Defect Detection for Patterned Fabric Images Based on GHOG and Low-Rank Decomposition

In contrast to defect-free fabric images with macro-homogeneous textures and regular patterns, the fabric images with the defect are characterized by the defect regions that are salient and sparse among the redundant background. Therefore, as an effective tool for separating an image into a redundant part (the background) and sparse part (the defect), the low-rank decomposition model provides an ideal solution for patterned fabric defect detection. In this paper, a novel patterned method for fabric defect detection is proposed based on a novel texture descriptor and the low-rank decomposition model. First, an efficient second-order orientation-aware descriptor, denoted as GHOG, is designed by combining Gabor and histogram of oriented gradient (HOG). In addition, a spatial pooling strategy based on human vision mechanism is utilized to further improve the discrimination ability of the proposed descriptor. The proposed texture descriptor can make the defect-free image blocks lay in a low-rank subspace, while the defective image blocks have deviated from this subspace. Then, a constructed low-rank decomposition model divides the feature matrix generated from all the image blocks into a low-rank part, which represents the defect-free background, and a sparse part, which represents sparse defects. In addition, a non-convex log det as a smooth surrogate function is utilized to improve the efficiency of the constructed low-rank model. Finally, the defects are localized by segmenting the saliency map generated by the sparse matrix. The qualitative results and quantitative evaluation results demonstrate that the proposed method improves the detection accuracy and self-adaptivity comparing with the state-of-the-art methods

Fabric Defect Detection Based on Pattern Template Correction

This paper proposes a novel template-based correction (TC) method for the defect detection on images with periodic structures. In this method, a fabric image is segmented into lattices according to variation regularity, and correction is applied to reduce the effect of misalignment among lattices. Also, defect-free lattices are chosen for establishing an average template as a uniform reference. Furthermore, the defect detection procedure is composed of two steps, namely, defective lattices locating and defect shape outlining. Defective lattices locating is based on classification for defect-free and defective patterns, which involves an improved E-V method with template-based correction and centralized processing, while defect shape outlining provides pixel-level results by threshold segmentation. In this paper we also present some experiments on fabric defect detection. Experimental results show that the proposed method is effective

Modelling visual search for surface defects

Much work has been done on developing algorithms for automated surface defect detection. However, comparisons between these models and human perception are rarely carried out. This thesis aims to investigate how well human observers can nd defects in textured surfaces, over a wide range of task di culties. Stimuli for experiments will be generated using texture synthesis methods and human search strategies will be captured by use of an eye tracker. Two di erent modelling approaches will be explored. A computational LNL-based model will be developed and compared to human performance in terms of the number of xations required to find the target. Secondly, a stochastic simulation, based on empirical distributions of saccades, will be compared to human search strategies

Smart optical coordinate and surface metrology

Manufacturing has recently experienced increased adoption of optimised and fast solutions for checking product quality during fabrication, allowing for manufacturing times and costs to be significantly reduced. Due to the integration of machine learning algorithms, advanced sensors and faster processing systems, smart instruments can autonomously plan measurement pipelines, perform decisional tasks and trigger correctional actions as required. In this paper, we summarise the state of the art in smart optical metrology, covering the latest advances in integrated intelligent solutions in optical coordinate and surface metrology, respectively for the measurement of part geometry and surface texture. Within this field, we include the use of a priori knowledge and implementation of machine learning algorithms for measurement planning optimisation. We also cover the development of multi-sensor and multi-view instrument configurations to speed up the measurement process, as well as the design of novel feedback tools for measurement quality evaluation

Acoustic emission monitoring of wood materials and timber structures: A critical review

The growing interest in timber construction and using more wood for civil engineering applications has given highlighted importance of developing non-destructive evaluation (NDE) methods for structural health monitoring and quality control of wooden construction. This study, critically reviews the acoustic emission (AE) method and its applications in the wood and timber industry. Various other NDE methods for wood monitoring such as infrared spectroscopy, stress wave, guided wave propagation, X-ray computed tomography and thermography are also included. The concept and experimentation of AE are explained, and the impact of wood properties on AE signal velocity and energy attenuation is discussed. The state-of-the-art AE monitoring of wood and timber structures is organized into six applications: (1) wood machining monitoring; (2) wood drying; (3) wood fracture; (4) timber structural health monitoring; (5) termite infestation monitoring; and (6) quality control. For each application, the opportunities that the AE method offers for in-situ monitoring or smart assessment of wood-based materials are discussed, and the challenges and direction for future research are critically outlined. Overall, compared with structural health monitoring of other materials, less attention has been paid to data-driven methods and machine learning applied to AE monitoring of wood and timber. In addition, most studies have focused on extracting simple time-domain features, whereas there is a gap in using sophisticated signal processing and feature engineering techniques. Future research should explore the sensor fusion for monitoring full-scale timber buildings and structures and focus on applying AE to large-size structures containing defects. Moreover, the effectiveness of AE methods used for wood composites and mass timber structures should be further studied

Acoustic emission monitoring of wood materials and timber structures: A critical review

