1,426 research outputs found

    Investigation and Evaluation of Methods for Measuring Surface Texture on Worktops and Kitchen Fronts

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    In this Master Thesis different methods for measuring and evaluating surface textures have been investigated and evaluated. A method for digitizing textures called photometric stereo have also been studied. The purpose has been to find methods that can replace or supplement the current method of visual inspection used for surface texture studies by IKEA of Sweden. The suggested methods are going to be used by the company for securing that the surface textures on laminate worktops and pigment lacquered kitchen fronts are both consistent between different suppliers and matching the original reference sample. The thesis work has been written in three phases. First a background study of surface texture measurement methods has been carried out as well as a market research about what instruments are used for surface texture measurements. The next step has been an investigation of what problems IKEA is experiencing and finding the cause of these problems. This includes studies of the manufacturing process for laminates, the tools used for giving texture to laminates and how textures patterns are developed. The manufacturing process of the kitchen front has been also studied. In the last step the different methods have been tested and evaluated based on the needs of IKEA of Sweden

    Leather inspection and characterization using non-destructive techniques

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    Leather is a widely used component of many products such as shoes, car seats, garments and other leather goods. Because it is a natural material, a tanned hide will contain visual and hidden flaws. In addition, its mechanical properties vary over the hide. At present, hides are inspected and assessed by skilled operatives. Further, current objective leather testing requires removal of samples and is either destructive and/or incompatible with real time operation, and little or no information about the rest of the skin is provided.A novel mechanical scanning system was built for non-destructive leather testing. The investigation was focused on two of the most important physical leather properties, static compressibility across thickness and tensile properties for low strain regions. The results of static compression energy measurements for a compressive strain of 10 percent, showed a close agreement with the results of tests performed by a conventional compressibility tester. Further, the results of strain energy and stress measurements for a strain of 2 percent, revealed a very good correlation with the results of conventional tensile tests for a similar strain.The application of infrared thermography, a non destructive and contact less technique, to leather characterisation and inspection was investigated in this work. It was shown that this technique could be used for detecting defects in leather, as well as for estimating their size and deepness. However, defect visibility by infrared thermography is conditioned by the fact that a defective area has to cause different material properties or produce an internal thermal resistance. Further, the prohibitive cost of infrared thermography cameras for automation is a serious limitation for its application in current leather testing. It is recommended that the ideal testing system would be based on the combination of mechanical scanning, normal computer vision and infrared thermography. The normal computer vision part of this system would be responsible for measuring area and detecting defects that are visible in nature. The infrared thermography part of the system would be responsible for detecting the type of defects overlooked by the previous method, as well as some thermo-physical parameters. Finally, the Mechanical Scanning System would provide the physical properties of leather, like compressibility, tensile modulus, shear stress and softness that the vision based inspection systems are incapable of providing. In this way, every single skin could be completely characterised in terms of defects and physical properties

    A Guide to Additive Manufacturing

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    This open access book gives both a theoretical and practical overview of several important aspects of additive manufacturing (AM). It is written in an educative style to enable the reader to understand and apply the material. It begins with an introduction to AM technologies and the general workflow, as well as an overview of the current standards within AM. In the following chapter, a more in-depth description is given of design optimization and simulation for AM in polymers and metals, including practical guidelines for topology optimization and the use of lattice structures. Special attention is also given to the economics of AM and when the technology offers a benefit compared to conventional manufacturing processes. This is followed by a chapter with practical insights into how AM materials and processing parameters are developed for both material extrusion and powder bed fusion. The final chapter describes functionally graded AM in various materials and technologies. Throughout the book, a large number of industrial applications are described to exemplify the benefits of AM

    Wood Science for Conservation of Cultural Heritage – Braga 2008

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    COST Action IE0601 "Wood Science for Conservation of Cultural Heritage" (www.woodculther.org) aims to improve the conservation of European wooden cultural heritage objects, by fostering research and interaction between researchers in various fields of wood science, conservators of wooden artworks, scientists from related fields. These proceedings contain the papers presented in the 2nd International Conference held in Braga (Portugal) 5-7/11/2008, dealing with themes such as material properties, biological degradation, characterization and measurement techniques, conservation, structures. This conference was patronized by the European Society for Wood Mechanics (ESWM), an informal body promoting wood mechanics in Europe by regular organisation of meetings through running COST Actions

