Comprehensive Knowledge-Driven AI System for Air Classification Process

Air classifier devices have a distinct advantage over other systems used to separate materials. They maximize the mill’s capacity and therefore constitute efficient methods of reducing the energy consumption of crushing and grinding operations. Since improvement in their performance is challenging, the development of an efficient modeling system is of great practical significance. The paper introduces a novel, knowledge-based classification (FLClass) system of bulk materials. A wide range of operating parameters are considered in the study: the mean mass and the Sauter mean diameter of the fed material, classifier rotor speed, working air pressure, and test conducting time. The output variables are the Sauter mean diameter and the cut size of the classification product, as well as the performance of the process. The model was successfully validated against experimental data. The maximum relative error between the measured and predicted data is lower than 9%. The presented fuzzy-logic-based approach allows an optimization study of the process to be conducted. For the considered range of input parameters, the highest performance of the classification process is equal to almost 362 g/min. To the best of our knowledge, this paper is the first one available in open literature dealing with the fuzzy logic approach in modeling the air classification process of bulk materials.

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Fuzzy logic approach for investigation of microstructure and mechanical properties of Sn96.5-Ag3.0-Cu0.5 lead free solder alloy

Soldering & Surface Mount Technology ◽

10.1108/ssmt-02-2017-0005 ◽

2017 ◽

Vol 29 (4) ◽

pp. 191-198 ◽

Cited By ~ 6

Author(s):

Muhammad Aamir ◽

Izhar Izhar ◽

Muhammad Waqas ◽

Muhammad Iqbal ◽

Muhammad Imran Hanif ◽

...

Keyword(s):

Mechanical Properties ◽

Fuzzy Logic ◽

Solder Alloy ◽

Thermal Aging ◽

Fuzzy Rule ◽

Rule Base ◽

Content Type ◽

Wide Range ◽

Fuzzy Logic Approach ◽

Free Solder

Purpose This paper aims to develop a fuzzy logic-based algorithm to predict the intermetallic compound (IMC) size and mechanical properties of soldering material, Sn96.5-Ag3.0-Cu0.5 (SAC305) alloy, at different levels of temperature. The reliability of solder joint in materials selection is critical in terms of temperature, mechanical properties and environmental aspects. Owing to a wide range of soldering materials available, the selection space finds a fuzzy characteristic. Design/methodology/approach The developed algorithm takes thermal aging temperature for SAC305 alloy as input and converts it into fuzzy domain. These fuzzified values are then subjected to a fuzzy rule base, where a set of rules determines the IMC size and mechanical properties, such as yield strength (YS) and ultimate tensile strength (UTS) of SAC305 alloy. The algorithm is successfully simulated for various input thermal aging temperatures. To analyze and validate the developed algorithm, an SAC305 lead (Pb)-free solder alloy is developed and thermally aged at 40, 60 and 100°C temperature. Findings The experimental results indicate an average IMCs size of 5.967 (in Pixels), 19.850 N/mm2 YS and 22.740 N/mm2 UTS for SAC305 alloy when thermally aged at an elevated temperature of 140°C. In comparison, the simulation results predicted 5.895 (in Pixels) average IMCs size, 19.875 N/mm2 YS and 22.480 N/mm2 UTS for SAC305 alloy at 140°C thermally aged temperature. Originality/value From the experimental and simulated results, it is evident that the fuzzy-based developed algorithm can be used effectively to predict the IMCs size and mechanical properties of SAC305 at various aging temperatures, for the first time.