The growing interest in timber construction and using more wood for civil engineering applications has given highlighted importance of developing non-destructive evaluation (NDE) methods for structural health monitoring and quality control of wooden construction. This study, critically reviews the acoustic emission (AE) method and its applications in the wood and timber industry. Various other NDE methods for wood monitoring such as infrared spectroscopy, stress wave, guided wave propagation, X-ray computed tomography and thermography are also included. The concept and experimentation of AE are explained, and the impact of wood properties on AE signal velocity and energy attenuation is discussed. The state-of-the-art AE monitoring of wood and timber structures is organized into six applications: (1) wood machining monitoring; (2) wood drying; (3) wood fracture; (4) timber structural health monitoring; (5) termite infestation monitoring; and (6) quality control. For each application, the opportunities that the AE method offers for in-situ monitoring or smart assessment of wood-based materials are discussed, and the challenges and direction for future research are critically outlined. Overall, compared with structural health monitoring of other materials, less attention has been paid to data-driven methods and machine learning applied to AE monitoring of wood and timber. In addition, most studies have focused on extracting simple time-domain features, whereas there is a gap in using sophisticated signal processing and feature engineering techniques. Future research should explore the sensor fusion for monitoring full-scale timber buildings and structures and focus on applying AE to large-size structures containing defects. Moreover, the effectiveness of AE methods used for wood composites and mass timber structures should be further studied

Non-destructive quality control of carbon anodes using modal analysis, acousto-ultrasonic and latent variable methods

La performance des cuves d’électrolyse utilisées dans la production d’aluminium primaire par le procédé Hall-Héroult est fortement influencée par la qualité des anodes de carbone. Celles-ci sont de plus en plus variables en raison de la qualité décroissante des matières premières (coke et braie) et des changements de fournisseurs qui deviennent de plus en plus fréquents afin de réduire le coût d’achat et de rencontrer les spécifications des usines. En effet, les défauts des anodes, tels les fissures, les pores et les hétérogénéités, causés par cette variabilité, doivent être détectés le plus tôt possible afin d’éviter d’utiliser des anodes défectueuses dans les cuves et/ou d’apporter des ajustements au niveau du procédé de fabrication des anodes. Cependant, les fabricants d’anodes ne sont pas préparés pour réagir à cette situation afin de maintenir une qualité d'anode stable. Par conséquent, il devient prioritaire de développer des techniques permettant d’inspecter le volume complet de chaque anode individuelle afin d’améliorer le contrôle de la qualité des anodes et de compenser la variabilité provenant des matières premières. Un système d’inspection basé sur les techniques d’analyse modale et d’acousto-ultrasonique est proposé pour contrôler la qualité des anodes de manière rapide et non destructive. Les données massives (modes de vibration et signaux acoustiques) ont été analysées à l'aide de méthodes statistiques à variables latentes, telles que l'Analyse en Composantes Principales (ACP) et la Projection sur les Structures Latentes (PSL), afin de regrouper les anodes testées en fonction de leurs signatures vibratoires et acousto-ultrasoniques. Le système d'inspection a été premièrement investigué sur des tranches d'anodes industrielles et ensuite testé sur plusieurs anodes pleine grandeur produites sous différentes conditions à l’usine de Alcoa Deschambault au Québec (ADQ). La méthode proposée a permis de distinguer les anodes saines de celles contenant des défauts ainsi que d’identifier le type et la sévérité des défauts, et de les localiser. La méthode acousto-ultrasonique a été validée qualitativement par la tomographie à rayon-X, pour les analyses des tranches d’anodes. Pour les tests réalisés sur les blocs d’anode, la validation a été réalisée au moyen de photos recueillies après avoir coupé certaines anodes parmi celles testées.The performance of the Hall-Héroult electrolysis reduction process used for the industrial aluminium smelting is strongly influenced by the quality of carbon anodes, particularly by the presence of defects in their internal structure, such as cracks, pores and heterogeneities. This is partly due to the decreasing quality and increasing variability of the raw materials available on the market as well as the frequent suppliers changes made in order to meet the smelter’s specifications and to reduce purchasing costs. However, the anode producers are not prepared to cope with these variations and in order to maintain consistent anode quality. Consequently, it becomes a priority to develop alternative methods for inspecting each anode block to improve quality control and maintain consistent anode quality in spite of the variability of incoming raw materials.A rapid and non-destructive inspection system for anode quality control is proposed based on modal analysis and acousto-ultrasonic techniques. The large set of vibration and acousto-ultrasonic data collected from baked anode materials was analyzed using multivariate latent variable methods, such as Principal Component Analysis (PCA) and Partial Least Squares (PLS), in order to cluster the tested anodes based on vibration and their acousto-ultrasonic signatures. The inspection system was investigated first using slices collected from industrial anodes and then on several full size anodes produced under different conditions at the Alcoa Deschambault in Québec (ADQ). It is shown that the proposed method allows discriminating defect-free anodes from those containing various types of defects. In addition, the acousto-ultrasonic features obtained in different frequency ranges were found to be sensitive to the defects severities and were able to locate them in anode blocks. The acousto-ultrasonic method was validated qualitatively using X-ray computed tomography, when studying the anode slices. The results obtained on the full size anode blocks were validated by means of images collected after cutting some tested anodes

