Analysis of the benefit generated by using fuzzy numbers in a TOPSIS model developed for machine tool selection problems

Selection of the appropriate machine tools for a manufacturing company is a very important but at the same time a complex and difficult problem because of the availability of wide-ranging alternatives and similarities among machine tools. In the literature, various machine tool selection procedures are developed. The developed procedures mainly use Multi Criteria Decision Making (MCDM) methods. In the literature, fuzzy MCDM models, in which fuzzy numbers are used instead of crisp values, are proposed to deal with the vagueness and imprecision inherent in the machine tool selection problem. Although, the available studies in the literature developed various fuzzy models, they do not propose any approaches to measure the benefit generated by incorporating fuzziness in their selection models. This paper aims to fill this gap by trying to quantify the level of benefit provided by employing the fuzzy numbers in the MCDM models. Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is used as the MCDM approach to rank the machine tools in this paper. In the paper, by increasing the fuzziness level steadily in the fuzzy numbers, the obtained machine tool rankings are compared with the ranking obtained with the crisp values. The statistical significance of the differences between the ranks is calculated using Spearman's rank-correlation coefficient. It can be observed from the results that as the vagueness and imprecison increases, fuzzy numbers instead of crisp numbers should be used. On the other hand, in sitiuations where there is a low level of fuzziness or the average value of the fuzzy number can be guessed, using crisp numbers will be more than adequate.     

An experimental design approach using TOPSIS method for the selection of computer-integrated manufacturing technologies

The selection of Computer-Integrated Manufacturing (CIM) technologies becomes more complex as the decision makers in the manufacturing organization have to assess a wide range of alternatives based on a set of attributes. Although, a lot of Multi-Attribute Decision-Making (MADM) methods are available to deal with selection applications, this paper aims to explore the applicability of an integrated TOPSIS and DoE method to solve different CIM selection problems in real-time industrial applications. Four CIM selection problems, which include selection of (a) an industrial robot, (b) a rapid prototyping process, (c) a CNC machine tool and (d) plant layout design, are considered in this paper. TOPSIS method and Design of Experiment (DoE) are used together to identify critical selection attributes and their interactions of all these cases by fitting a polynomial to the experimental data in a multiple linear regression analysis. This mathematical model development process involves TOPSIS experiments with the model. The regression meta-model greatly reduced the cost, time and amount of the calculation step in application the TOPSIS model. Application results were validated and shown that they provide good approximations to four decision making problem's results in the literature.     

Subcooled Flow Boiling Over Microstructured Plates In Rectangular Minichannels

Microstructures offer enhancements in boiling heat transfer by increasing bubble departure frequency, active nucleation site density, critical cavity size, and surface area. Integration of microstructures to surfaces alters significant surface parameters such as porosity of the microstructured plates, contact angle, and configuration of microstructures on the surface, which all affect boiling heat transfer. The goal of this study is to investigate boiling heat transfer on different microstructured plates and the effect of various microscale surface morphologies on boiling heat transfer. The microstructured surfaces were formed on aluminum alloy 2024 sheets with the use of a simple and environmentally friendly technique of random mechanical sanding (grits of #36, #60, #400, and #1,000). Distilled water was pumped using a micro gear pump to the rectangular minichannel test section at flow rates of 100, 180, and 290 ml/min, which correspond to mass fluxes of 5.46, 10.58, and 16.15 kg/m².s, respectively. It was observed that surfaces with low grit (grit #36) showed no considerable enhancement, whereas the use of higher grit counts considerably enhanced boiling heat transfer up to a critical grit count. The results were supported by the images from the performed visualization of flow boiling.     

Concurrent learning adaptive control of linear systems with exponentially convergent bounds

Concurrent learning adaptive controllers, which use recorded and current data concurrently for adaptation, are developed for model reference adaptive control of uncertain linear dynamical systems. We show that a verifiable condition on the linear independence of the recorded data is sufficient to guarantee global exponential stability. We use this fact to develop exponentially decaying bounds on the tracking error and weight error, and estimate upper bounds on the control signal. These results allow the development of adaptive controllers that ensure good tracking without relying on high adaptation gains, and can be designed to avoid actuator saturation. Simulations and hardware experiments show improved performance. Copyright © 2012 John Wiley & Sons, Ltd.     

