Classification Of Raw Materials Research Articles

The Classification of Non-Asbestos Gasket Formulation Using Clustering Method

At present, study on the non-asbestos gasket materials is the hotspot research in static sealing field. The non-asbestos sealing gaskets research and development has made great strides into the practical phase. Formula is an important factor of material, which determines performance of material. In order to obtain well performance, it is needed to optimization formula to get optimal formula that not only improve performance of non-asbestos gasket, but also reduce development time accordingly reduce cost of non-asbestos gasket. Classification of raw materials can be transformed into a mathematical clustering problem. It means that according to some algorithm, there will be some sort of input values of similar links together. Many neural networks were widely used in the classification of different materials. A method of classification by using neural network to the known 15 kinds of the non-asbestos gaskets of formula data was proposed in this paper. By using the PNN (probabilistic neural network), LVQ(Learning Vector Quantization) neural network and SOM (Self-Organizing Feature Map) neural network respectively to classify the non-asbestos gaskets to find a suitable method in the classification of non-asbestos gaskets formula. The results indicated that PNN neural network and LVQ neural network method based on the data that provided in the paper both can effectively classify, while SOM neural network can not classify them ideally, thus it provides a new theoretical basis for the classification of the non-asbestos gaskets.

Raw Material Classification by Means of Hyperspectral Imaging and Hierarchical Temporal Memories

The recently proposed hierarchical temporal memory (HTM) paradigm of soft computing is applied to the detection and classification of foreign materials in a conveyor belt carrying tobacco leaves in a cigarette manufacturing industry. The HTM has been exposed to hyperspectral imaging data from 10 types of unwanted materials intermingled with tobacco leaves. The impact of the HTM architecture and the configuration of internal parameters on its classification performance have been explored. Classification results match or surpass those attained with other methods, such as Artificial Neural Networks (ANNs), with the advantage that HTM are able to handle raw spectral data and no preprocessing, spectral compression, or reflectance correction is required. It is also demonstrated that an optimized configuration of the HTM architecture and internal values can be derived from the statistical properties of the hyperspectral data, allowing the extension of the approach to other classification problems.

The Central Dispensary: A Good Manufacturing Practice Trend

AbstractThe design and the operation of a Central Dispensary, the selection of weighing equipment, the methods of classification of raw materials to ensure material stability and operator safety, and the documentation to ensure quality finished product meeting current Good Manufacturing Practice are discussed.

The composition of animal feeds

AbstractThis paper postulates that, as the competition between animal and human feed is now a cause for socio‐political concern, it is likely that there will be a return to the situation in which the animal feed industry utilises raw materials surplus to, or non‐competitive with, human food. A brief discussion on each of the most important nutrients follows, outlining their importance in animal feeds before explaining the role of “fillers” in animal feeds. Least cost formulation is then explained, before an attempt is made at a raw material classification where the point is made that a dietary formulation depends more upon the specification of the required nutrients than it does on identifying individually selected raw materials. However, materials will be accepted or rejected according to their “economic value”.