    A Guide to Additive Manufacturing

    Get PDF
    This open access book gives both a theoretical and practical overview of several important aspects of additive manufacturing (AM). It is written in an educative style to enable the reader to understand and apply the material. It begins with an introduction to AM technologies and the general workflow, as well as an overview of the current standards within AM. In the following chapter, a more in-depth description is given of design optimization and simulation for AM in polymers and metals, including practical guidelines for topology optimization and the use of lattice structures. Special attention is also given to the economics of AM and when the technology offers a benefit compared to conventional manufacturing processes. This is followed by a chapter with practical insights into how AM materials and processing parameters are developed for both material extrusion and powder bed fusion. The final chapter describes functionally graded AM in various materials and technologies. Throughout the book, a large number of industrial applications are described to exemplify the benefits of AM

    Deteção automática de defeitos em couro

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    Dissertação de mestrado em Informatics EngineeringEsta dissertação desenvolve-se em torno do problema da deteção de defeitos em couro. A deteção de defeitos em couro é um problema tradicionalmente resolvido manualmente, usando avaliadores ex perientes na inspeção do couro. No entanto, como esta tarefa é lenta e suscetível ao erro humano, ao longo dos últimos 20 anos tem-se procurado soluções que automatizem a tarefa. Assim, surgiram várias soluções capazes de resolver o problema eficazmente utilizando técnicas de Machine Learning e Visão por Computador. No entanto, todas elas requerem um conjunto de dados de grande dimensão anotado e balanceado entre as várias categorias. Assim, esta dissertação pretende automatizar o processo tradicio nal, usando técnicas de Machine Learning, mas sem recorrer a datasets anotados de grandes dimensões. Para tal, são exploradas técnicas de Novelty Detection, as quais permitem resolver a tarefa de inspeção de defeitos utilizando um conjunto de dados não supervsionado, pequeno e não balanceado. Nesta dis sertação foram analisadas e testadas as seguintes técnicas de novelty detection: MSE Autoencoder, SSIM Autoencoder, CFLOW, STFPM, Reverse, and DRAEM. Estas técnicas foram treinadas e testadas com dois conjuntos de dados diferentes: MVTEC e Neadvance. As técnicas analisadas detectam e localizam a mai oria dos defeitos das imagens do MVTEC. Contudo, têm dificuldades em detetar os defeitos das imagens do dataset da Neadvance. Com base nos resultados obtidos, é proposta a melhor metodologia a usar para três diferentes cenários. No caso do poder computacional ser baixo, SSIM Autoencoder deve ser a técnica usada. No caso onde há poder computational suficiente e os exemplos a analisar são de uma só cor, DRAEM deve ser a técnica escolhida. Em qualquer outro caso, o STFPM deve ser a opção escolhida.This dissertation develops around the leather defects detection problem. The leather defects detec tion problem is traditionally manually solved, using experient assorters in the leather inspection. However, as this task is slow and prone to human error, over the last 20 years the searching for solutions that automatize this task has continued. In this way, several solutions capable to solve the problem effi ciently emerged using Machine Learning and Computer Vision techniques. Nonetheless, they all require a high-dimension dataset labeled and balanced between all categories. Thus, this dissertation pretends to automatize the traditional process, using the Machine Learning techniques without requiring a large dimensions labelled dataset. To this end, there will be explored Novelty Detection techniques, that in tend to solve the leather inspection task using an unsupervised small and non-balanced dataset. This dissertation analyzed and tested the following Novelty Detection techniques: MSE Autoencoder, SSIM Autoencoder, CFLOW, STFPM, Reverse, and DRAEM. These techniques are trained and tested in two distinct datasets: MVTEC and Neadvance. The analyzed techniques detect and localize most MVTEC defects. However, they have difficulties in defect detection on Neadvance samples. Based on the ob tained results, it is proposed the best methodology to use for three distinct scenarios. In the case where the computational power available is low, SSIM Autoencoder should be the technique to use. In the case where there is enough computational power and the samples to inspect have the same color, DRAEM should be the chosen technique. In any other case, the STFPM should be the chosen option
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