Abstract:The main aim of this study was to characterize cooked bagasse fi bers from Agave angustifolia Haw. The fi bers were characterized using scanning electron microscopy, differential scanning calorimetry, thermogravimetric analysis, X-ray Diffraction and chemical analysis. The tensile strength was also tested using fi bers with a uniform length (30 mm). The fi bers were light brown in color, with a mean diameter and length of 501 μm and 144 mm, respectively. Scanning electron microscopy images revealed elliptically shaped cells with varying lumen size. Holocellulose content was approximately 82.12 %, and total lignin content was approximately 20.69 %. Due to the crystallinity and lignin content, the fi bers proved to be thermo-stable until 220 °C.The mean values of tensile strength, Young's modulus, % strain (ε), and ultimate tensile strength were determined via mechanical tests. The results are comparable to those of other common lignocellulosic fi bers, confi rming their potential use as a reinforcing element in a polymer matrix to form a new biodegradable composite. Keywords: chemical composition, crystallinity, mechanical tests, morphology, thermal analysis Resumen: El objetivo principal de este estudio fue caracterizar las fi bras de bagazo cocido de Agave angustifolia Haw. Las fi bras fueron caracterizadas a través de microscopia electrónica de barrido, calorimetría diferencial de barrido, análisis termogravimétri-cos, difracción de rayos X y análisis químico. También se realizaron pruebas de resistencia a la tracción usando fi bras de longitud constante (30 mm). Las fi bras presentaron un color marrón claro, con diámetro medio de 501 μm y longitud media de 144 mm. Las imágenes del microscopio electrónico de barrido mostraron células de forma elíptica con diferente tamaño de lumen. El contenido de holocelulosa fue alrededor de 82.12 % y el contenido total de lignina de aproximadamente 20.69 %. La fi bra resultó ser térmicamente estable hasta 220 °C debido a la cristalinidad y el contenido de lignina. El esfuerzo de tensión, el módulo de Young, el porcentaje de deformación (ε) y el esfuerzo último de tensión fueron obtenidos de las pruebas mecánicas. Los resultados son comparables a los de otras fi bras lignocelulósicas comunes, lo cual confi rma que estas fi bras tienen potencial como refuerzo en una matriz polimérica para formar un nuevo compuesto biodegradable. Palabras clave: análisis térmico, composición química, cristalinidad, morfología, pruebas mecánicas. ETHNOBOTANY R ecently, research on plant fi bers has been increasing due to the abundance of these materials and their status as renewable resources (Joseph et al., 1999;Ghali et al., 2006;Lucena et al., 2009;Ku et al., 2011;Kestur et al., 2013). Consequently, this focus has led to further research on the specifi c characterization of individual plant fi bers such as bamboo, okra, sisal, and henequen (Mishra et al., 2004; Bé-akou et al., 2008;De Rosa et al., 2010;Liu et al., 2012;Arrakhiz et al., 2013) and studies on composite materials (Mohan...

A neural network and a genetic algorithm were used in a hybrid method to get the optimal design parameters of an Agave angustifolia Haw. green leaf shredder. First, a prototype of an experimental machine was built using the design parameters recommended by the literature and calculated using linear equations. Then, the shredder prototype was subjected to experiments. The defibration data with different blade adjustments were obtained with experimental values. The data was configured and trained with an artificial neural network to establish a correlation between the defibration quality and the design parameters. The multi-objective optimization method based on genetic algorithms determined the optimal design parameters of the shredder’s functional mechanical elements. The best point was obtained from the least number of broken fibers (2.83%) and the most waste (73.15%). The method used proved suitable to optimize the design parameters; this was based on actual data obtained by experiments performed with the prototype and then modeled through artificial intelligence methods such as neural networks to determine an optimal solution using evolutionary genetic algorithm methods.