas, together with the size and location of the damage which are visualized by nondestructive testing. In addition, the residual tensile strength of repaired structures containing damage can be calculated more conveniently and intuitively with simple multiplication operation by the proposed heat map model without scarifies in accuracy. Scarf-repaired laminates Residual strength Convolutional neural network Heat map Tao, Chongcong verfasserin aut Zhang, Chao verfasserin aut Ji, Hongli verfasserin aut Qiu, Jinhao verfasserin aut Enthalten in Composite structures Amsterdam : Elsevier, 1983 306 (DE-627)320509044 (DE-600)2013177-X (DE-576)094531447 0263-8223 nnns volume:306 GBV_USEFLAG_U SYSFLAG_U GBV_ELV 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 51.75 Verbundwerkstoffe Schichtstoffe AR 306
allfieldsGer	10.1016/j.compstruct.2022.116597 doi (DE-627)ELV009033955 (ELSEVIER)S0263-8223(22)01329-0 DE-627 ger DE-627 rda eng 670 DE-600 51.75 bkl Xu, Yao verfasserin aut Rapid and visualized residual strength prediction of Scarf-repaired laminates using hierarchical surrogate model 2022 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Defects and/or damage of the bonding surfaces of scarf-repaired CFRP laminates can be detected with nondestructive testing technologies. However, it is difficult to judge whether the defect and/or damage is severe enough to affect the normal use of the repaired structure. Destructive testing is usually required to access the residual strength, which is not ideal for real world applications. In this paper, a nondestructive strength prediction approach is proposed that comprises of a high-fidelity finite element model, an efficient convolutional neural network model and visualized damage-strength heat map model to form a hierarchical surrogate model. The tensile strength of the structures can be rapidly obtained with an average error of 1.4% by comprehensively considering the angle of the scarf joints, the interfacial strength of the adhesive film, the stacking sequence of laminas, together with the size and location of the damage which are visualized by nondestructive testing. In addition, the residual tensile strength of repaired structures containing damage can be calculated more conveniently and intuitively with simple multiplication operation by the proposed heat map model without scarifies in accuracy. Scarf-repaired laminates Residual strength Convolutional neural network Heat map Tao, Chongcong verfasserin aut Zhang, Chao verfasserin aut Ji, Hongli verfasserin aut Qiu, Jinhao verfasserin aut Enthalten in Composite structures Amsterdam : Elsevier, 1983 306 (DE-627)320509044 (DE-600)2013177-X (DE-576)094531447 0263-8223 nnns volume:306 GBV_USEFLAG_U SYSFLAG_U GBV_ELV 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 51.75 Verbundwerkstoffe Schichtstoffe AR 306
allfieldsSound	10.1016/j.compstruct.2022.116597 doi (DE-627)ELV009033955 (ELSEVIER)S0263-8223(22)01329-0 DE-627 ger DE-627 rda eng 670 DE-600 51.75 bkl Xu, Yao verfasserin aut Rapid and visualized residual strength prediction of Scarf-repaired laminates using hierarchical surrogate model 2022 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Defects and/or damage of the bonding surfaces of scarf-repaired CFRP laminates can be detected with nondestructive testing technologies. However, it is difficult to judge whether the defect and/or damage is severe enough to affect the normal use of the repaired structure. Destructive testing is usually required to access the residual strength, which is not ideal for real world applications. In this paper, a nondestructive strength prediction approach is proposed that comprises of a high-fidelity finite element model, an efficient convolutional neural network model and visualized damage-strength heat map model to form a hierarchical surrogate model. The tensile strength of the structures can be rapidly obtained with an average error of 1.4% by comprehensively considering the angle of the scarf joints, the interfacial strength of the adhesive film, the stacking sequence of laminas, together with the size and location of the damage which are visualized by nondestructive testing. In addition, the residual tensile strength of repaired structures containing damage can be calculated more conveniently and intuitively with simple multiplication operation by the proposed heat map model without scarifies in accuracy. Scarf-repaired laminates Residual strength Convolutional neural network Heat map Tao, Chongcong verfasserin aut Zhang, Chao verfasserin aut Ji, Hongli verfasserin aut Qiu, Jinhao verfasserin aut Enthalten in Composite structures Amsterdam : Elsevier, 1983 306 (DE-627)320509044 (DE-600)2013177-X (DE-576)094531447 0263-8223 nnns volume:306 GBV_USEFLAG_U SYSFLAG_U GBV_ELV 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 51.75 Verbundwerkstoffe Schichtstoffe AR 306
language	English
source	Enthalten in Composite structures 306 volume:306
sourceStr	Enthalten in Composite structures 306 volume:306
format_phy_str_mv	Article
bklname	Verbundwerkstoffe Schichtstoffe
institution	findex.gbv.de
topic_facet	Scarf-repaired laminates Residual strength Convolutional neural network Heat map
dewey-raw	670
isfreeaccess_bool	false
container_title	Composite structures
authorswithroles_txt_mv	Xu, Yao @@aut@@ Tao, Chongcong @@aut@@ Zhang, Chao @@aut@@ Ji, Hongli @@aut@@ Qiu, Jinhao @@aut@@
publishDateDaySort_date	2022-01-01T00:00:00Z
hierarchy_top_id	320509044
dewey-sort	3670
id	ELV009033955
language_de	englisch
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author	Xu, Yao
spellingShingle	Xu, Yao ddc 670 bkl 51.75 misc Scarf-repaired laminates misc Residual strength misc Convolutional neural network misc Heat map Rapid and visualized residual strength prediction of Scarf-repaired laminates using hierarchical surrogate model