Assembly Line

An assembly line is a manufacturing process in which parts are added to a product in a sequential manner using optimally planned logistics to create a finished product in the fastest possible way. It is a flow-oriented production system where the productive units performing the operations, referred to as stations, are aligned in a serial manner. The present edited book is a collection of 12 chapters written by experts and well-known professionals of the field. The volume is organized in three parts according to the last research works in assembly line subject. The first part of the book is devoted to the assembly line balancing problem. It includes chapters dealing with different problems of ALBP. In the second part of the book some optimization problems in assembly line structure are considered. In many situations there are several contradictory goals that have to be satisfied simultaneously. The third part of the book deals with testing problems in assembly line. This section gives an overview on new trends, techniques and methodologies for testing the quality of a product at the end of the assembling line

Machine learning algorithms for efficient process optimisation of variable geometries at the example of fabric forming

Für einen optimalen Betrieb erfordern moderne Produktionssysteme eine sorgfältige Einstellung der eingesetzten Fertigungsprozesse. Physikbasierte Simulationen können die Prozessoptimierung wirksam unterstützen, jedoch sind deren Rechenzeiten oft eine erhebliche Hürde. Eine Möglichkeit, Rechenzeit einzusparen sind surrogate-gestützte Optimierungsverfahren (SBO1). Surrogates sind recheneffiziente, datengetriebene Ersatzmodelle, die den Optimierer im Suchraum leiten. Sie verbessern in der Regel die Konvergenz, erweisen sich aber bei veränderlichen Optimierungsaufgaben, etwa häufigen Bauteilanpassungen nach Kundenwunsch, als unhandlich. Um auch solche variablen Optimierungsaufgaben effizient zu lösen, untersucht die vorliegende Arbeit, wie jüngste Fortschritte im Maschinenlernen (ML) – im Speziellen bei neuronalen Netzen – bestehende SBO-Techniken ergänzen können. Dabei werden drei Hauptaspekte betrachtet: erstens, ihr Potential als klassisches Surrogate für SBO, zweitens, ihre Eignung zur effiziente Bewertung der Herstellbarkeit neuer Bauteilentwürfe und drittens, ihre Möglichkeiten zur effizienten Prozessoptimierung für variable Bauteilgeometrien. Diese Fragestellungen sind grundsätzlich technologieübergreifend anwendbar und werden in dieser Arbeit am Beispiel der Textilumformung untersucht. Der erste Teil dieser Arbeit (Kapitel 3) diskutiert die Eignung tiefer neuronaler Netze als Surrogates für SBO. Hierzu werden verschiedene Netzarchitekturen untersucht und mehrere Möglichkeiten verglichen, sie in ein SBO-Framework einzubinden. Die Ergebnisse weisen ihre Eignung für SBO nach: Für eine feste Beispielgeometrie minimieren alle Varianten erfolgreich und schneller als ein Referenzalgorithmus (genetischer Algorithmus) die Zielfunktion. Um die Herstellbarkeit variabler Bauteilgeometrien zu bewerten, untersucht Kapitel 4 anschließend, wie Geometrieinformationen in ein Prozess-Surrogate eingebracht werden können. Hierzu werden zwei ML-Ansätze verglichen, ein merkmals- und ein rasterbasierter Ansatz. Der merkmalsbasierte Ansatz scannt ein Bauteil nach einzelnen, prozessrelevanten Geometriemerkmalen, der rasterbasierte Ansatz hingegen interpretiert die Geometrie als Ganzes. Beide Ansätze können das Prozessverhalten grundsätzlich erlernen, allerdings erweist sich der rasterbasierte Ansatz als einfacher übertragbar auf neue Geometrievarianten. Die Ergebnisse zeigen zudem, dass hauptsächlich die Vielfalt und weniger die Menge der Trainingsdaten diese Übertragbarkeit bestimmt. Abschließend verbindet Kapitel 5 die Surrogate-Techniken für flexible Geometrien mit variablen Prozessparametern, um eine effiziente Prozessoptimierung für variable Bauteile zu erreichen. Hierzu interagiert ein ML-Algorithmus in einer Simulationsumgebung mit generischen Geometriebeispielen und lernt, welche Geometrie, welche Umformparameter erfordert. Nach dem Training ist der Algorithmus in der Lage, auch für nicht-generische Bauteilgeometrien brauchbare Empfehlungen auszugeben. Weiter zeigt sich, dass die Empfehlungen mit ähnlicher Geschwindigkeit wie die klassische SBO zum tatsächlichen Prozessoptimum konvergieren, jedoch kein bauteilspezifisches A-priori-Sampling nötig ist. Einmal trainiert, ist der entwickelte Ansatz damit effizienter. Insgesamt zeigt diese Arbeit, wie ML-Techniken gegenwärtige SBOMethoden erweitern und so die Prozess- und Produktoptimierung zu frühen Entwicklungszeitpunkten effizient unterstützen können. Die Ergebnisse der Untersuchungen münden in Folgefragen zur Weiterentwicklung der Methoden, etwa die Integration physikalischer Bilanzgleichungen, um die Modellprognosen physikalisch konsistenter zu machen