In real world multi criteria decision making (MCDM) problem, it is tough to solve a decision matrix with vague and imprecise data. The degree of impreciseness depends on the kind of data available. For interval valued data this impreciseness is less and interval-valued MCDM methods can be effectively used to solve the problem. A flexible manufacturing system (FMS) selection problem was taken into consideration to find the best FMS among available alternatives. An interval extension of CODAS method is proposed in this paper which was used to solve the problem along with two other interval-valued decision-making methods i.e. interval-valued TOPSIS, interval-valued EDAS. All the three methods are distance-based approaches and it was found that the interval-valued CODAS method gave the exact same ranking with that of interval-valued TOPSIS and interval-valued EDAS.

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The potential of laser-induced fluorescence (LIF) spectroscopy for the characterization of different stages of dental caries using 404-nm diode laser excitation was investigated. In vitro spectra from 16 sound, 10 noncavitated carious and 10 cavitated carious molar teeth were recorded on a miniature fibre-optic spectrometer. The areas under the receiver operating characteristics (ROC-AUC) were calculated and one-way analysis of variance (ANOVA) was performed. The LIF spectra of the carious teeth showed two peaks at 635 and 680 nm in addition to a broad band seen at 500 nm in sound teeth. The fluorescence intensity ratios, F500/F635 and F500/F680, in carious teeth were always lower than those in sound teeth. The ROC-AUC for discriminating between carious and sound teeth was 0.94, and for discriminating between noncavitated and cavitated carious teeth was 0.87. Statistically significant differences (p<0.001) were seen between sound, noncavitated carious and cavitated carious teeth. The results showed that LIF spectroscopy has the potential to be useful for characterizing different stages of caries in a clinical setting.

Electrical discharge machining (EDM) is a thermo-electrical process that can be conveniently utilized for generating complex shaped profiles on hard-to-machine conductive materials using metallic tool electrodes. In this work, composite tools made of copper-tungsten-boron carbide (Cu-W-B4C) manufactured by powder metallurgy (PM) route are used during machining of titanium alloy (Ti6Al4V). The effect of four input machining parameters viz. current, pulse-on-time, duty cycle and percentage of tungsten and boron carbide on material removal rate (MRR), tool wear rate (TWR) and surface roughness (Ra) is studied. A novel meta-heuristic approach such as simple optimization (SOPT) algorithm has been used for single and multi-objective optimization. The pareto-optimal solutions obtained by SOPT have been ranked by VIKOR method to find out the best suitable optimal solution. Analysis of experimental data suggests vital information for controlling the machining parameters to improve the machining performance.

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