UNIVERSIDADE FEDERAL DE SANTA CATARINA
CENTRO TECNOLÓGICO
PROGRAMA DE PÓS-GRADUAÇÃO EM ENGENHARIA MECÂNICA
BERNARDO CASSIMIRO FONSECA DE OLIVEIRA
INSPECTION OF IMPACT DAMAGES IN CARBON FIBRE
REINFORCED PLASTIC PLATES THROUGH THE FUSION OF
IMAGES FROM OPTICAL LOCK-IN THERMOGRAPHY AND
OPTICAL SQUARE-PULSE SHEAROGRAPHY TECHNIQUES
FLORIANÓPOLIS
2019Bernardo Cassimiro Fonseca de Oliveira
INSPECTION OF IMPACT DAMAGES IN CARBON FIBRE REINFORCED PLASTIC PLATES THROUGH THE FUSION OF IMAGES FROM OPTICAL LOCK-IN THERMOGRAPHY AND OPTICAL SQUARE-PULSE SHEAROGRAPHY
TECHNIQUES
Tese de doutorado submetida ao Programa de Pós-Graduação em Engenharia Mecânica da Universidade Federal de Santa Catarina para obtenção do grau de Doutor em Engenharia Mecânica.
Orientador: Prof. Dr. Armando Albertazzi Gonçalves Júnior.
Coorientadores: Prof. Dr. Crhistian Raffaelo Baldo e Prof. Dr. Gustavo Daniel Donatelli.
Florianópolis 2019
Bernardo Cassimiro Fonseca de Oliveira
INSPECTION OF IMPACT DAMAGES IN CARBON FIBRE REINFORCED PLASTIC PLATES THROUGH THE FUSION OF IMAGES FROM OPTICAL LOCK-IN THERMOGRAPHY AND OPTICAL SQUARE-PULSE SHEAROGRAPHY
TECHNIQUES
O presente trabalho em nível de doutorado foi avaliado e aprovado por banca examinadora composta pelos seguintes membros:
Prof. Meinhard Sesselmann, Dr. Eng.
Universidade Federal de Minas Gerais (relator, videoconferência) Marc Georges, PhD.
Universidade de Liège (videoconferência) Prof. Marcelo Stemmer, Dr.-Ing. Universidade Federal de Santa Catarina Profa. Analucia Vieira Fantin, Dr. Eng. Universidade do Estado de Santa Catarina
Certificamos que esta é a versão original e final do trabalho de conclusão que foi julgado adequado para obtenção do título de Doutor em Engenharia Mecânica.
_______________________________ Prof. Jonny Carlos da Silva, Dr. Eng.
Coordenador do Programa de Pós-Graduação em Engenharia Mecânica
_______________________________
Prof. Armando Albertazzi Gonçalves Júnior, Dr. Eng. Orientador
Florianópolis, 20 de agosto de 2019.
Armando Albertazzi Goncalves Junior:23249501549
Assinado de forma digital por Armando Albertazzi Goncalves Junior:23249501549 Dados: 2019.08.22 20:12:31 -03'00'
Assinado de forma digital por Jonny Carlos da Silva:51451506449 Dados: 2019.08.23 11:13:57 -03'00'
A meus pais, Simone e Domingos, à minha irmã, Sabrina,
à Elise e à Nilce.
AGRADECIMENTOS
Aos meus pais, Simone e Domingos, e à minha irmã, Sabrina, pelo amor e apoio dado a mim em todos os momentos.
À Elise, cuja presença foi essencial em todos os momentos.
À Nilce, por todo carinho desde sempre, não alterado pela distância. Ao Professor Armando, pela orientação e paciência.
Ao Professor Crhistian, pela coorientação e pelos auxílios com complicações do projeto. Ao Professor Gustavo Donatelli, pelos direcionamentos nesta etapa, com a qual pude tanto aprender.
Ao Fernando Lopez, cujas orientações foram tão importantes.
Aos membros da banca, por terem aceitado o convite de avaliar meu trabalho. Ao Artur, grande amigo, com quem se pode contar.
Aos grandes amigos e colegas Herberth e Lucas, que puderam dividir comigo momentos durante essa jornada.
Ao Miguel, mais uma vez pela amizade e disponibilidade, pelas conversas nos momentos difíceis.
Ao Dr. Estiven, não somente colega, como também um amigo, a quem se pode recorrer quando as soluções não aparecem.
Danken möchte ich meinen Freunden Philipp und Alex, die in schweren Momenten so wichtig waren.
Ein besonderer Dank gilt Gabriela Falkenburger. Es ist unmöglich, zu bewerten, wie ihr Beisein notwendig war, um meine Bildung zu verbessern.
To my friend Luca, with who I could complain about the wrong things in the world. Ao Guilherme, Marcos, Chico, Tiago, Vinícius, Bernardo II, Ahryman, Hiago, Magrão, Patryk e outros, pela convivência, amizade, cafés e churrascos.
Aos amigos antigos, agora também velhos, Thiago, Tino, Leo, Bill, Xandeco e outros, pelas possibilidades de escape, que foram escassas graças ao pouco tempo, mas vitais.
A todos os membros do LABMETRO e da CERTI que contribuíram direta ou indiretamente com o projeto, em especial Rosana, Analucia, Mauro, Daniel, Élcio e Thiago Linhares.
À CAPES, DFG, DAAD e Petrobras, pelo subsídio dado ao projeto e à minha formação. À UFSC, pela forte formação nesses 12 anos.
“Somewhere, something incredible is waiting to be known.” (Carl Sagan)
RESUMO
A preservação do meio ambiente tem estado mais em evidência ultimamente devido a alarmantes resultados de estudos sobre os efeitos de emissões de gases na atmosfera. A redução de peso é uma forma de lidar com esse problema e melhorar o desempenho de máquinas que emitem esses gases. Uma alternativa interessante nesse sentido é a substituição de ligas metálicas por materiais compósitos, como os plásticos reforçados por fibras de carbono (PRFCs), devido à sua capacidade de ter um comportamento mecânico similar ao dos metais tendo apenas uma fração de seu peso. Entretanto, materiais compósitos são tipicamente anisotrópicos, o que aumenta substancialmente a complexidade da análise mecânica desses materiais, em especial quando expostos a danos causados por impactos. Isso leva a cenários perigosos, porque esses defeitos são tipicamente difíceis de serem visualizados, muitas vezes sendo invisíveis do lado da peça que sofreu o impacto. Ensaios não destrutivos (EnDs) são métodos de inspeção de integridade de materiais capazes de detectar essas anormalidades. Entretanto, não há um EnD capaz de detectar de maneira confiável todos os tipos de anormalidades sob todas as condições e em todos os tipos de materiais. No caso particular da inspeção de compósitos para detecção de danos de impacto, o ultrassom é a tecnologia mais completa e é a referência em muitos setores como o aeronáutico. Contudo, ela pode apresentar problemas relacionados à rugosidade da superfície do corpo inspecionado, às suas propriedades mecânicas e à exigência do contato com o corpo. Shearografia e termografia são EnDs baratos, sem contato e amplamente utilizadas na inspeção de compósitos, no entanto têm diferentes sensibilidades aos defeitos causados por impactos em PRFCs, podendo levar a erros de interpretação. É interessante, portanto, combinar essas duas técnicas para alcançar resultados confiáveis com menor custo e prover uma alternativa em potencial às indústrias que dependem de ultrassom para seus ensaios. A fusão de dados fornece saídas promissoras para esses problemas através da combinação de dados provenientes de duas ou mais fontes de uma forma sinergética. Neste trabalho, é proposta a combinação entre termografia ativa modulada e shearografia em pulsos quadrados para inspecionar danos de impacto de diferentes magnitudes, desde defeitos quase invisíveis até estágios em que há perfuração das amostras. Múltiplas combinações de técnicas de processamento de imagens foram desenvolvidas e testadas para se avaliar quais as melhores opções para fundir e segmentar danos de impacto em imagens provenientes desses dois métodos de EnD, usando o ultrassom como referência. Os resultados indicam que a fusão proposta entre esses métodos é efetiva para se alcançar resultados próximos aos fornecidos pelo ultrassom, podendo ser considerada, portanto, a fusão de dados proposta uma potencial alternativa de inspeção em substituição à técnica de referência, levando assim a uma flexibilização nos setores que dependem fortemente dela.
