Automated Pipe Spool Recognition in Cluttered Point Clouds

Construction management is inextricably linked to the awareness and control of 3D geometry. Progress tracking, quality assurance/quality control, and the location, movement, and assembly of materials are all critical processes that rely on the ability to monitor 3D geometry. Therefore, advanced capabilities in site metrology and computer vision will be the foundation for the next generation of assessment tools that empower project leaders, planners, and workers. 3D imaging devices enable the capture of the existing geometric conditions of a construction site or a fabricated mechanical or structural assembly objectively, accurately, quickly, and with greater detail and continuity than any manual measurement methods. Within the construction literature, these devices have been applied in systems that compare as-built scans to 3D CAD design files in order to inspect the geometrical compliance of a fabricated assembly to contractually stipulated dtolerances. However, before comparisons of this type can be made, the particular object of interest needs to be isolated from background objects and clutter captured by the indiscriminate 3D imaging device. Thus far, object of interest extraction from cluttered construction data has remained a manual process. This thesis explores the process of automated information extraction in order to improve the availability of information about 3D geometries on construction projects and improve the execution of component inspection, and progress tracking. Specifically, the scope of the research is limited to automatically recognizing and isolating pipe spools from their cluttered point cloud scans. Two approaches are developed and evaluated. The contributions of the work are as follows: (1) A number of challenges involved in applying RANdom SAmple Consensus (RANSAC) to pipe spool recognition are identified. (2) An effective spatial search and pipe spool extraction algorithm based on local data level curvature estimation, density-based clustering, and bag-of-features matching is presented. The algorithm is validated on two case studies and is shown to successfully extract pipe spools from cluttered point clouds and successfully differentiate between the specific pipe spool of interest and other similar pipe spools in the same search space. Finally, (3) the accuracy of curvature estimation using data collected by low-cost range-cameras is tested and the viability of use of low-cost range-cameras for object search, localization, and extraction is critically assessed

Nondestructive Testing Methods and New Applications

Nondestructive testing enables scientists and engineers to evaluate the integrity of their structures and the properties of their materials or components non-intrusively, and in some instances in real-time fashion. Applying the Nondestructive techniques and modalities offers valuable savings and guarantees the quality of engineered systems and products. This technology can be employed through different modalities that include contact methods such as ultrasonic, eddy current, magnetic particles, and liquid penetrant, in addition to contact-less methods such as in thermography, radiography, and shearography. This book seeks to introduce some of the Nondestructive testing methods from its theoretical fundamentals to its specific applications. Additionally, the text contains several novel implementations of such techniques in different fields, including the assessment of civil structures (concrete) to its application in medicine

NASA SBIR abstracts of 1991 phase 1 projects

The objectives of 301 projects placed under contract by the Small Business Innovation Research (SBIR) program of the National Aeronautics and Space Administration (NASA) are described. These projects were selected competitively from among proposals submitted to NASA in response to the 1991 SBIR Program Solicitation. The basic document consists of edited, non-proprietary abstracts of the winning proposals submitted by small businesses. The abstracts are presented under the 15 technical topics within which Phase 1 proposals were solicited. Each project was assigned a sequential identifying number from 001 to 301, in order of its appearance in the body of the report. Appendixes to provide additional information about the SBIR program and permit cross-reference of the 1991 Phase 1 projects by company name, location by state, principal investigator, NASA Field Center responsible for management of each project, and NASA contract number are included

Estudio de métodos de construcción de ensembles de clasificadores y aplicaciones

La inteligencia artificial se dedica a la creación de sistemas informáticos con un comportamiento inteligente. Dentro de este área el aprendizaje computacional estudia la creación de sistemas que aprenden por sí mismos. Un tipo de aprendizaje computacional es el aprendizaje supervisado, en el cual, se le proporcionan al sistema tanto las entradas como la salida esperada y el sistema aprende a partir de estos datos. Un sistema de este tipo se denomina clasificador. En ocasiones ocurre, que en el conjunto de ejemplos que utiliza el sistema para aprender, el número de ejemplos de un tipo es mucho mayor que el número de ejemplos de otro tipo. Cuando esto ocurre se habla de conjuntos desequilibrados. La combinación de varios clasificadores es lo que se denomina "ensemble", y a menudo ofrece mejores resultados que cualquiera de los miembros que lo forman. Una de las claves para el buen funcionamiento de los ensembles es la diversidad. Esta tesis, se centra en el desarrollo de nuevos algoritmos de construcción de ensembles, centrados en técnicas de incremento de la diversidad y en los problemas desequilibrados. Adicionalmente, se aplican estas técnicas a la solución de varias problemas industriales.Ministerio de Economía y Competitividad, proyecto TIN-2011-2404