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Application of Neural Networks and Fuzzy Logic to the Calibration of the Seven-Hole Probe

Journal of Fluids Engineering ◽

10.1115/1.2819670 ◽

1998 ◽

Vol 120 (1) ◽

pp. 95-101 ◽

Cited By ~ 19

Author(s):

O. K. Rediniotis ◽

G. Chrysanthakopoulos

Keyword(s):

Neural Network ◽

Neural Networks ◽

Fuzzy Logic ◽

Middle Layer ◽

Multi Layer Perceptron ◽

Fuzzy Logic Systems ◽

Calibration Methods ◽

Wide Range ◽

Fuzzy Logic Approach ◽

Logic Approach

The theory and techniques of Artificial Neural Networks (ANN) and Fuzzy Logic Systems (FLS) are applied toward the formulation of accurate and wide-range calibration methods for such flow-diagnostics instruments as multi-hole probes. Besides introducing new calibration techniques, part of the work’s objective is to: (a) apply fuzzy-logic methods to identify systems whose behavior is described in a “crisp” rather than a “linguistic” framework and (b) compare the two approaches, i.e., neural network versus fuzzy logic approach, and their potential as universal approximators. For the ANN approach, several network configurations were tried. A Multi-Layer Perceptron with a 2-node input layer, a 4-node output layer and a 7-node hidden/middle layer, performed the best. For the FLS approach, a system with center average defuzzifier, product-inference rule, singleton fuzzifier, and Gaussian membership functions was employed. The Fuzzy Logic System seemed to outperform the Neural Network/Multi-Layer Perceptron.

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Fuzzy-Logic Approach to Estimate the Passengers’ Preference when Choosing a Bus Line within the Public Transport System

Communications - Scientific letters of the University of Zilina ◽

10.26552/com.c.2021.3.a150-a157 ◽

2021 ◽

Vol 23 (3) ◽

pp. A150-A157

Author(s):

Vitalii Naumov ◽

Baurzhan Zhamanbayev ◽

Dinara Agabekova ◽

Zhumazhan Zhanbirov ◽

Igor Taran

Keyword(s):

Fuzzy Logic ◽

Transport System ◽

Public Transport ◽

Optimization Problems ◽

The Public ◽

Public Transport System ◽

Wide Range ◽

Fuzzy Logic Approach ◽

Logic Approach ◽

The Given

For the developed system of public transport, the passengers, as the customers, have a variety of alternatives when choosing the transport mode or even the route for the given mode of public transport. The estimation of the passengers’ preference is the key task for transportation planners for solving the wide range of optimization problems in the field of public transport. A methodology for estimation of the passengers’ preference when choosing the bus line within a public transport system is developed in this paper. The proposed approach is based on the fuzzy-logic mathematical apparatus and uses the surveys’ data to calculate the membership functions defining the passengers’ preference. The case study of the passengers’ survey, held in Talas (Kazakhstan), is used to illustrate the developed methodology.

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Fluidized Bed Jet Milling Process Optimized for Mass and Particle Size with a Fuzzy Logic Approach

Materials ◽

10.3390/ma13153303 ◽

2020 ◽

Vol 13 (15) ◽

pp. 3303 ◽

Cited By ~ 2

Author(s):

Jaroslaw Krzywanski ◽

Dariusz Urbaniak ◽

Henryk Otwinowski ◽

Tomasz Wylecial ◽

Marcin Sosnowski

Keyword(s):

Fuzzy Logic ◽

Fluidized Bed ◽

Rotational Speed ◽

Milling Process ◽

Operating Conditions ◽

Air Pressure ◽

Practical Significance ◽

Wide Range ◽

The Mean ◽

Jet Milling

The milling process is a complex phenomenon dependent on various technological and material parameters. The development of a fluidized bed jet milling model is of high practical significance, since milling is utilized in many industries, and its complexity is still not sufficiently recognized. Therefore, this research aims to optimize fluidized bed jet milling with the use of fuzzy logic (FL) based approach as one of the primary artificial intelligence (AI) methods. The developed fuzzy logic model (FLMill) of the investigated process allows it to be described as a non-iterative procedure, over a wide range of operating conditions. Working air pressure, rotational speed of the classifier rotor, and time of conducting the test are considered as inputs, while mass and mean Sauter diameter of the product are defined as outputs. Several triangular and constant linguistic terms are used in the developed FLMill model, which was validated against the experimental data. The optimum working air pressure and the test’s conducting time are 500 kPa and 3000 s, respectively. The optimum rotational speed of the classifier is equal to 50 s−1, considering the mass of the grinding product, and 250 s−1 for the mean Sauter diameter of the product. Such operating parameters allow obtaining 243.3 g of grinding product with the mean Sauter diameter of 11 µm. The research proved that the use of fuzzy logic modeling as a computer-based technique of solving mechanical engineering problems allows effective optimization of the fluidized bed jet milling process.