Palavras-chave: termografia, shearografia, fusão de imagens, plásticos reforçados com fibra
RESUMO ESTENDIDO Introdução
A preservação do meio ambiente tem estado mais em evidência ultimamente devido a alarmantes resultados de estudos sobre os efeitos de emissões de gases na atmosfera. A redução de peso é uma forma de lidar com esse problema e melhorar o desempenho de máquinas que emitem esses gases. Uma alternativa interessante nesse sentido é a substituição de ligas metálicas por materiais compósitos, como os plásticos reforçados por fibras de carbono (PRFCs), devido à sua capacidade de ter um comportamento mecânico similar ao dos metais tendo apenas uma fração de seu peso. Entretanto, materiais compósitos são tipicamente anisotrópicos, o que aumenta substancialmente a complexidade da análise mecânica desses materiais, em especial quando expostos a danos causados por impactos. Isso leva a cenários perigosos, porque esses defeitos são tipicamente difíceis de serem visualizados, muitas vezes sendo invisíveis do lado da peça que sofreu o impacto. Ensaios não destrutivos (EnDs) são métodos de inspeção de integridade de materiais capazes de detectar essas anormalidades. Entretanto, não há um EnD capaz de detectar de maneira confiável todos os tipos de anormalidades sob todas as condições e em todos os tipos de materiais. No caso particular da inspeção de compósitos para detecção de danos de impacto, o ultrassom é a tecnologia mais completa e é a referência em muitos setores como o aeronáutico. Contudo, ela pode apresentar problemas relacionados à rugosidade da superfície do corpo inspecionado, às suas propriedades mecânicas e à exigência do contato com o corpo. Shearografia e termografia são EnDs baratos, sem contato e amplamente utilizadas na inspeção de compósitos, no entanto têm diferentes sensibilidades aos defeitos causados por impactos em PRFCs, podendo levar a erros de interpretação. É interessante, portanto, combinar essas duas técnicas para alcançar resultados confiáveis com menor custo e prover uma alternativa em potencial às indústrias que dependem de ultrassom para seus ensaios. A fusão de dados fornece saídas promissoras para esses problemas através da combinação de dados provenientes de duas ou mais fontes de uma forma sinergética. Neste trabalho, é proposta a combinação entre termografia ativa modulada (TAM) e shearografia em pulsos quadrados (SPQ) para inspecionar danos de impacto de diferentes magnitudes, desde defeitos quase invisíveis até estágios em que há perfuração das amostras. Múltiplas combinações de técnicas de processamento de imagens foram desenvolvidas e testadas para se avaliar quais as melhores opções para fundir e segmentar danos de impacto em imagens provenientes desses dois métodos de EnD, usando o ultrassom como referência. Os resultados indicam que a fusão proposta entre esses métodos é efetiva para se alcançar resultados próximos aos fornecidos pelo ultrassom, podendo ser considerada, portanto, a fusão de dados proposta uma potencial alternativa de inspeção em substituição à técnica de referência, levando assim a uma flexibilização nos setores que dependem fortemente dela.
Objetivos
O objetivo principal deste trabalho é desenvolver métodos para fundir imagens capturadas por um sistema de TAM e por um sistema de SPQ de forma a melhorar a capacidade de detecção de defeitos e a confiabilidade de EnD realizados em corpos de prova de PRFC que apresentam danos de impacto, permitindo se obter resultados próximos aos obtidos pela tecnologia de referência em EnD. Os objetivos específicos são: (a) estudar, caracterizar e fabricar corpos de prova de PRFC com danos de impacto controlados que estejam dentro da classificação de danos de impacto pouco visíveis; (b) conduzir uma caracterização metrológica sólida com essas amostras utilizando como tecnologia de referência o ultrassom, permitindo obter padrões nominais dos defeitos; (c) realizar e otimizar medições com SPQ de maneira a inspecionar apropriadamente defeitos de impacto em amostras de PRFC; (d) realizar e otimizar medições
com TAM de maneira a inspecionar apropriadamente defeitos de impacto em amostras de PRFC; (e) desenvolver ferramentas de fusão de imagens que sejam capazes de gerar imagens fusionadas com o máximo de informação útil presentes nas imagens de termografia e shearografia; e (f) avaliar as ferramentas desenvolvidas com métricas específicas em relação às imagens de referência obtidas com ultrassom.
Metodologia
Corpos de prova de epóxi reforçados com fibras de carbono de 60 mm x 60 mm x 2 mm foram danificados com impactos controlados e inspeções com sistemas de TAM e de SPQ foram realizadas. Um número de combinações igual a 1530 contendo ferramentas de extração de componentes, métodos de alinhamento espacial, pré-processamento, segmentação e métodos de fusão foi proposto. Entre essas ferramentas pode se destacar o uso de análise de componentes principais, segmentação global de Otsu, segmentação adaptativa de Bradley, decomposição bidimensional por ondeletas, filtragem no domínio de Fourier, operadores morfológicos, operadores booleanos, filtros passa-baixas convencionais, intensificação de contraste, ferramentas de remoção de salto de fase, filtros iterativos, entre outros. Essas combinações foram avaliadas utilizando como critérios o diâmetro equivalente, a acurácia binária, o coeficiente capa de Cohen e o coeficiente de correlação de Matthews, utilizando como referência segmentações manuais de cada uma das técnicas e também as imagens obtidas com ultrassom.
Resultados e discussão
A melhor combinação de ferramentas para segmentação de defeitos em imagens de termografia contém a utilização de um filtro passa-baixas no domínio de Fourier seguido da segmentação adaptativa de Bradley. Para imagens de shearografia, a melhor combinação contém um filtro passa-baixas baseado da decomposição bidimensional com transformada por ondeletas. Para o método de fusão binária, a melhor segmentação passa pela ferramenta de alinhamento espacial por decomposição por transformada por ondeletas. As imagens do segundo método de fusão denominado fusão em nível de cinza são melhor segmentadas utilizando interpolação linear, a regra de fusão por mínimos, aferramenta de filtragem por decomposição por transformada por ondeletas e a segmentação por multilimiarização. Por fim, para a fusão baseada em análise de componentes principais, segmenta-se melhor com a combinação da ferramenta de filtragem por decomposição por transformada por ondeletas e da segmentação por multilimiarização. Foi demonstrado ainda que o método de fusão binária é o melhor dos métodos de fusão propostos e que ele cumpre com os objetivos propostos no trabalho, sendo melhor do que as segmentações das imagens de termografia e shearografia individualmente e seus resultados sendo próximos dos fornecidos pela técnica de ultrassom.
Considerações finais
Neste trabalho, foram propostas diversas combinações de ferramentas para processamento e fusão de imagens de shearografia e termografia para inspeção de danos de impacto em corpos de prova de PRFC. Foi demonstrado um método de fusão que é melhor dos que TAM e SPQ individualmente na tarefa de caracterizar danos de impacto. Ainda, se mostrou que: (a) as capacidades de detecção de defeitos de TAM e SPQ em relação a danos de impacto são similares, mas que a SPQ funciona melhor para detecção de danos de pequena magnitude; (b) há uma maior dificuldade para segmentação de defeitos de impacto utilizando SPQ; (c)
ferramentas de extração de componentes, pré-processamento e segmentação são passos importantes para as imagens de TAM; e (d) para SPQ são passos importantes para obtenção de bons resultados a correta escolha de algoritmos de pré-processamento e de segmentação. Como inovação proposta se destaca o desenvolvimento de métodos para fundir imagens de TAM e SPQ comparáveis à técnica de referência, o ultrassom. Como outras contribuições, destaca-se: (a) o desenvolvimento de ferramentas específicas para segmentação de defeitos em imagens de TAM e SPQ; (b) a comparação de TAM e SPQ na tarefa de identificar danos de impacto; e (c) a análise de importância de procedimentos de processamento de imagens com TAM e SPQ.
Palavras-chave: termografia, shearografia, fusão de imagens, plásticos reforçados com fibra
ABSTRACT
The preservation of the environment has been more in evidence lately, due to alarming study results about the effects of gas emissions in the atmosphere. The weight reduction is a way to enhance the performance of gas-emitting machines. An interesting alternative to reduce weight is the replacement of metallic alloys for composite materials, such as the carbon fibre reinforced plastics (CFRPs), due to their capability of such materials to provide a mechanical behaviour comparable to the one of metals but with a fraction of their weights. However, composite materials are typically anisotropic, which enhances substantially the mechanical analysis complexity of damages such as the ones caused by impacts. This leads to many dangerous scenarios since the defects caused by impacts are difficult to visually detect and many are invisible on the impacted side. Non-destructive testing (NDT) methods are integrity inspections capable of detecting such abnormalities. But there is no NDT method capable of reliably detect all abnormalities under all conditions in all materials. In the case of inspecting composites in order to look for damages caused by impacts, ultrasound is the most complete technology, being the reference for several sectors like aeronautics. However, ultrasound may present drawbacks regarding the surface texture of the inspected part, its mechanical properties and the possible need of physical contact. Shearography and thermography are contactless NDT methods that are widely used for composite inspection and whose inspection may be cheaper than the ultrasound one, but they have different sensitivities regarding the effect of impacts on CFRP parts, which can lead to misinterpretations of their results. Combining such NDT methods to achieve reliable, cheaper results can be consequently interesting. Data fusion tools provide promising outcomes when it comes to combining data from two or more sources in a synergistical way. This work proposes the combined use of active lock-in thermography and square pulse shearography to inspect impact damages stemming from different magnitudes in CFRP plates, from barely visible defects up to perforation stages. Several combinations of different image processing tools were developed and tested to evaluate which is the best option for fusing and segmenting impact damages in images coming from these two NDT techniques, using results from ultrasound as reference. The results indicate that the fusion of the proposed NDT methods is technically feasible and is capable of achieving inspection results close to those obtained by ultrasound. Therefore, the proposal represents a viable alternative for industries that relies on ultrasound as the main NDT technique.