Design, organization and implementation of a methods pool and an application systematics for condition based maintenance

Zunehmender Wettbewerb in der Industrie erfordert immer kürzere Amortisationszeiten von kapitalintensiven Produktionsanlangen. Wesentliche Voraussetzungen für die Realisierung kurzer Amortisationszeiträume sind eine hohe Verfügbarkeit der Anlagen und das Erreichen einer gleichmäßig hohen und konstanten Produktqualität. Eine effiziente Instandhaltungsstrategie unterstützt diese Anforderungen an die Verfügbarkeit und an die Produktqualität, vor allem durch eine geringe Bedarfswartung und zunehmend vorbeugende Instandhaltungsbemühungen. In der Industrie wird hierzu häufig die zustandsbasierte Instandhaltung (Condition Based Maintenance - CBM) angewendet. Die CBM Methode versucht aus Zustandseinschätzung der Maschinen, abgeleitet von verschiedenen Zustandsüberwachungs-Verfahren (Condition Monitoring Technique - CMT) und zerstörungsfreien Prüfungen (Nondestructive Test - NDT), erste Mängel zu identifizieren, bevor sie sich kritisch auf die Produktion auswirken. Ein effektives CBM Programm verlangt eine frühe Fehlererkennung und eine genaue Identifikation der Fehlerattribute. Diese Anforderungen werden in der Industrie heute noch unzureichend erfüllt. Die Ursache liegt vor allem in den hohen Kosten, die sich aufgrund unzureichender Information über die potenziellen Fehler ergeben, sowie in der unzulänglichen Kenntnis oder ungeeigneten Anwendung von verschiedenem CMTs und NDTs begründet. Daher werden im Rahmen dieser Arbeit eine neuartige Toolbox und ein Anwendungskonzept entwickelt, um die Umsetzung eines effektiven CBM Programms in der Automobil-Zulieferindustrie zu unterstützen. Hierbei ist der Ansatz so allgemein gewählt, dass er nicht nur auf das Anwendungsgebiet der Automobilindustrie beschränkt ist, sondern auch auf die allgemeine Herstellungs- oder Produktionsindustrie angewendet werden kann. Die CBM-Toolbox setzt sich aus drei Hauptwerkzeugen zusammen. Das erste Werkzeug fasst statistische Fehler-Analysen zusammen, die die in einem Informationssystem des Betriebes vorhandenen Fehlerdaten auswertet, um die relevanten Informationen tabellarisch bzw. grafisch darzustellen. Das zweite Werkzeug ist eine Wissensdatenbank in der das Expertenwissen über verschiedene CMTs und NDTs verwaltet wird. Dieses Expertenwissen ist so strukturiert, dass zusätzlich zu jeder Methode, ihre Anwendbarkeit, Nachweisbarkeit und Vorteile bzw. Nachteile dargestellt werden. Das dritte Werkzeug ist eine objektbasierte Problem-und-Ursache-Analyse, deren Ergebnis eine tabellarisch dargestellte Problem-Ursache Beziehung von besonderen Maschinenanlagen ist. Diese Hauptwerkzeuge werden durch zwei weitere Werkzeuge, ein Finanzanalyse-Werkzeug und eine Auswahlmatrix ergänzt, die die verschiedenen Entscheidungsmöglichkeiten hinsichtlich der Umsetzbarkeit bewertet.The everyday increasing competition in industry and the compulsion of faster investment paybacks for complex and expensive machinery, in addition to operational safety, health and environmental requirements, take for granted high availability of the production machinery and high and stable quality of products. These targets are reached only if the machinery is kept in proper working condition by utilizing an appropriate maintenance tactic. In this frame of thought, monitoring of machinery systems has become progressively more important in meeting the rapidly changing maintenance requirements of today’s manufacturing systems. Besides, as the pressure to reduce manning in plants increases, so does the need for additional automation and reduced organizational level maintenance. Augmented automation in manufacturing plants has led to rapid growth in the number of machinery sensors installed. Along with reduced manning, increased operating tempos are requiring maintenance providers to make repairs faster and ensure that equipment operates reliably for longer periods. To deal with these challenges, condition based maintenance (CBM) has been widely employed within industry. CBM, as a preventive and predictive action, strives to identify incipient faults before they become critical through structural condition assessment derived from Different condition monitoring techniques (CMT) and nondestructive tests (NDT). An effective CBM program requires early recognition of failures and accurate identification of the associated attributes in a feasible manner. The achievement of this proficiency in industry is still intricate and relatively expensive due to deficient information about the potential failures as well as inadequate knowledge or improper application of different CMTs and NDTs. Accordingly, a new toolbox has been developed to facilitate and sustain effective CBM programs in the automotive supply industry. The CBM toolbox is consisted of three major tools. The first tool is a series of statistical failure analyses which uses the failure history data available in a plant’s information system to generate valuable information in tabulated and graphical postures. The second tool is a repository filled with expert knowledge about different CMTs and NDTs formatted in a way that in addition to the concept of each technique, its applicability, detectability, and its pros and cons are expressed. The third tool is an object based problem and cause analysis whose outcome is tabulated problem-cause relationships associated with particular machinery objects. These major tools are also accompanied by two supplementary tools, a financial analysis tool and a selection matrix, to ensure feasibility of all undertaken decisions while using the toolbox

