Short sisal fiber (SF)-reinforced polypropylene (PP) composites were prepared by melt blending followed by injection molding. To improve the interfacial bonding between SF and PP, the PP matrix was maleated (MAPP) by blending PP and maleic-anhydride-grafted-PP in the weight ratio of 9/1. It was found that the SF/MAPP composites have lower melt viscosity (as reflected by torque rheometer measurements) than the SF/PP composites at the respective sisal fiber contents. In terms of mechanical properties, PP maleation has the effect of improving the tensile strength. This can be explained in terms of the improved SF/matrix interfacial bonding when MAPP was used. However, the impact strength was reduced when the PP matrix was maleated. The improved SF/matrix interfacial bonding prevented fracture mechanisms such fiber/matrix debonding and fiber pullout from taking place.
Affective product design aims at incorporating customers' affective needs into design variables of a new product so as to optimize customers' affective satisfaction. Faced with fierce competition in marketplaces, companies try to determine the settings in order to maximize customers' affective satisfaction with products. To achieve this, a set of customer survey data is required in order to develop a model which relates customers' affective responses to the design variables of a new product. Customer survey data is usually fuzzy since human feeling is usually fuzzy, and the relationship between customers' affective responses and design variables is usually nonlinear. However, previous research on modelling the relationship between affective response and design variables has not addressed the development of explicit models involving either nonlinearity or fuzziness. In this paper, an intelligent fuzzy regression approach is proposed to generate models which represent this nonlinear and fuzzy relationship between affective responses and design variables. In order to do this, K.Y. Chan is the corresponding author. His email address is Kit.Chan@curtin.edu.au.we extend the existing work on fuzzy regression by first utilizing an evolutionary algorithm to construct branches of a tree representing structures of a model where the nonlinearity of the model can be addressed. The fuzzy regression algorithm is then used to determine the fuzzy coefficients of the model. The models thus developed are explicit, and consist of fuzzy, nonlinear terms which relate affective responses to design variables. A case study of affective product design of mobile phones is used to illustrate the proposed method. h refers to the degree to which the fuzzy linear model fits the data sets in developing the fuzzy model.
Keywords: Fuzzy regression, evolutionary algorithm, affective product design
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