Keywords: thermography, shearography, image fusion, lock-in loading, carbon fibre reinforced
LIST OF FIGURES
Figure 2.1 – Most common reinforcement formats for polymeric composites. ... 21
Figure 2.2 – Most common reinforcement formats for polymeric composites. ... 22
Figure 2.3 – Types of defect caused by low-velocity impacts in composite laminates. ... 26
Figure 2.4 – Reverse pine-tree pattern generated by impacts in thin laminates. ... 26
Figure 2.5 – Residual strength after impact in relation to the permanent indentation. In A, the part is yet good. In B, the part must the replaced but still is duty. In C, the part does not stand the service load anymore. ... 27
Figure 2.6 – Classification of damages in relation to its visual detectability. ... 28
Figure 3.1 – A generic ultrasound device. ... 32
Figure 3.2 – A-scan, B-scan and C-scan. ... 33
Figure 3.3 – Defect effect in an object inspected by shearography. The derivative slope caused by the defect presence revealed by the loading procedure, generating the interference fringes. ... 35
Figure 3.4 – An example of the speckle effect on an image of a surface illuminated by laser light and captured by a monochromatic camera. ... 36
Figure 3.5 – A configuration of a shearography system based on the Michelson interferometer. ... 36
Figure 3.6 – A five-step procedure to calculate optical phase maps and to generate the optical phase difference map to reveal defects with shearography. ... 38
Figure 3.7 – Phase map acquisition with triple-shear formation using three wedge plates. .... 39
Figure 3.8 – Spectrum of electromagnetic radiation. ... 40
Figure 3.9 – A diagram showing the lock-in thermography procedure. ... 41
Figure 4.1 – Loading procedure variables. ... 47
Figure 4.2 – Square pulse and lock-in loading methods from the point of view of the temperature of the body. ... 48
Figure 4.3 – A sonotrode in contact with a sample in an application of vibrothermography. . 51
Figure 4.4 – The effect of the temperature during the heating of a sample with defects whose densities are different from the one of the non-defective part of the body, and in different depths. ... 53
Figure 4.5 – Configuration of an NDT with induction heating procedure. ... 54
Figure 4.6 – High-powered flash lamp for heat loading... 55
Figure 4.7 – Halogen lamp for heat loading. ... 55
Figure 4.8 – Two different energy transfer modes for using the radiation heating method. .... 56
Figure 5.1 – A generic image fusion is shown, in which information of both input images are present in the output image. ... 60
Figure 5.2 – Image fusion in pixel-level, feature-level and decision-level. ... 61
Figure 5.3 – Registration steps before image fusion in pixel-level, feature-level and decision-level. ... 62
Figure 5.4 – Generic diagram of a multi-scale decomposition tool used to fuse images. ... 66
Figure 5.5 – A flowchart of the IHS method. ... 68
Figure 5.6 – Detecting damage in CFRP: (a) pulse thermography, (b) ultrasound thermography and (c) the fused data. ... 70
Figure 5.7 – Comparing the tool developed in (PARAMANANDHAM; RAJENDIRAN, 2018) with other fusion techniques. ... 71
Figure 5.8 – A fusion performed with sparse representation shown in (ZHANG et al., 2018). In (a), the infrared image. In (b), the visible-light image. In (c), the fused image. ... 72
Figure 5.9 – Detecting blind holes with lock-in thermography at 0.0125 Hz and 0.0250 Hz in a scatter plot, with the revelation of three defects in the chart denoted by the numbered circles. ... 72 Figure 5.10 – People detection using fused images. In (a), the visible-light image; in (b), the infrared image; and in (c) the pedestrian detection in a portion of the fused image. ... 73 Figure 5.11 – Thermography using different excitation sources. On the left, the thermogram with optical excitation, on the right, the thermogram of the ultrasonic excitation and in the centre the fusion of these images. ... 73 Figure 5.12 – The image fusion method proposed in (BAVIRISETTI; DHULI, 2016). ... 74 Figure 5.13 – Acquisition system proposed in (BULANON; BURKS; ALCHANATIS, 2009). ... 75 Figure 5.14 – The image fusion method proposed in (BULANON; BURKS; ALCHANATIS, 2009). ... 75 Figure 5.15 – Temperature measurement, surface measurement and the fusion result shown in (GEORGES et al., 2014). ... 76 Figure 6.1 – The test bench used for inspecting the samples with OLT. In 1, the halogen lamp for the optical loading. In 2, the sample. In 3, the loading system control. In 4, the infrared camera. ... 78 Figure 6.2 – The test bench used for inspecting the samples with OSS. In 1, the halogen lamp for the optical loading. In 2, the sample covered with white coating to allow an interferometric measurement. In 3, a plate to keep the sample in right position. In 4, the one-shot
shearography system. ... 79 Figure 6.3 – A plate used as sample in this work. ... 80 Figure 6.4 – The drop-weight impact test machine used in this work. In (1), the striker. In (2), the clamping device. In (3), the damping system. In (4), the guidance rails. ... 81 Figure 6.5 – The chessboard pattern in the printed circuit board for calibrating the infrared camera. ... 84 Figure 6.6 – Flowchart of the procedures proposed in this work. ... 85 Figure 6.7 – An example of spatial image registration. The background is removed and the origin of the coordinate system moves to the edge of the plate. ... 87 Figure 6.8 – An example of the MPSC. ... 88 Figure 6.9 – Plane subtraction example. In (a) the original wrapped image. In (b) the
unwrapped image. In (c) the unwrapped image after the plane subtraction. ... 90 Figure 6.10 – Generation of matrix A by reducing the dimensionality of the images. ... 91 Figure 6.11 – Flowchart of 2DFFT. ... 92 Figure 6.12 – An 0.020 Hz OLT thermal phase image of a sample with a 10 J impact damage before and after the application of 2DFFT. ... 93 Figure 6.13 – An 0.020 Hz OLT thermal phase image of a sample with a 10 J impact damage before and after the use of ATC with a 3 x 3 defect-free neighbourhood. ... 94 Figure 6.14 – An 0.020 Hz OLT thermal phase image of a sample with a 10 J impact damage before and after the use of CE. ... 95 Figure 6.15 – Flowchart of WDLP. ... 95 Figure 6.16 – An 0.020 Hz OLT thermal phase image of a sample with a 10 J impact damage and its 3rd-level decomposition with WDLP. ... 96 Figure 6.17 – An OSS unwrapped optical phase image of a sample with a 10 J impact damage before and after the application of GSMO. ... 97 Figure 6.18 – An OSS unwrapped optical phase image of a sample with a 10 J impact damage before and after the application of MT. ... 98 Figure 6.19 – An OSS unwrapped optical phase image of a sample with a 10 J impact damage before and after the application of EXMMT. ... 99
Figure 6.20 – An OSS unwrapped optical phase image of a sample with a 10 J impact damage
before and after the application of FMM... 100
Figure 6.21 – An 0.030 Hz OLT thermal phase image of a sample with an 8 J impact damage before and after the application of OTSU. ... 100
Figure 6.22 – A 0.020 Hz OLT thermal phase image of a sample with a 10 J impact damage before and after the application of BRAD. ... 101
Figure 6.23 – Binary fusion method applied to the OLT thermal phase images and the OSS temporal images of a sample with a 10 J impact damage. In (a), the segmented image resulting from the segmentation of OSS images. In (b), the segmented image resulting from the segmentation of OLT images. In (c), the fused image. ... 103
