Resumo: A indústria automotiva responde, atualmente, por uma grande parte do mercado de consumo de plásticos, por isso existe um crescente interesse no investimento em processos de reciclagem, inclusive devido à vinculação às atividades relacionadas à proteção ambiental. Dentre as peças automotivas, os pára-choques são relativamente fáceis de serem reciclados devido às suas dimensões e por constituírem, geralmente, de um único material, o polipropileno (PP). Neste trabalho, analisaram-se as propriedades mecânicas de misturas de PP virgem e reciclado, em três porcentagens diferentes, a fim de detectar as alterações das mesmas, relacionando-as à qualidade do produto. Concluiu-se que porcentagens de reciclado acima de 30% causam deterioração das propriedades mecânicas do produto. Este estudo visa a fornecer uma contribuição para o aumento na utilização de plásticos reciclados na indústria automotiva.Palavras-chave: Reciclagem de polipropileno, caracterização mecânica, pára-choques automotivos. Mechanical Characterization of Recycled Polypropylene for Automotive IndustryAbstract: The automotive industry is now responsible for a large share of plastic consumption, which prompted increasing investments in recycling processes, also due to the need of ambient protection. Among the automotives parts, bumpers are relatively easy to be recycled due to their dimensions and because they are made of a single material, polypropylene (PP). In this work, the mechanical properties of mixtures of virgin and recycled PP, in three different percentages, were analyzed in order to detect alterations and associate them with the product quality. It was concluded that percentages of recycled material above 30% deteriorate the mechanical properties of the product. This study aims to provide a contribution to the increase in the recycled plastic use in the automotive industry.Keywords: Polypropylene recycling, mechanical characterization, automotive bumpers. IntroduçãoO aumento do custo das resinas plásticas, pressionado pelas constantes flutuações do preço do petróleo no mercado internacional, tem estimulado as pesquisas em reciclagem de polímeros. Normalmente, o preço do plástico reciclado é 40% mais baixo do que o da resina virgem. Portanto, a substituição da resina virgem pela reciclada traz benefícios de redução de custo e aumento da competitividade, além de auxiliar na preservação ambiental.Embora uma grande quantidade de metais, polímeros, borrachas e outros tipos de materiais sejam utilizados na indústria automotiva, sob o ponto de vista da reciclagem, pode-se verificar que os polímeros são os materiais que apresentam propriedades mecânicas inferiores, quando reciclados [1] . A reciclagem é essencial para a reutilização dos recursos aplicados durante a vida útil de um automóvel e existe um crescente interesse da indústria automotiva nas atividades relacionadas à proteção ambiental.Dentre as peças automotivas, os pára-choques são relativamente fáceis de serem reciclados devido às suas dimensões e por constituírem, geralmente, de um ...
Tuning a complete image processing chain (IPC) is not a straightforward task. The first problem to overcome is the evaluation of the whole process. Until now researchers have focused on the evaluation of single algorithms based on a small number of test images and ad hoc tuning independent of input data. In this paper, we explain how the design of experiments applied on a large image database enables statistical modeling for IPC significant parameter identification. The second problem is then considered: how can we find the relevant tuning and continuously adapt image processing to input data? After the tuning of the IPC on a typical subset of the image database using numerical optimization, we develop an adaptive IPC based on a neural network working on input image descriptors. By testing this approach on an IPC dedicated-to-road obstacle detection, we demonstrate that this experimental methodology and software architecture can ensure continuous efficiency. The reason is simple: the IPC is globally optimized, from a large number of real images and with adaptive processing of input data.
O sucesso escolar dos alunos de etnia cigana: desafios emergentes. O caso dos alunos do Agrupamento de Escolas Infante D. Henrique.
Tuning a complete image processing chain (IPC) remains a tricky step. Until now researchers focused on the evaluation of single algorithms, based on a small number of test images and ad hoc tuning independent of input data. In this paper we explain how, by combining statistical modeling with design of experiments, numerical optimization and neural learning, it is possible to elaborate a powerful and adaptive IPC. To succeed, it is necessary to build a large image database, to describe input images and finally to evaluate the IPC output. By testing this approach on an IPC dedicated to road obstacle detection, we demonstrate that this experimental methodology and software architecture ensure a steady efficiency. The reason is simple: the IPC is globally optimized, from a large number of real images (180 out of a sequence of 30 000) and with adaptive processing of input data
This paper comments on Sokal and Bricmont's stance towards interdisciplinarity approaches between the human and natural sciences, as stated in their controversial book de "possível" e "potencial" (Atlan, 1991) no modelo de risco.
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