Applications of x-ray computed microtomography to material science: devices and prespectives

2006/2007The three-dimensional visualization of the inner microstructural features of objects and materials is an aspect of relevant interest for a wide range of scientific and industrial applications. X-ray computed microtomography (μ-CT) is a powerful non-destructive technique capable to satisfy these needs. Once the complete reconstruction of the sample is available, a quantitative characterisation of the microstructure is essential. Through digital image processing tools, image analysis software or custom developed algorithms, it is possible to obtain an exhaustive geometrical, morphological and topological description of the features inside the volume, or to extract other particular parameters of interest (e.g. porosity, voids distribution, cell size distribution, average struts length, connectivity between the cells, tortuosity). This thesis was carried out at the third-generation Elettra Synchrotron Radiation Facility (Trieste, Italy), where a hard X-ray imaging beamline is available. The experience developed at this beamline has leaded scientists to design a complementary state-of-the-art μ-CT facility based on a micro-focus X-ray source, working both in absorption and phase contrast mode. In this dissertation a detailed description of this facility is given together with a rigorous characterization of the imaging system capabilities, in terms of the actual achievable spatial resolution, in order to optimize the working parameters for the different experiments. The main artefacts that concur to the degradation of the quality of the reconstructed images have been considered (e.g. beam hardening effects, ring artefacts, uncertainness associated with the cone-beam geometry): procedures are presented in order to eliminate, or at least to reduce, the causes of these artefacts. The aspects related to the digital image processing of the reconstructed data are intensively developed in this study: appropriated methodologies have been elaborated capable to deal with the different three-dimensional data of complex porous media, providing a correlation between the microstructure and the macroscopic behaviour of the observed materials. Three representative examples obtained with the described methods are used to demonstrate the application of μ-CT, combined with the developed image processing tools, to material science: the geometrical and morphological characterisation of polyurethane foams employed in the automotive industry due their vibro-acoustic properties; a new approach to characterize the resonance spruce wood microstructure in order to study its acoustical behaviour; finally, the possibility of revealing defects in hybrid-friction stir welded aluminium joints, guiding the optimization of the process parameters.La visualizzazione tridimensionale della struttura interna di oggetti e materiali costituisce un aspetto di notevole interesse per una ampia gamma di applicazioni scientifiche ed industriali. La microtomografia computerizzata a raggi X (μ-CT) rappresenta una potente tecnica di indagine adeguata a soddisfare tali richieste. Una volta completata la ricostruzione del campione in esame, è essenziale poter fornire una caratterizzazione quantitativa della microstruttura evidenziata. Attraverso gli strumenti messi a disposizione dalle moderne tecniche di analisi di immagine, per mezzo di software dedicati o algoritmi personalizzati, è possibile ottenere una descrizione esaustiva della geometria, morfologia e topologia degli elementi microstrutturali presenti, che consenta l’estrazione dei parametri di interesse per la particolare applicazione (porosità, distribuzione dei vuoti, dimensione degli elementi, lunghezze caratteristiche, grado di interconnessione, tortuosità etc.). Il presente lavoro di tesi è stato svolto presso il laboratorio di luce sincrotrone di terza generazione Elettra (Trieste, Italia), dove è disponibile una linea sperimentale dedicata all’imaging con raggi X duri. L’esperienza acquisita da parte dei ricercatori di questa linea ha consentito poi la progettazione di una stazione per μ-CT complementare, allo stato dell’arte e basata su una sorgente di radiazione a microfuoco, capace di operare con modalità di raccolta delle immagini sia in assorbimento sia in contrasto di fase. In questa tesi viene fornita una dettagliata descrizione della stazione, accompagnata da una rigorosa caratterizzazione del sistema impiegato per l’acquisizione e la ricostruzione delle immagini, in termini di risoluzione spaziale raggiungibile, così da consentire l’ottimizzazione dei parametri critici di lavoro nelle differenti condizioni sperimentali. Vengono poi presi in considerazione i principali artefatti che contribuiscono al deterioramento della qualità delle immagini ottenute (come il beam hardening, gli artefatti ad anello, gli artefatti legati all’incertezza geometrica associata al fascio conico etc.): vengono quindi proposti dei metodi per l’eliminazione, o almeno la riduzione, delle cause che li determinano. Nella tesi inoltre sono sviluppati in maniera approfondita gli aspetti connessi al trattamento dei dati digitali raccolti: sono state infatti elaborate delle metodologie appropriate, capaci di trattare i diversi tipi di dato provenienti dall’analisi di mezzi porosi, determinanti per la comprensione della correlazione tra la microstruttura del materiale ed il suo comportamento macroscopico. Infine, vengono proposti tre esempi rappresentativi per dimostrare l’efficacia dell’applicazione della μ-CT, in combinazione con gli strumenti di analisi di immagine messi a punto, alla scienza dei materiali: la caratterizzazione geometrica e morfologica di schiume di poliuretano impiegate nell’industria automobilistica come isolante vibro-acustico; un nuovo approccio rivolto alla caratterizzazione della struttura del legno di risonanza al fine di studiarne il comportamento acustico; la possibilità di mettere in luce i difetti in giunti di saldatura di leghe d’alluminio realizzati con la tecnica ibrida friction stir welding/TIG in maniera da ottimizzare i parametri di processo.XX Ciclo197