Figure 6.24 – Flowchart of the grey level fusion procedures: in (a), the procedure considering just a decomposition for multi-modal registration; and in (b), the procedure with more decomposition steps, which was used only with the WD decomposition method. ... 104
Figure 6.25 – An OSS optical phase image of a sample with a 10 J impact damage before and after the application of MC. ... 104
Figure 6.26 – Grey level fusion method for PYR and LI decomposition procedures of a sample with a 10 J impact before any preprocessing or segmentation. In (a), the 0.020 Hz OLT thermal phase image. In (b), a temporal OSS image. In (c) – (f), the fused images with PYR decomposition method and maximum, mean, minimum and sum fusion rules. In (g) – (j), the fused images with LI decomposition method and maximum, mean, minimum and sum fusion rules. ... 106
Figure 6.27 – Twenty-two results of the grey level fusion method for WD decomposition procedures of a sample with a 10 J impact before any preprocessing or segmentation. ... 108
Figure 6.28 – PCA fusion method applied to the OLT thermal phase images and the OSS temporal images of a sample with a 10 J impact damage. In (a), one OSS image. In (b), the 0.020 Hz OLT image. In (c) – (g), the 1st-5th fused images... 109
Figure 6.29 – Flowchart of the procedure to generate the ultrasound reference images. ... 110
Figure 7.1 – Comparison between OLT images with 1 J and 3 J impact damages and OSS images with 1 J and 3 J impact damages. In (a)-(d), OLT images with 1 J impact damages. In (e)-(h), OLT images with 3 J impact damages. In (i)-(l), OSS images with 1 J impact damages. In (m)-(p), OSS images with 3 J impact damages ... 116
Figure 7.2 – Comparison between OLT images similar areas but different impact energies. In (a), an OLT image with a 7 J damage. In (a), an OLT image with a 5 J damage. In (a), an OLT image with a 4 J damage. In (a), other OLT image with a 7 J damage. ... 117
Figure 7.3 – Summary of the results of OLT-NCE. ... 119
Figure 7.4 – Summary of the results of OLT-PCA. ... 120
Figure 7.5 – Summary of the results of OSS-NCE... 124
Figure 7.6 – Summary of the results of OSS-PCA. ... 125
Figure 7.7 – Summary of the results of binary fusion. ... 128
Figure 7.8 – Summary of the results of GLF. ... 131
Figure 7.9 – Summary of the results of GLF-LI... 132
Figure 7.10 – Summary of the results of GLF-PYR. ... 133
Figure 7.11 – Summary of the results of GLF-WD. ... 134
Figure 7.12 – Summary of the results of PCA fusion method. ... 137
Figure 7.13 – Comparison between the best methods using as reference the manually-segmented images. ... 140
Figure 7.14 – Comparison between the best methods using as reference the binary ultrasound images. ... 141
Figure 7.15 – Error in the equivalent diameter measurement using the reference technology as nominal value. ... 142
Figure 7.16 – Equivalent diameter of the lowest impact energies. ... 142 Figure A.1 – A model of the plate with constant surface temperature. ... 157 Figure A.2 – Disposition of the RTDs over the inspected plate in contact with a metallic plate with constant temperature. ... 158 Figure A.3 – Temperature vs. Time graph of three PT-100. ... 159 Figure B.1 – OLT-NCE thermal phase samples at 0.020 Hz of 1 J (columns 1 to 3) and 3 J (columns 4 to 6) and their segmentation results to ACC, KAPP and MATT. ... 161 Figure B.2 – OLT-NCE thermal phase samples at 0.020 Hz of 4 J (columns 1 to 3) and 5 J (columns 4 to 6) and their segmentation results to ACC, KAPP and MATT. ... 162 Figure B.3 – OLT-NCE thermal phase samples at 0.020 Hz of 6 J (columns 1 to 3) and 7 J (columns 4 to 6) and their segmentation results to ACC, KAPP and MATT. ... 163 Figure B.4 – OLT-NCE thermal phase samples at 0.020 Hz of 8 J (columns 1 to 3) and 9 J (columns 4 to 6) and their segmentation results to ACC, KAPP and MATT. ... 164 Figure B.5 – OLT-NCE thermal phase samples at 0.020 Hz of 10 J (columns 1 to 3) and 12 J (columns 4 to 6) and their segmentation results to ACC, KAPP and MATT. ... 165 Figure C.1 – OLT-PCA first component samples at 0.020 Hz of 1 J and 3 J and their
segmentation results to ACC, KAPP and MATT. ... 169 Figure C.2 – OLT-PCA first component samples at 0.020 Hz of 4 J and 5 J and their
segmentation results to ACC, KAPP and MATT. ... 170 Figure C.3 – OLT-PCA first component samples at 0.020 Hz of 6 J and 7 J and their
segmentation results to ACC, KAPP and MATT. ... 171 Figure C.4 – OLT-PCA first component samples at 0.020 Hz of 8 J and 9 J and their
segmentation results to ACC, KAPP and MATT. ... 172 Figure C.5 – OLT-PCA first component samples at 0.020 Hz of 10 J and 12 J and their segmentation results to ACC, KAPP and MATT. ... 173 Figure D.1 – OSS-NCE samples of 1 J (columns 1 to 3) and 3 J (columns 4 to 6) and their segmentation results to ACC, KAPP and MATT. ... 177 Figure D.2 – OSS-NCE samples of 4 J (columns 1 to 3) and 5 J (columns 4 to 6) and their segmentation results to ACC, KAPP and MATT. ... 178 Figure D.3 – OSS-NCE samples of 6 J (columns 1 to 3) and 7 J (columns 4 to 6) and their segmentation results to ACC, KAPP and MATT. ... 179 Figure D.4 – OSS-NCE samples of 8 J (columns 1 to 3) and 9 J (columns 4 to 6) and their segmentation results to ACC, KAPP and MATT. ... 180 Figure D.5 – OSS-NCE samples of 10 J (columns 1 to 3) and 12 J (columns 4 to 6) and their segmentation results to ACC, KAPP and MATT. ... 181 Figure E.1 – OSS-PCA samples of 1 J (columns 1 to 3) and 3 J (columns 4 to 6) and their segmentation results to ACC, KAPP and MATT. ... 185 Figure E.2 – OSS-PCA samples of 4 J (columns 1 to 3) and 5 J (columns 4 to 6) and their segmentation results to ACC, KAPP and MATT. ... 186 Figure E.3 – OSS-PCA samples of 6 J (columns 1 to 3) and 7 J (columns 4 to 6) and their segmentation results to ACC, KAPP and MATT. ... 187 Figure E.4 – OSS-PCA samples of 8 J (columns 1 to 3) and 9 J (columns 4 to 6) and their segmentation results to ACC, KAPP and MATT. ... 188 Figure E.5 – OSS-PCA samples of 10 J (columns 1 to 3) and 12 J (columns 4 to 6) and their segmentation results to ACC, KAPP and MATT. ... 189 Figure F.1 – Samples of binary fusion with LI and NCE of 1 J (columns 1 to 3) and 3 J (columns 4 to 6) according to ACC, KAPP and MATT. ... 193 Figure F.2 – Samples of binary fusion with LI and NCE of 4 J (columns 1 to 3) and 5 J (columns 4 to 6) according to ACC, KAPP and MATT. ... 194
Figure F.3 – Samples of binary fusion with LI and NCE of 6 J (columns 1 to 3) and 7 J (columns 4 to 6) according to ACC, KAPP and MATT. ... 195 Figure F.4 – Samples of binary fusion with LI and NCE of 8 J (columns 1 to 3) and 9 J (columns 4 to 6) according to ACC, KAPP and MATT. ... 196 Figure F.5 – Samples of binary fusion with LI and NCE of 10 J (columns 1 to 3) and 12 J (columns 4 to 6) according to ACC, KAPP and MATT. ... 197 Figure G.1 – Samples of binary fusion with LI and PCA of 1 J (columns 1 to 3) and 3 J (columns 4 to 6) according to ACC, KAPP and MATT. ... 199 Figure G.2 – Samples of binary fusion with LI and PCA of 4 J (columns 1 to 3) and 5 J (columns 4 to 6) according to ACC, KAPP and MATT. ... 200 Figure G.3 – Samples of binary fusion with LI and PCA of 6 J (columns 1 to 3) and 7 J (columns 4 to 6) according to ACC, KAPP and MATT. ... 201 Figure G.4 – Samples of binary fusion with LI and PCA of 8 J (columns 1 to 3) and 9 J (columns 4 to 6) according to ACC, KAPP and MATT. ... 202 Figure G.5 – Samples of binary fusion with LI and PCA of 10 J (columns 1 to 3) and 12 J (columns 4 to 6) according to ACC, KAPP and MATT. ... 203 Figure H.1 – Samples of grey level fusion with LI-MIN of 1 J (columns 1 to 3) and 3 J
