To address the problem of how to build quantitative evaluation index models that reflect the essential characteristics of reconfigurable manufacturing system (RMS) and rank alternative reconfiguration schemes, which possess both advantages and disadvantages, an evaluation method based on the preference ranking organization method for enrichment evaluation (PROMETHEE) is proposed. Based on a consideration of the reconfiguration of the reconfigurable machine components and manufacturing cells, quantitative models of the key characteristics of an RMS (scalability, convertibility, diagnosability, modularity, integrability, and customization) are established, after which the quantitative models are used as the basis for constructing an RMS evaluation index system. The analytic hierarchy process (AHP) is used to assign the weights for these indices. During the evaluation process, PROMETHEE I is first applied to analyze the advantages and disadvantages of each alternative scheme. Then, PROMETHEE II is adopted to analyze the net advantages of the schemes. Finally, all of the alternative configurations are ranked according to the analysis results above. The workshop of an institute that has both research and production capabilities was used as an example to validate the effectiveness and practicability of the proposed method. The example contains 10 alternative reconfiguration schemes, and each scheme consists of six evaluation indices. The computation result shows that quantitative models of six key RMS characteristics are equipped with the ability of quantitative description of the RMS reconfiguration scheme, which gives intuitive decision-making information combined with PROMETHEE, including advantage and disadvantage between alternative schemes, for a decision-maker to select the satisfactory configuration. In addition, only a 7.2 % data loss during the evaluation data processing means the rationality of the selected evaluation index and evaluation algorithm.
Improvement in detection accuracy is an important and hot topic for laser induced breakdown spectroscopy (LIBS). Discharged-pulse assisted (DPA) plasma has been investigated as an effective way to enhance analytical capabilities and accuracy of LIBS. Most of reported DPA experiments have been performed using high voltage and power to comprehend spectrum enhancement. For safety concerns and maneuverability of LIBS equipment; low power and small current discharge are viable for industrial application. In this paper, the enhanced spectra with many extra peaks and higher line intensities were also detected, realized by a low-power discharge assisted LIBS (Max. 2.8 kV and ~1 mA), which are much lower than reported in literature ~MW discharge. The number of atomic peaks of the sample increases, on the other hand, and gradual peaks become stronger with the increase of discharged HV from 1 kV to 1.5 kV, 1.75 kV, 2 kV, 2.5 kV and 2.8 kV. The discharge current increases from 0.2 mA to 1.5 mA, which is almost threshold discharge voltage. After processing, the original spectra, including the peak shift and peak correction by statistics and physics, resulted in achievement of better line stability in terms of relative standard deviation (RSD) of ash, carbon, and volatile coal samples with root mean square error prediction (RMSEP) of 0.4864, 0.3682, 0.3374 and the linear regression coefficient R = 0.99, 0.99,0.98, respectively. The result proposes a promising method to improve detection accuracy of LIBS with simple setup, high safety and low-cost.
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