Shaping single-walled metal oxide nanotubes from precursors of controlled curvature

We demonstrate new molecular-level concepts for constructing nanoscopic metal oxide objects. First, the diameters of metal oxide nanotubes are shaped with angstrom-level precision by controlling the shape of nanometer-scale precursors. Second, we measure (at the molecular level) the subtle relationships between precursor shape and structure and final nanotube curvature. Anionic ligands are used to exert fine control over precursor shapes, allowing assembly into nanotubes whose diameters relate directly to the curvatures of the 'shaped' precursors.     

Electrothermal flow on electrodes arrays at physiological conductivities

AC electrothermal (ET) flow is inevitable for microfluidic systems dissipating electric energy in a conducting medium. Therefore, many practical applications of biomicrofluidics are prone to ET flow. Here, a series of observations are reported on ET flow in a microfluidic chamber that houses three electrode pairs. The observations indicate that the variations in liquid conductivity and channel height critically impact the structure and magnitude of the flow field. Observations indicate that after a critical conductivity a global ET flow is present in the chamber, while at lower conductivities a vortex is present at every electrode edge. In addition, no ET flow is observed when the chamber height is kept below a critical value at physiological conductivity (∼1.5 S/m). The experimental observations are compared with the numerical simulations of ET flow. The validity of the assumptions made in the current AC ET flow theory is also discussed in the light of the experimental data. The observations can be critical while designing microfluidic systems that involve power dissipation in conductive fluids.Inspec keywords: bioelectric phenomena, microfluidics, numerical analysis, bioMEMSOther keywords: electrode array, physiological conductivity, AC electrothermal flow, microfluidic system, electric energy, biomicrofluidics, microfluidic chamber, liquid conductivity, flow field structure, flow field magnitude, global ET flow, ET flow numerical simulation, AC ET flow theory, power dissipation, conductive fluid     

Development of a quick credibility scoring decision support system using fuzzy TOPSIS

In this study, a quick credibility scoring decision support system is developed for the banks to determine the credibility of manufacturing firms in Turkey. The proposed decision support system is expected to be used by the banks when they want to determine whether an applicant firm is worth a detailed credit check or not. Using such a quick credit scoring decision model reduces the banks’ workload. The proposed credit scoring model is based on the financial ratios and fuzzy TOPSIS approach. It obtains two separate scores which reflect the attractiveness of manufacturing industries within the overall economy and manufacturing firms’ performance with respect to its competitors belonging to the same industry. These two scores are then used to determine the credibility of applicant manufacturing firms. The developed decision support system is tested with various real cases and satisfactory results are obtained. An application is also provided in the paper for illustrative purposes.     

Finite‐time distributed control with time transformation

In this article, we propose distributed control algorithms for first‐ and second‐order multiagent systems for addressing finite‐time control problem with a priori given, user‐defined finite‐time convergence guarantees. The proposed control frameworks are predicated on a recently developed time transformation approach. Specifically, our contribution is twofold: First, a generalized time transformation function is proposed that converts the user‐defined finite‐time interval to a stretched infinite‐time interval, where one can design a distributed control algorithm on this stretched interval and then transform it back to the original finite‐time interval for achieving a given multiagent system objective. Second, for a specific time transformation function, we analytically establish the robustness properties of the resulting finite‐time distributed control algorithms against vanishing and nonvanishing system uncertainties. By contrast to existing finite‐time approaches, it is shown that the proposed algorithms can preserve a priori given, user‐defined finite‐time convergence regardless of the initial conditions of the multiagent system, the graph topology, and without requiring a knowledge of the upper bounds of the considered class of system uncertainties. Illustrative numerical examples are included to further demonstrate the efficacy of the presented results.     

Detection of tool breakage in milling operations—II. The neural network approach

On-line monitoring of tool cutting conditions and tool breakage is very important for automated factories of the future. In this paper, the time series based tooth period modeling technique (TPMT) is proposed for detecting tool breakage by monitoring a cutting force or torque signal in any direction. TPMT uses the fast a posteriori error sequential technique (FAEST) for on-line modeling of cutting force or torque signals. Tool breakage is detected by evaluating variations of the characteristics of the monitored signal in each tooth period. TPMT was tested on simulated and experimental end milling data. The proposed technique detected tool breakage in all of the test cases without giving any false alarms in the transition cases.