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A Fuzzy Logic Approach to Measure Underweight Among Kindergarten’s Kids

Journal of Computing Research and Innovation ◽

10.24191/jcrinn.v3i3.76 ◽

2018 ◽

Vol 3 (3) ◽

pp. 36-45

Author(s):

Balkiah Moktar ◽

Nur Amalina Aziz ◽

Muhamad Hasbullah Mohd Razali

Keyword(s):

Body Mass Index ◽

Fuzzy Logic ◽

Cardiovascular Risk ◽

Control Function ◽

Output Control ◽

Smooth Control ◽

Wide Range ◽

Fuzzy Logic Approach ◽

Logic Approach ◽

Comparison Of Effectiveness

Nowadays, being underweight during childhood is equally risky as being overweight. Underweight children lead to decrement in their academic performance, a social problem as well as challenging attitudes. Underweight people are likely to be less fit and active, which would also increase their cardiovascular risk and health problem. Their mind also becomes inflexible while their concentration and ability to decide are markedly weak. The purpose of this study is to evaluate the chances of having underweight among the children by using Fuzzy Logic Approach. Besides that, the comparison of effectiveness result of underweight between Body Mass Index (BMI) method and Fuzzy Logic Approach using the Mamdani method will be made. The data on weight and height of the children were collected from 3 kindergartens in Perlis. The result shows that 60 to 70 percent of children having an underweight in the range between 79.1% to 91.2% compare to the BMI method that is only 10 percent of children have underweight. It shows that Mamdani method was very effective compared to BMI Method because of the flexibility from the output control that is a smooth control function despite a wide range of input.

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Measuring flood resilience: a fuzzy logic approach

International Journal of Building Pathology and Adaptation ◽

10.1108/ijbpa-12-2016-0029 ◽

2017 ◽

Vol 35 (5) ◽

pp. 470-487 ◽

Cited By ~ 8

Author(s):

Victor Oluwasina Oladokun ◽

David G. Proverbs ◽

Jessica Lamond

Keyword(s):

Fuzzy Logic ◽

Fuzzy Inference ◽

Business Case ◽

Individual Property ◽

Inference System ◽

Content Type ◽

Wide Range ◽

Resilience Index ◽

Fuzzy Logic Approach ◽

The Individual

Purpose Flood resilience is emerging as a major component of an integrated strategic approach to flood risk management. This approach recognizes that some flooding is inevitable and aligns with the concept of “living with water.” Resilience measurement is a key in making business case for investments in resilient retrofits/adaptations, and could potentially be used to inform the design of new developments in flood prone areas. The literature is, however, sparse on frameworks for measuring flood resilience. The purpose of this paper is to describe the development of a fuzzy logic (FL)-based resilience measuring model, drawing on a synthesis of extant flood resilience and FL literature. Design/methodology/approach An abstraction of the flood resilience system followed by identification and characterization of systems’ variables and parameters were carried out. The resulting model was transformed into a fuzzy inference system (FIS) using three input factors: inherent resilience, supportive facilities (SF) and resident capacity. Findings The resulting FIS generates resilience index for households with a wide range of techno-economic and socio-environmental features. Originality/value It is concluded that the FL-based model provides a veritable tool for the measurement of flood resilience at the level of the individual property, and with the potential to be further developed for larger scale applications, i.e. at the community or regional levels.

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