(columns 4 to 6). ... 205 Figure H.2 – Samples of grey level fusion with LI-MIN of 4 J (columns 1 to 3) and 5 J
(columns 4 to 6). ... 206 Figure H.3 – Samples of grey level fusion with LI-MIN of 6 J (columns 1 to 3) and 7 J
(columns 4 to 6). ... 207 Figure H.4 – Samples of grey level fusion with LI-MIN of 8 J (columns 1 to 3) and 9 J
(columns 4 to 6). ... 208 Figure H.5 – Samples of grey level fusion with LI-MIN of 10 J (columns 1 to 3) and 12 J (columns 4 to 6). ... 209 Figure I.1 – Samples of grey level fusion with PYR-MIN of 1 J (columns 1 to 3) and 3 J (columns 4 to 6). ... 213 Figure I.2 – Samples of grey level fusion with PYR-MIN of 4 J (columns 1 to 3) and 5 J (columns 4 to 6). ... 214 Figure I.3 – Samples of grey level fusion with PYR-MIN of 6 J (columns 1 to 3) and 7 J (columns 4 to 6). ... 215 Figure I.4 – Samples of grey level fusion with PYR-MIN of 8 J (columns 1 to 3) and 9 J (columns 4 to 6). ... 216 Figure I.5 – Samples of grey level fusion with PYR-MIN of 10 J (columns 1 to 3) and 12 J (columns 4 to 6). ... 217 Figure J.1 – Samples of grey level fusion with WD-MIN of 1 J (columns 1 to 3) and 3 J (columns 4 to 6). ... 221 Figure J.2 – Samples of grey level fusion with WD-MIN of 4 J (columns 1 to 3) and 5 J (columns 4 to 6). ... 222 Figure J.3 – Samples of grey level fusion with WD-MIN of 6 J (columns 1 to 3) and 7 J (columns 4 to 6). ... 223 Figure J.4 – Samples of grey level fusion with WD-MIN of 8 J (columns 1 to 3) and 9 J (columns 4 to 6). ... 224 Figure J.5 – Samples of grey level fusion with WD-MIN of 10 J (columns 1 to 3) and 12 J (columns 4 to 6). ... 225 Figure K.1 – Samples of PCA fusion of 1 J and 3 J according to ACC, KAPP AND MATT. ... 229 Figure K.2 – Samples of PCA fusion of 4 J and 5 J according to ACC, KAPP AND MATT. ... 230
Figure K.3 – Samples of PCA fusion of 6 J and 7 J according to ACC, KAPP AND MATT. ... 231 Figure K.4 – Samples of PCA fusion of 8 J and 9 J according to ACC, KAPP AND MATT. ... 232 Figure K.5 – Samples of PCA fusion of 10 J and 12 J according to ACC, KAPP AND MATT. ... 233
LIST OF TABLES
Table 2.1 – Comparison between mechanical properties of common metallic alloys and
composites. ... 21
Table 2.2 – Ultimate tensile strength and impact resistance of some composite materials depending of the matrix and fibre materials. ... 23
Table 2.3 – Maximum services temperatures of some polymers commonly used in composite materials. ... 24
Table 3.1 – Summary of NDT categories and their objectives. ... 30
Table 3.2 – Comparison between the presented NDT methods. ... 44
Table 3.3 – Defect detectability in relation to the NDT methods discussed in this work. ... 45
Table 4.1 – Comparison of the heating techniques presented in this chapter. ... 57
Table 5.1 – Fusion procedures according to which inputs and outputs are provided... 59
Table 5.2 – Examples of image fusion applications. ... 69
Table 6.1 – Infrared vision system specifications. ... 78
Table 6.2 – Visible-light system specifications. ... 79
Table 6.3 – OLT measurement parameters. ... 82
Table 6.4 – OSS measurement parameters. ... 82
Table 7.1 – Drop-weight impact test heights and resulting impact energies. ... 115
Table 7.2 – Distribution of results in the Appendices. ... 117
Table 7.3 – AKW of the metrics applied to OLT images... 121
Table 7.4 – Multiple comparison test of the metrics applied to OLT images for the factor ‘Preprocessing’. ... 122
Table 7.5 – Multiple comparison test of the metrics applied to OLT images for the factor ‘Segmentation’. ... 122
Table 7.6 – AKW of the metrics applied to OSS images. ... 126
Table 7.7 – Multiple comparison test of the metrics applied to OSS images for the factor ‘Preprocessing’. ... 127
Table 7.8 – Multiple comparison test of the metrics applied to OSS images for the factor ‘Segmentation’. ... 127
Table 7.9 – AKW of the metrics applied to binary-fused images. ... 129
Table 7.10 – Multiple comparison test of the metrics applied to binary-fused images for the factor ‘Registration’. ... 130
Table 7.11 – AKW of the metrics applied to the grey level fused images. ... 135
Table 7.12 – Multiple comparison test of the metrics applied to grey level fused images for the factor ‘Preprocessing’. ... 136
Table 7.13 – Multiple comparison test of the metrics applied to grey level fused images for the factor ‘Segmentation’. ... 136
Table 7.14 – AKW of the metrics applied to PCA-fused images... 138
Table 7.15 – Multiple comparison test of the metrics applied to PCA-fused images for the factor ‘Preprocessing’. ... 138
Table 7.16 – Multiple comparison test of the metrics applied to PCA-fused images for the factor ‘Segmentation’. ... 139
Table A.1 – Thermal diffusivity measurement. ... 160
Table B.1 – OLT-NCE measurement results: ACC. ... 166
Table B.2 – OLT-NCE measurement results: KAPP. ... 167
Table B.3 – OLT-NCE measurement results: MATT. ... 168
Table C.1 – OLT-PCA measurement results: ACC. ... 174
Table C.2 – OLT-PCA measurement results: KAPP. ... 175
Table D.1 – OSS-NCE measurement results: ACC. ... 182
Table D.2 – OSS-NCE measurement results: KAPP. ... 183
Table D.3 – OSS-NCE measurement results: MATT. ... 184
Table E.1 – OSS-PCA measurement results: ACC... 190
Table E.2 – OSS-PCA measurement results: KAPP... 191
Table E.3 – OSS-PCA measurement results: MATT. ... 192
Table F.1 – Binary fusion measurement results with NCE images: ACC. ... 198
Table F.2 – Binary fusion measurement results with NCE images: KAPP. ... 198
Table F.3 – Binary fusion measurement results with NCE images: MATT. ... 198
Table G.1 – Binary fusion measurement results with PCA images: ACC. ... 204
Table G.2 – Binary fusion measurement results with PCA images: KAPP. ... 204
Table G.3 – Binary fusion measurement results with PCA images: MATT. ... 204
Table H.1 – Best grey level fusion measurement results: LI-ACC. ... 210
Table H.2 – Best grey level fusion measurement results: LI-KAPP. ... 211
Table H.3 – Best grey level fusion measurement results: LI-MATT. ... 212
Table I.1 – Best grey level fusion measurement results: PYR-ACC. ... 218
Table I.2 – Best grey level fusion measurement results: PYR-KAPP. ... 219
Table I.3 – Best grey level fusion measurement results: PYR-MATT. ... 220
Table J.1 – Best grey level fusion measurement results: WD-ACC. ... 226
Table J.2 – Best grey level fusion measurement results: WD-KAPP. ... 227
Table J.3 – Best grey level fusion measurement results: WD-MATT. ... 228
Table K.1 – PCA fusion measurement results: ACC. ... 234
Table K.2 – PCA fusion measurement results: KAPP. ... 235
NOMENCLATURE
2DFFT Two-dimensional Fast Fourier Transform method ACC Binary Accuracy metric
AKW Analysis of Kruskall-Wallis ANOVA Analysis of Variance
ASTM American Society of Testing and Materials ATC Absolut Thermal Contrast method
BF Binary Image Fusion method
BRAD Adaptive Segmentation with Bradley’s method
BT Brovey Transform
BVID Barely Visible Impact Damage CE Contrast Enhancement method CFRP Carbon Fibre Reinforced Plastics CN Colour Normalization
DCT Discrete Cosine Transform
DIN German Institute for Standardization, from the German name Deutsches
Institut für Normung