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topic_title	670 DE-600 51.75 bkl Rapid and visualized residual strength prediction of Scarf-repaired laminates using hierarchical surrogate model Scarf-repaired laminates Residual strength Convolutional neural network Heat map
topic	ddc 670 bkl 51.75 misc Scarf-repaired laminates misc Residual strength misc Convolutional neural network misc Heat map
topic_unstemmed	ddc 670 bkl 51.75 misc Scarf-repaired laminates misc Residual strength misc Convolutional neural network misc Heat map
topic_browse	ddc 670 bkl 51.75 misc Scarf-repaired laminates misc Residual strength misc Convolutional neural network misc Heat map
format_facet	Elektronische Aufsätze Aufsätze Elektronische Ressource
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title	Rapid and visualized residual strength prediction of Scarf-repaired laminates using hierarchical surrogate model
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title_full	Rapid and visualized residual strength prediction of Scarf-repaired laminates using hierarchical surrogate model
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author_browse	Xu, Yao Tao, Chongcong Zhang, Chao Ji, Hongli Qiu, Jinhao
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title_sort	rapid and visualized residual strength prediction of scarf-repaired laminates using hierarchical surrogate model
title_auth	Rapid and visualized residual strength prediction of Scarf-repaired laminates using hierarchical surrogate model
abstract	Defects and/or damage of the bonding surfaces of scarf-repaired CFRP laminates can be detected with nondestructive testing technologies. However, it is difficult to judge whether the defect and/or damage is severe enough to affect the normal use of the repaired structure. Destructive testing is usually required to access the residual strength, which is not ideal for real world applications. In this paper, a nondestructive strength prediction approach is proposed that comprises of a high-fidelity finite element model, an efficient convolutional neural network model and visualized damage-strength heat map model to form a hierarchical surrogate model. The tensile strength of the structures can be rapidly obtained with an average error of 1.4% by comprehensively considering the angle of the scarf joints, the interfacial strength of the adhesive film, the stacking sequence of laminas, together with the size and location of the damage which are visualized by nondestructive testing. In addition, the residual tensile strength of repaired structures containing damage can be calculated more conveniently and intuitively with simple multiplication operation by the proposed heat map model without scarifies in accuracy.
abstractGer	Defects and/or damage of the bonding surfaces of scarf-repaired CFRP laminates can be detected with nondestructive testing technologies. However, it is difficult to judge whether the defect and/or damage is severe enough to affect the normal use of the repaired structure. Destructive testing is usually required to access the residual strength, which is not ideal for real world applications. In this paper, a nondestructive strength prediction approach is proposed that comprises of a high-fidelity finite element model, an efficient convolutional neural network model and visualized damage-strength heat map model to form a hierarchical surrogate model. The tensile strength of the structures can be rapidly obtained with an average error of 1.4% by comprehensively considering the angle of the scarf joints, the interfacial strength of the adhesive film, the stacking sequence of laminas, together with the size and location of the damage which are visualized by nondestructive testing. In addition, the residual tensile strength of repaired structures containing damage can be calculated more conveniently and intuitively with simple multiplication operation by the proposed heat map model without scarifies in accuracy.
abstract_unstemmed	Defects and/or damage of the bonding surfaces of scarf-repaired CFRP laminates can be detected with nondestructive testing technologies. However, it is difficult to judge whether the defect and/or damage is severe enough to affect the normal use of the repaired structure. Destructive testing is usually required to access the residual strength, which is not ideal for real world applications. In this paper, a nondestructive strength prediction approach is proposed that comprises of a high-fidelity finite element model, an efficient convolutional neural network model and visualized damage-strength heat map model to form a hierarchical surrogate model. The tensile strength of the structures can be rapidly obtained with an average error of 1.4% by comprehensively considering the angle of the scarf joints, the interfacial strength of the adhesive film, the stacking sequence of laminas, together with the size and location of the damage which are visualized by nondestructive testing. In addition, the residual tensile strength of repaired structures containing damage can be calculated more conveniently and intuitively with simple multiplication operation by the proposed heat map model without scarifies in accuracy.
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title_short	Rapid and visualized residual strength prediction of Scarf-repaired laminates using hierarchical surrogate model
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author2	Tao, Chongcong Zhang, Chao Ji, Hongli Qiu, Jinhao
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doi_str	10.1016/j.compstruct.2022.116597
up_date	2024-07-06T21:45:32.284Z
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Rapid and visualized residual strength prediction of Scarf-repaired laminates using hierarchical surrogate model

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