Computational Materials & Modelling
Maosheng Zheng; Yi Wang; Haipeng Teng
Abstract
Multi object optimization in material selection involves the satisfaction of optimizing the multi attributes simultaneously, which analogically corresponds to the simultaneous appearance of the event of the multi attributes in the viewpoint of probability theory, thus the optimization of multi – ...
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Multi object optimization in material selection involves the satisfaction of optimizing the multi attributes simultaneously, which analogically corresponds to the simultaneous appearance of the event of the multi attributes in the viewpoint of probability theory, thus the optimization of multi – object becomes the assessment of the “joint probability” of these multi – attribute problem. Furthermore, the preferential degree of the candidate material in the material selection is reflected by the concept of preferential probability, and a quantitative approach for evaluating the partial preferential probability of each material attribute indicator and the total (joint) preferential probability of candidate material in the material selection is proposed on basis of probability theory correspondingly. In the approach, all material attribute indicators are divided into beneficial or unbeneficial types; each material attribute indicator of the candidate contributes one partial preferential probability linearly to its authorized material upon its nature of whether beneficial or unbeneficial type merely; the product of all partial preferential probabilities of a candidate makes its total preferential probability, which is the final unique index in the material selection decisively; the candidate materials can be ranked according to their total preferential probabilities, which determines the result of the selection. Furthermore, the condition of discrete input variables and the objects is extended to the case of continuous input variables and the objects. Some examples are given in detail, satisfied results are obtained.
Shikha Rai; A.K. Rai; I.M.L. Das; K.C. Tripathi
Abstract
Our key aim is to validate the use of statistical methods for analysis of Laser-Induced Breakdown Spectroscopy (LIBS) datasets of pure nitro compounds (4-nitroaniline and 4-nitrotoluene) and of test samples formed in Cu matrix. Laser-Induced Breakdown Spectroscopy (LIBS) provides the spectral lines of ...
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Our key aim is to validate the use of statistical methods for analysis of Laser-Induced Breakdown Spectroscopy (LIBS) datasets of pure nitro compounds (4-nitroaniline and 4-nitrotoluene) and of test samples formed in Cu matrix. Laser-Induced Breakdown Spectroscopy (LIBS) provides the spectral lines of the constituent elements. The interest behind this study is to establish the essence behind the supplementation of LIBS analysis with statistical methods. When the energetic materials were doped with the interferents, such as Cu metal powder it leads to the alteration of the spectral profile of both the target samples, which have similar constituent elements such as C, H, N and O. So, for this situation, it is difficult to classify the test samples from their pure samples only on the basis of its spectral signatures. Hence, in order to classify these sets, we have applied sophisticated chemometric techniques such as linear correlation and Principal Components Analysis (PCA) to familiar LIBS datasets and found that 50% test samples of 4-nitroaniline and 70% test samples of 4-nitrotoluene were successfully discriminated. The causes for partial classification for both the samples have also been discussed in detail.