DWIT Drop-Weight Impact Testing
EXMMT Extended Maxima and Minima Transform method FFT Fast Fourier Transform
FMM Fast March Method
FN False Negative
FP False Positive
GLF Grey Level Image Fusion method
GSMO Global segmentation and morphological operator method HPF High Pass Filtering
IHS Intensity-Hue-Saturation method KAPP Cohen’s Kappa metric
KSNT Kolmogorov-Smirnov Normality Test
Laser Light Amplification by Stimulated Emission of Radiation LI Linear Interpolation method
LPT Laplacian Pyramid Transform LVID Large Visible Impact Damage
MATT Matthews Correlation Coefficient metric
MAX Maximum Fusion Rule
MC Max Complement method
MEAN Mean Fusion Rule MIN Minimum Fusion Rule MT Multi-Threshold method
NCE No Component Extraction method NDT Non-Destructive Testing
NPP No Preprocessing
OLT Optical Lock-in Thermography OSS Optical Square-pulse Shearography OTSU Global Segmentation with Otsu’s method PCA Principal Component Analysis
PCAF Principal Component Analysis Image Fusion method
PYR Pyramid method
RTD Resistance Temperature Detector SNR Signal-to-Noise Ratio
SUM Sum Fusion Rule
TN True Negative
TP True Positive
VID Visible Impact Damage WD Wavelet Decomposition
WDF Two-dimensional wavelet decomposition for fusion
WDLP Two-dimensional wavelet decomposition for low-pass filtering WDSA Two-dimensional wavelet decomposition for spatial analysis
CONTENTS
1 INTRODUCTION ... 15
1.1 CONTEXTUALIZATION ... 15 1.2 OBJECTIVES ... 17
1.2.1 Main objective ... 17 1.2.2 Specific objectives and the question to be answered ... 17
1.3 TEXT STRUCTURE ... 18
2 COMPOSITE MATERIALS ... 19
2.1 CONTEXTUALIZATION ... 19 2.2 DEFINITION AND CHARACTERISTICS ... 19 2.3 REINFORCEMENTS... 21 2.4 MATRICES ... 23 2.5 IMPACT DAMAGES IN COMPOSITE MATERIALS ... 24
2.5.1 Impact loadings ... 24 2.5.2 Structural failures caused by low-velocity impacts ... 25 2.5.3 Damage classification ... 27 2.5.4 Impact testing ... 29
3 NON-DESTRUCTIVE TESTING OF COMPOSITE MATERIALS ... 30
3.1 ULTRASOUND ... 31
3.1.1 Definition and characteristics ... 31 3.1.2 Operation ... 31 3.1.3 Applications ... 33
3.2 SHEAROGRAPHY ... 34
3.2.1 Definition and characteristics ... 34 3.2.2 Operation ... 35 3.2.3 Applications ... 38
3.3 THERMOGRAPHY ... 39
3.3.1 Definition and characteristics ... 39 3.3.2 Operation ... 40 3.3.3 Applications ... 42
3.4 COMPARISON BETWEEN THE PRESENTED NDT METHODS ... 43
4 LOADING TECHNIQUES FOR DEFECT REVEALING IN ACTIVE
NON-DESTRUCTIVE TESTS ... 46
4.1 GENERAL CHARACTERISTICS OF LOADING TECHNIQUES ... 46 4.2 TEMPERATURE PROFILE DURING THE HEATING PROCEDURE ... 48 4.3 ULTRASOUND HEATING ... 50 4.4 INDUCTION HEATING ... 52 4.5 RADIATION HEATING ... 54 4.6 COMPARISON BETWEEN HEATING METHODS ... 57
5 IMAGE FUSION ... 58
5.1 FUNDAMENTALS AND DEFINITIONS OF DATA FUSION ... 58 5.2 IMAGE FUSION ... 60 5.3 IMAGE FUSION TECHNIQUES ... 63
5.3.1 Algebraic operators ... 64 5.3.2 Boolean operators ... 64 5.3.3 Brovey transform ... 65 5.3.4 Colour normalization ... 65 5.3.5 Multi-scale decomposition tools... 65 5.3.6 High-pass filtering ... 67
5.3.7 Intensity-hue-saturation method ... 67 5.3.8 PCA ... 67 5.3.9 Ehlers algorithm ... 68
5.4 APPLICATIONS OF IMAGE FUSION USING IMAGES FROM INFRARED AND VISIBLE-LIGHT SENSORS ... 69 5.4.1 Application A ... 69 5.4.2 Application B ... 70 5.4.3 Application C ... 70 5.4.4 Application D ... 70 5.4.5 Application E ... 72 5.4.6 Application F ... 72 5.4.7 Application G ... 73 5.4.8 Application H ... 74 5.4.9 Application I ... 74 5.4.10 A brief discussion about these applications ... 76 6 PROPOSED APPROACH ... 77
6.1 EQUIPMENT SELECTION ... 77
6.1.1 OLT system ... 77 6.1.2 OSS system ... 78
6.2 SAMPLES ... 80 6.3 PROCEDURE TO PRODUCE THE IMPACT DAMAGES ... 80 6.4 IMAGE ACQUISITION PARAMETERS ... 82 6.5 CAMERA CALIBRATION IN THE OSS AND OLT SYSTEMS ... 83 6.6 FUSION FRAMEWORKS ... 83 6.7 SPATIAL IMAGE REGISTRATION ... 86 6.8 GENERATION OF FILTERED OSS OPTICAL PHASE DIFFERENCE MAPS . 87
6.8.1 Filtering ... 87 6.8.2 Phase unwrapping ... 89 6.8.3 Secondary fringe attenuation ... 89
6.9 IMAGE COMPONENT EXTRACTION ... 90 6.10 IMAGE PREPROCESSING TOOL ... 92
6.10.1 2DFFT (two-dimensional Fast Fourier Transform method) ... 92 6.10.2 ATC (absolute thermal contrast) ... 93 6.10.3 CE (contrast enhancement) ... 94 6.10.4 WDLP (two dimensional wavelet decomposition for low-pass filtering) ... 94
6.11 IMAGE SEGMENTATION TOOLS ... 96
6.11.1 GSMO (global segmentation with morphological operators) ... 96 6.11.2 MT (multi-threshold) ... 97 6.11.3 EXMMT (extended maxima and minima transforms) ... 98 6.11.4 FMM (fast march method) ... 99 6.11.5 OTSU (global segmentation with Otsu’s method) ... 99 6.11.6 BRAD (adaptive segmentation with Bradley’s method) ... 100
6.12 IMAGE FUSION TOOLS ... 101
6.12.1 Multi-modal image registration ... 101 6.12.2 BF (binary fusion) ... 102 6.12.3 Grey level fusion ... 102 6.12.4 PCA fusion ... 109
6.13 ULTRASOUND DATA PROCESSING FOR THE COMPARISON WITH THE BEST OLT, OSS AND DATA FUSION TECHNIQUES ... 109 6.14 METRICS FOR ALGORITHM AND NDT METHOD EVALUATIONS ... 110
6.14.1 ACC ... 112 6.14.2 MATT ... 112 6.14.3 KAPP ... 113
6.15 EQUIVALENT DIAMETER EVALUATION ... 113
7 RESULTS AND DISCUSSIONS ... 114
7.1 THERMAL DIFFUSIVITY ΑZ OF THE CFRP SAMPLES AND
DETERMINATION OF THE EXCITATION FREQUENCIES FOR THE OLT MEASUREMENTS ... 114 7.2 IMPACT DAMAGE MEASUREMENTS ... 114 7.3 INPUT AND OUTPUT IMAGES ... 115 7.4 DEFECT CHARACTERIZATION WITH OLT IMAGES ... 116 7.5 DEFECT CHARACTERIZATION WITH OSS IMAGES ... 123 7.6 IMAGE FUSION METHODS ... 126
7.6.1 Binary fusion ... 127 7.6.2 Grey level fusion ... 130 7.6.3 PCA fusion ... 135
7.7 COMPARISON BETWEEN OLT, OSS AND THE PROPOSED FUSION METHODS CONSIDERING THEIR OWN REFERENCES ... 139 7.8 COMPARISON WITH THE REFERENCE TECHNOLOGY ... 140
8 CONCLUSIONS AND PERSPECTIVES ... 143
8.1 CONCLUSIONS ... 143 8.2 CONTRIBUTIONS ... 145 8.3 FUTURE WORKS ... 146 8.4 PUBLICATIONS ... 146
REFERENCES ... 148 APPENDIX A – DETERMINATION OF THE THERMAL DIFFUSIVITY IN THE DIRECTION Z ... 157 APPENDIX B – OLT-NCE RESULTS ... 161 APPENDIX C – OLT-PCA RESULTS ... 169 APPENDIX D – OSS-NCE RESULTS ... 177 APPENDIX E – OSS-PCA RESULTS ... 185 APPENDIX F – BINARY FUSION RESULTS FOR NCE ... 193 APPENDIX G – BINARY FUSION RESULTS FOR PCA ... 199 APPENDIX H – GREY LEVEL FUSION RESULTS WITH LI ... 205 APPENDIX I – GREY LEVEL FUSION RESULTS WITH PYR ... 213 APPENDIX J – GREY LEVEL FUSION SAMPLES WITH WD-MIN ... 221 APPENDIX K – PCA FUSION RESULTS ... 229
1 INTRODUCTION
1.1 CONTEXTUALIZATION
The preservation of the environment has been more in evidence lately, due to alarming study results about the effects of gas emissions in the atmosphere. Many actions have been taken to reduce these effects, and one of them is the pursuit for the improvement of energy efficiency of machines and industrial apparatuses which produces noxious sub-products. In many cases, for example the ones of the automotive and aerospace industries, the weight reduction is a way to enhance the performance of gas-emitting machines and an interesting alternative in this sense is the replacement of metallic alloys for composite materials, such as the carbon fibre reinforced plastic (CFRP) (CHENG; TIAN, 2012; JOLLY et al., 2015; YANG; HE, 2016a; YANG; HE; ZHANG, 2016; KORONIS; SILVA, 2019). An example of this is the start of mass production of a composite-body of BMW i3 series in 2013 (SILBERSCHMIDT, 2016).
