At present, traditional 3D modeling programs consist of a set of tools that reflect conventional means of mechanical manufacturing and have limitations in relation with the current manufacturing capacities. On the other hand, organic and morphing 3D modeling programs are designed to transform a model from one known shape to another also known shape. Generative design helps the designers to detach themselves during the design process and can provide them with completely unexpected geometrical solutions. In this paper, starting from 3D morphing techniques and genetic algorithms, a new methodology of product shape definition is developed, capable of imitating processes that occur in nature and aimed at creating new and different product designs. This methodology enables to overcome the limitations imposed by design fixation and allows better exploitation of the great possibilities granted by the new manufacturing techniques, most notably additive manufacturing. The initial process of research and information gathering gives this work a solid basis to develop the new methodology. The results of this initial process are briefly resumed in this paper in order to explain the main motivation for developing this work. The workflow of this methodology is presented as a theoretical process, since its implementation has not been, at least for the moment, put into practice. Before presenting the conclusion for this proposal, several examples have been formulated in order to help the reader to catch the point of the entire process.
A medida que se avanza en el desarrollo de la ciudad, aumenta el número de vehículos, accidentes y congestión proporcionalmente. Un sistema de tráfico vehicular se comporta como un sistema a eventos discretos; y debido a las variaciones que influyen en la congestión, su modelo y control se convierten en una tarea compleja. Las Redes de Petri (Petri Nets) son una de las herramientas poderosas para el modelamiento de sistemas de eventos discretos de manera gráfica y matemática. En algunos sistemas existe poca información, datos inexactos y/o cambios permanentes en el modelo del sistema. Esto ha llevado a las técnicas de modelado a trascender a técnicas de adaptación y representación del conocimiento humano mediaste sistemas computacionales bio-inspirados, como las Redes Neuronales (Neural Networks) y la Lógica Fuzzy (Fuzzy Logic). Dichas técnicas son estructuradas en este trabajo como el modelado aproximado mediante el aprendizaje de un sistema concurrente discreto, bajo las redes de Petri Difusas para la representación del conocimiento mediante reglas de inferencia y las Adaptativas para la reacción ante un entorno caótico como un sistema de tráfico vehicular.
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