Narrow Results

The need of engineered materials with high strength to weight ratio was instrumental for the development of a novel magnesium metal–metal composite with the addition of titanium (reinforcement) and aluminum (alloying element) through disintegrated melt deposition technique. The X-ray diffraction analysis and scanning electron microscopy analysis used to explore the metallurgical insights of the developed magnesium metal–metal composite. Wear tests were carried out with pin-on-disc equipment by varying the input parameters load and sliding velocity over a sliding distance of 2000m. Wear was obtained as the output from the experiments, and the same was analyzed through Pareto analysis of variance, to identify the significant parameters. Also, a fuzzy logic-based model was developed to predict the wear behavior of the metal–metal composite. The wear mechanisms involved in the dry sliding wear behavior were analyzed through worn surface analysis and wear debris analysis.

In this paper, we originally propose a new predicting model to the fashion features of fabrics. It enables to select fabrics satisfying fashion demand according to a small amount of technical parameters which is easy to be measured. For this, we set up three mathematical models. By using fuzzy technique, we first define several fuzzy sets to express measured technical parameters and sensory properties of fabrics. Then, we set up the relational model between the technical parameters and sensory properties by using the rough set method. Next, we set up the relational model between the fashion themes (to express the fashion features of fabrics) and sensory properties by using fuzzy technique. Combine with the two models, we establish the relational model between fashion themes and technical parameters. The proposed model has been validated through a successful real design case.