Composites, materials with more than one phase based on the union of two materials to get together advantages of both, have been utilized for thousands of years. However, investments in their development have been improving remarkably in the last years due to this new weight-reduction paradigm. The reason for this interest is the capability of such materials to provide a mechanical behaviour comparable to the one of metals but with a fraction of their weights, yet avoiding problems such as corrosion (MARINUCCI, 2011; REZENDE; COSTA; BOTELHO, 2011).
On the other side, composite materials are typically anisotropic, which increases substantially the mechanical analysis complexity, leading to an uncertainty regarding the relation between maintenance intervals and repairability. In this sense, damages caused by impacts, which are very significant to such materials due to their higher probability of occurrence, are a topic of interest, because many composites have a low impact resistance, including the CFRPs. Impacts can produce defects such as matrix fractures, delaminations, debondings and fibre fractures, which may significantly reduce various mechanical strengths of a component. For the aerospace industry, a few scenarios like dropped tools, hail, bird strike or runway debris are examples of impact scenarios. However, the real issue at this point is that many dangerous damages caused by impacts are difficult to visually detect and many are invisible on the impacted side (MALLICK, 1997; KRÜGER; MOOK; MAGDEBURG, 2006; SILBERSCHMIDT, 2016).
Non-destructive testing (NDT) methods are integrity inspections made to detect discontinuities in the structure of materials without modifying their properties or geometrical characteristics, which may impair the return to service of the inspected component before the abnormalities bring the part to catastrophic failures. There are many different kinds of NDT techniques: visual inspection, radiography, thermography, shearography, speckle interferometry, computed tomography, ultrasound, eddy current, i.a. (ASHBEE, 1993; HUNG; HO, 2005; LIU et al., 2011; WORKMAN; MOORE, 2012; KARBHARI, 2013).
There is no NDT method capable of reliably detecting all abnormalities under all conditions in all materials. All techniques have their particularities, advantages, disadvantages and preferable applications. In the case of inspecting composites in order to look for damages caused by impacts, ultrasound is seen in fields like aerospace as the most complete technology, being considered as a reference. Although ultrasound is extensively used and known in many fields due to its performance, it may present drawbacks regarding the surface finish of the inspected part, its mechanical properties and the need of contacting it. Shearography and thermography are contactless, widely used for composite inspection, and can be cheaper but they have different sensitivities regarding the effect of impacts on CFRP parts, which can lead to misinterpretations of their results. Combining such NDT methods to achieve reliable, cheaper results is consequently interesting (STEINCHEN; YANG, 2003; KESSLER, 2004; IBARRA-CASTANEDO, 2005; ZÖCKE, 2009; WORKMAN; MOORE, 2012; KARBHARI, 2013; YANG; XIE, 2016).
Data fusion tools provide promising outcomes when it comes to combining data from two or more sources in a synergistical way. Their use leads to a higher knowledge about phenomena, providing complementarity. In this field, image fusion is a very relevant topic for image-based NDT methods such as shearography and thermography (VARSHNEY, 2004; VILLANUEVA, 2009; BUSSE; SPIESSBERGER; GLEITER, 2010a; MITCHELL, 2010, 2012).
This work proposes the combined use of active lock-in thermography and square pulse shearography to inspect impact damages of different magnitudes in CFRP plates, from barely visible to perforation stages. For this purpose, several combinations of different image processing tools were developed and tested to evaluate which is the best option for fusing and segmenting impact damages in images coming from these two NDT techniques. Ultrasound results were used as reference to allow one to compare the outcomes of the proposed tools. Metrics used for assessing of binary data were used for the evaluations and the equivalent diameter was used also as a mean to evaluate the developed tools. With these tasks, one tries to
answer if it is possible to have a system which uses image fusion from thermography and shearography and provides information on damages caused by impacts in CFRP plates which are comparable to the ones provided by an ultrasound system. The results indicated that the fusion of the proposed NDT methods is technically feasible to achieve inspection results close to the ones provided by the reference technology and it can be seen as a potential alternative for industries that rely on ultrasound as the main NDT technique.
1.2 OBJECTIVES
1.2.1 Main objective
“Develop methods for fusing images captured by an optical lock-in thermography system and an optical square-pulse shearography system to improve the detectability and reliability of NDT of CFRP samples containing impact damages, allowing one to achieve results close to the ones provided by NDT reference technology.”
1.2.2 Specific objectives and the question to be answered
To reach the main objective, the following specific objectives must be accomplished: • to study, characterize, manufacture CFRP plates and apply controlled damages to them,
which can be representative in relation to barely visible damage inspection;
• to conduct a solid metrological characterization of these samples with ultrasound, the reference technologies, allowing one to have a reliable ground truth for the developed image fusion tools;
• to perform and optimize the measurements with the square pulse shearography technique to inspect appropriately impact damages in CFRP plates;
• to perform and optimize the measurements with the active lock-in thermography technique to inspect appropriately impact damages in CFRP plates;
• to develop image fusion tools which can generate fused images in which the maximum amount of useful information of the thermography and shearography images are present on it; and
• to evaluate the developed tools with specific metrics in relation to the ground truths provided by the reference technology, analysing the efficiency of the resulting fusion method.
“Is it possible to have a system which uses image fusion from thermography and shearography and provides information on damages caused by impacts in CFRP plates which are comparable to the ones provided by an ultrasound system?”
1.3 TEXT STRUCTURE
In chapter 2, concepts regarding composite materials are addressed, helping to understand impact damages and allowing a better development in the subsequent sections. In chapter 3, NDT methods relevant to the subject of this project are presented, together with their main characteristics and work principles. The loading techniques used to reveal defects in composites, which are pertinent to thermography and shearography, are explored in chapter 4. In chapter 5, image fusion methods are addressed, including an introduction to data fusion theory and applications of such techniques. Then, in chapter 6, the methods, the tools and the metrics applied in this work are presented. The chapter 7 addresses the results obtained with the proposed methodology, and discussion are made regarding them. Finally, a general summary of the work with the achieved results is given in chapter 8, with suggestions of future works.
2 COMPOSITE MATERIALS
This chapter starts with a brief contextualization regarding composite materials in section 2.1. After that, in section 2.2, definitions and characteristics are presented. In section 2.3, reinforcements are described, with emphasis on the carbon fibres, object of study of this work. Information about the matrices is provided in section 2.4. Finally, in section 2.5, an explanation of impact damages in composites is given.
2.1 CONTEXTUALIZATION
There are evidences of usage of composite materials since 1800 b. C. with the Egyptians, who used wood and straw combined with clay bricks to reinforce them, although there was no technical knowledge regarding such topic in those times (WEETON, 1987; KESSLER, 2004).
Several different applications could be emphasized since then, but it can be said that the current growing interest in such materials are led by the aerospace industry, since in this field requirements like outstanding mechanical properties with the lowest weight possible are essential. Some specific parts can be used as example of flagships of this event like rocket tanks, satellite components, commercial and military aircraft fuselages, i.a. Other fields are also replacing metallic alloys for composite materials: civil and military naval industries; building sector with reinforced concrete and other new combinations; sports, with golf clubs, tennis rackets, carbon fibre bike frames and equipment for nautical activities; oil and gas field, with composite pipes, ducts and tubes and also as repairs for metallic pipes (MALLICK, 1997; KESSLER, 2004; MARINUCCI, 2011; PAES, 2015; SILBERSCHMIDT, 2016).
Another field in which composite material usage has been growing lately is the automotive sector. Traditionally low-carbon and medium-carbon steel alloys are the most used materials in this field, but for many parts plastics reinforced with fibres have been replacing them because of the new gas-emission reduction requirements which lead to factors like reduction of weight. Also, the use of these new materials are also related to the reduction of vibration and acoustic noise, to the avoidance of corrosion issues and to the reduction of manufacturing costs (MALLICK, 1997; KORONIS; SILVA, 2019).