A Methodological Approach to Knowledge-Based Engineering Systems for Manufacturing

A survey of implementations of the knowledge-based engineering approach in different technological sectors is presented. The main objectives and techniques of examined applications are pointed out to illustrate the trends and peculiarities for a number of manufacturing field. Existing methods for the development of these engineering systems are then examined in order to identify critical aspects when applied to manufacturing. A new methodological approach is proposed to overcome some specific limitations that emerged from the above-mentioned survey. The aim is to provide an innovative method for the implementation of knowledge-based engineering applications in the field of industrial production. As a starting point, the field of application of the system is defined using a spatial representation. The conceptual design phase is carried out with the aid of a matrix structure containing the most relevant elements of the system and their relations. In particular, objectives, descriptors, inputs and actions are defined and qualified using categorical attributes. The proposed method is then applied to three case studies with different locations in the applicability space. All the relevant elements of the detailed implementation of these systems are described. The relations with assumptions made during the design are highlighted to validate the effectiveness of the proposed method. The adoption of case studies with notably different applications also reveals the versatility in the application of the method

Deep learning in food category recognition

Integrating artificial intelligence with food category recognition has been a field of interest for research for the past few decades. It is potentially one of the next steps in revolutionizing human interaction with food. The modern advent of big data and the development of data-oriented fields like deep learning have provided advancements in food category recognition. With increasing computational power and ever-larger food datasets, the approach’s potential has yet to be realized. This survey provides an overview of methods that can be applied to various food category recognition tasks, including detecting type, ingredients, quality, and quantity. We survey the core components for constructing a machine learning system for food category recognition, including datasets, data augmentation, hand-crafted feature extraction, and machine learning algorithms. We place a particular focus on the field of deep learning, including the utilization of convolutional neural networks, transfer learning, and semi-supervised learning. We provide an overview of relevant studies to promote further developments in food category recognition for research and industrial applicationsMRC (MC_PC_17171)Royal Society (RP202G0230)BHF (AA/18/3/34220)Hope Foundation for Cancer Research (RM60G0680)GCRF (P202PF11)Sino-UK Industrial Fund (RP202G0289)LIAS (P202ED10Data Science Enhancement Fund (P202RE237)Fight for Sight (24NN201);Sino-UK Education Fund (OP202006)BBSRC (RM32G0178B8