Browsing by Author "Almetwally, Alsaid Ahmed"

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Impact properties of woven reinforced sandwich composite panels for automotive applications
(SAGE PUBLICATIONS INC., 2013) Aly, Nermin Mohamed; Saad, Mohamed Abdalla; Sherazy, Ehab Haider; Kobesy, Osama Mahrous; Almetwally, Alsaid Ahmed
Fiber-reinforced materials are widely used in many industrial applications including civil engineering, automotives, marine, aviation, etc. This is due to their high strength to weight ratios compared to metal structures. One of the major applications of composites is the structural components for automotives such as bumpers, fenders, hoods, door panels. For such applications, impact strength is required since it is directly related to the passenger safety requirements. Sandwich structures are extensively used in automobiles; the understanding of their behaviors under impact conditions is extremely important for the design and manufacturing of these engineering structures. In this study, 27 types of woven fabrics were produced using polyester fibers as warp threads with different structure parameters such as weft yarn material, picks densities, and weaving structures to be used as skin layers and nonwoven fabric was used as core layer. The sandwich composite specimens were prepared using the said woven fabrics with two types of thermoset resins and fabricated via the hand lay up method. The impact properties of the sandwich specimens produced were evaluated to choose the best samples performance to be used in automotive applications. It was found that the impact properties are strongly affected by woven fabric structure parameters and the resin properties.
Predicting the tensile properties of cotton/spandex core-spun yarns using artificial neural network and linear regression models
(TAYLOR & FRANCIS LTD, 2014) Almetwally, Alsaid Ahmed; Idrees, Hatim M. F.; Hebeish, Ali Ali
Recently, core-spun yarns showed many improved characteristics. The tensile properties of such yarns are accepted as one of the most important parameters for assessment of yarn quality. The tensile properties decide the performance of post-spinning operations; warping, weaving, and knitting, and the properties of the final textile product; hence, its accurate prediction carries much importance in industrial applications. In this study, artificial neural network (ANN) and multiple regression methods for modeling the tensile properties of cotton/spandex core-spun yarns are investigated. Yarn breaking strength, breaking elongation, and work of rupture of the core-spun yarns are studied. The two models were assessed by verifying root mean square error, mean bias error, and coefficient of determination (R-2-value). The results of this study revealed that ANN has better performance in predicting comparing with multiple linear regression.