2.2 DEFINITION AND CHARACTERISTICS
Composites are the union of two or more different materials, creating an output material somehow superior to the input ones, based on the desirable characteristics of them,
with two distinct phases: one continuous called matrix and other intermittent called reinforcement. These two phases typically have different, complementary properties and the boundary between them is called interface (ASHBEE, 1993; KESSLER, 2004; REZENDE; COSTA; BOTELHO, 2011).
One of the most important characteristics in composites is the cohesion force between matrix and reinforcements called adhesion. This force needs to be as large as possible to ensure that the composite is suitable for its duty. A poor adhesion leads to the situation in which the reinforcements work more as discontinuities, acting oppositely as planned (ASHBEE, 1993; REZENDE; COSTA; BOTELHO, 2011).
Besides adhesion, its performance depends on the reinforcement orientation, position, dimension, and format. The chemical composition of both matrix and reinforcements is also important. The output properties of a composite material are also directly proportional to the properties of their components, being related to the volumetric ratio of each constituent. Yet, matrix and reinforcements cannot present any incompatibility, such as very different thermal expansion coefficients. It is important to notice with this information that a composite has its properties related to the direction of the reinforcements, phenomenon called anisotropy (WEETON, 1987; KESSLER, 2004).
The main idea behind composites is to blend reinforcements and a matrix based on their properties, so they can perform specific tasks better, without bringing any disadvantage. In a general way, reinforcements are responsible for bringing the mechanical properties. However, they cannot be used alone because they are difficult to handle and highly sensible to hostile environments. The matrices are then a protection resource for the reinforcements while they guarantee their position and orientation and distribute the forces sustained by them in the most uniform way possible. Yet, often the matrices provide other desirable properties such as impact or wear resistances (WEETON, 1987; ASHBEE, 1993; MALLICK, 1997).
A comparison between material properties is provided in table 2.1, in which it is possible to see the similar mechanical properties of the evaluated materials and the difference between their weights.
There are several ways to manufacture and employ composite materials. The format of reinforcements is one of the most relevant variants. Considering polymeric matrices, one of the most used is the format of fibre, which is any elongated material with a minimum dimension ratio of 10 to 1. Long fibres are also called filaments. The particulate format is also very common in many fields, being the arrangement with small cubes, spheres, i.a. Laminates are also a very relevant format, in which layers of fibre tissue impregnated a specific matrix. The
layers can be used with different orientations, providing a significant design advantage (MARINUCCI, 2011). These formats are shown in figure 2.1.
Considering the fibre orientation, the most common ones are presented in figure 2.2. The nomenclature used to designate each orientation is given by the American Society for Testing and Materials (ASTM) with the standard ASTM D6507:2016 (ASTM INTERNATIONAL, 2016a).
2.3 REINFORCEMENTS
As mentioned earlier, the reinforcements can enhance many output properties of the composite materials, such as the tensile and compressive strengths, the performance in relation to fatigue, the fracture behaviour, i.a. (WEETON, 1987; MALLICK, 1997).
Table 2.1 – Comparison between mechanical properties of common metallic alloys and composites.
Material Density [g/cm³] Young modulus [GPa] Ultimate tensile strength [MPa] Steel, SAE 1010 7.87 207.0 365.0 Steel, SAE 1020 7.86 210.0 400.9 Aluminium alloy 7075-T6 2.70 68.9 572.2
Titanium alloy Ti-6Al-4V 4.43 110.0 1171.0
Epoxy + carbon fibre (high
strength) 1.55 138.0 1550.1
Epoxy + carbon fibre (high
modulus) 1.63 215.0 1240.4
Epoxy + E-glass fibre 1.85 39.3 965.0
Epoxy + Kevlar 49 1.38 75.8 1378.0
Source: adapted (ASHBEE, 1993; MALLICK, 1997; MARINUCCI, 2011).
Figure 2.1 – Most common reinforcement formats for polymeric composites.
With reinforced polymers, the most used reinforcement are the glass and carbon fibres. Other less employed examples are the bore, the aramid, and the asbestos (ASHBEE, 1993; MALLICK, 1997; KELLY; ZWEBEN, 2000).
Carbon fibres are frequently employed for manufacturing structural elements, especially in the aerospace industry. In general, they are highly resistant and stiff fibres, even in high temperatures. However, they are usually more expensive than other reinforcements, such as glass fibres. This may lead to implications regarding the fields in which this material is applied, although there is already a noticeable trend related to the reduction of its cost. In relation to their thermal properties, carbon fibres are in most cases good heat-conductors. Their thermal expansion coefficient is low, reaching negative values in some cases1, which is 1This is a very interesting characteristic, especially for metrology equipment, since it can provide less
uncertainty related to thermal expansion of components.
Figure 2.2 – Most common reinforcement formats for polymeric composites.
interesting e.g. for parts that need good geometrical stability. Still, this can be an issue when working with different materials and significant temperature variations (WEETON, 1987; KESSLER, 2004; AMENABAR et al., 2011; REZENDE; COSTA; BOTELHO, 2011).
Most of CFRPs are noticeably light-weighted and stiff. However, they do not have a good resistance against low- and high-speed impacts, mainly due to the low resilience of the carbon fibres, as shown in table 2.2. It is possible to see that the use of carbon fibres means more ultimate tensile strength but less impact resistance. This is a matter to be addressed, since in many fields such as aerospace impacts damages are very often and thus relevant (ASHBEE, 1993; MALLICK, 1997; KESSLER, 2004).
2.4 MATRICES
Matrices can be made of metals, ceramics and polymers, the latter one being the most used. The choice regarding the matrix material of a composite depends on both the reinforcement and the application of the desired part. The matrix characteristics are crucial for the following properties of the composite: Young's modulus, ultimate tensile strength, compression strength, shear modulus, shear strength, hardness, moisture resistance and some thermal properties. Most important, they can also be related to the improvement of the impact resistance in composites (MALLICK, 1997; KELLY; ZWEBEN, 2000; KORONIS; SILVA, 2019).
Polymers are matrix materials with great potential due to their relatively simple manufacturing process, their low density, their chemical stability and their interesting mechanical, dielectric and magnetic properties, although their work temperature tend to be
Table 2.2 – Ultimate tensile strength and impact resistance of some composite materials depending of the matrix and fibre materials.
Material Ultimate tensile strength [MPa] Impact resistance [J/cm]
Acrylonitrile butadiene styrene + 30%
glass fibres 100 3.5
Acrylonitrile butadiene styrene + 30%
carbon fibres 130 2.4
Nylon + 30% glass fibres 148 3.7
Nylon + 30% carbon fibres 207 4.3
Polycarbonate + 30% glass fibres 128 9.3
Polycarbonate + 30% carbon fibres 165 5.3
lower than for other materials, as shown in table 2.3 (KELLY; ZWEBEN, 2000; KORONIS; SILVA, 2019).
There are differences between a thermoset and a thermoplastic polymer when employed in composites. The first has a great fibre adhesion and provide good chemical stability, but it is less resilient, non-recyclable and the manufacturing process of thermoset-based composites are more time-demanding, being most used in structural composites. Some typical examples of thermoset polymers for composites are the epoxies and the polyesters. The latter provides higher durability, fracture resistance and reparability, however lower chemical resistance, being used in large-scale production processes and less mechanically demanding applications. High-performance thermoplastic polymers such as the polyether-ether-ketone and the polyaryl-ether-ketone. The epoxies are the most used polymeric matrix (ASHBEE, 1993; KELLY; ZWEBEN, 2000; KORONIS; SILVA, 2019).
2.5 IMPACT DAMAGES IN COMPOSITE MATERIALS
2.5.1 Impact loadings
It is possible to say that defects in composite materials are considerably more complicated to analyse than in metallic alloys. Two reasons arise in this sense. The first is that there is yet much to study about such materials to achieve a similar comprehension that exists related to metals, although several researchers have been investigating this topic. The second is the rise of the complexity provided by their anisotropy, given that most of the traditional metallic materials are isotropic and that the presence of more than one phase with different properties alters the stress distribution inside the component. This is especially intricate when considering dynamic2 loadings, such as impacts, which are composed of various mechanisms and, hence, request different models and assessment tools. High loading rates lead to significant changes in the evolution of the damage modes. In this sense, it is important to understand the
2The antonym of quasi-static processes, in this sense.
Table 2.3 – Maximum services temperatures of some polymers commonly used in composite materials.
Material Max. service temperature [ºC]
Bisphenol A diglycidyl ether 125
Tetraglycidyl diaminodiphenylmethane 190
Polyether-ether-ketone 250
Polyamide-imide 230
