Fuzzy Logic Techniques Research Articles

Prediction of formability of MIG and TIG welded joints by Erichsen cupping test and fuzzy-logic technique

The current investigation was carried out to evaluate the formability characteristics of tungsten inert gas (TIG) and metal inert gas (MIG) welded parts by conducting Erichsen tests. It is noted that in both the MIG and TIG welded parts, the fracture occurred irrespective of the sheet metal thickness. However, the formability of TIG welds was comparatively better than the MIG weld parts. Further, fuzzy-logic method was adopted to predict the Erichsen index value and compared with the experimental values. It was observed that fuzzy-logic predicted values were nearly accurate to the experimental ones, with a minor error of 1.4%. Fuzzy-logic technique is found to be a useful technique to predict the response and in saving time and costly trials on the shop floor, thus beneficial for industrial applications.

Survey for Dependent on Neuro Fuzzy Algorithm for Framework ID

This article focuses on hybrid neuro-fuzzy techniques, its advantages and disadvantages. The techniques of fuzzy logic and neural networks are commonly used for artificial intelligence. At the point when they are utilized in a joined manner, they are called Neuro-Fuzzy Systems. In the Neuro-Fuzzy Systems fuzzy sets and fuzzy rules are adjusted using input output patterns. The Neuro-fuzzy systems are implemented by different authors in different models. The Neuro-Fuzzy frameworks have the accompanying attributes: quick learning, versatility on-line, to modify itself with the point of getting the little worldwide blunder conceivable and little computational multifaceted nature. The obtaining of information, the pre-handling of information and preparing information are additionally significant for the achievement of the use of the neuro-fuzzy models

Mapping tree species distribution in support of China's integrated tree-livestock-crop system

The primary challenge of the contemporary world is to meet accelerating requirements for food. Limited land, competition between crop and livestock farming and climate change are major challenges. Agroforestry offer a form of sustainable agriculture through the direct provision of food by raising farmers’ incomes and through various ecosystem services. The first essential step in adopting agroforestry is the selection of appropriate tree species that fit local climates. In this paper, we mapped 20 fodder trees and important crops in China using the multi-model ensemble and Ecocrop modelling approach. Relying on the intersectional concept of set theory, the fuzzy logic technique was applied to identify regions where candidate trees could be grown with appropriate crops and livestock. The resulting models provide important insights into the climatic suitability of trees and crops and offer knowledge critical to the proper integration of trees with crops and livestock at specific locations. The results offer support for developing appropriate strategies regarding potential land-use within agroforestry systems in order to maximize ecosystem services and the benefits of sustainable agriculture. Model outputs could easily convert into conventional maps with clearly defined boundaries for site-specific planning for tree-crop-livestock integration. The next step for actualizing an integrated system is to investigate specifically what these different species may contribute to the existing farming systems, quantify the benefits and estimate any possible trade-offs.

Enhancement of Power Quality in a Grid Integrated WECS with Shunt Voltage Controller

This paper deals with study of wind energy conversion system when it is interconnected with the grid. Main objective of the paper is to improve the quality of the power when wind energy system is linked with the non-linear loads. In this work DSTATCOM (FACTS Device) is used as a shunt active controller which used to reduce harmonics, when grid is interconnected with the non-linear load. In this work a new methodology is proposed i.e., Fuzzy Logic technique is integrated in the control circuit of DSTATCOM that helps to compare the reference voltage and output voltage of grid in order to minimize the error and improve power quality

USE OF FUZZY LOGIC TO DEMONSTRATE POSSIBILITY OF HEALTH SYSTEM COLLAPSE DUE TO CORONAVIRUS PANDEMIC / USO DA LÓGICA FUZZY PARA DEMONSTRAR A POSSIBILIDADE DE COLAPSO DO SISTEMA DE SAÚDE DEVIDO À PANDEMIA DE CORONAVÍRUS

Introduction: At the end of 2019, contamination caused by an unknown virus affected the population of Wuhan, China. The virus, known as SARS-CoV-2, was found to cause flu-like symptoms, such as fever, cough, among others. Objective: using the fuzzy logic method graphically demonstrated the Risk of Collapse of the Health System, with the availability of beds /equipment to increase the number of infected, from the Sars-CoV-2 pandemic. Methods: Modeled a fuzzy system with a Mamdani model, later defined the physical variables of entry (Number of infected people and number of beds/equipment available) and output (Risk of collapse of the health system), then inserted the nine rules and the pertinence functions in the trapezoidal and triangular type graph, generating from this information a three-dimensional graph with the results. Results: From the three-dimensional graph, it can be seen that the results were consistent and demonstrate that countries must prevent the number of infected people from increasing, because when this situation occurs, the risk of collapse of the health system is high due to the unavailability of beds /equipment, in addition, this modeling of fuzzy logic along with the generated three-dimensional graph can be used to demonstrate a health crisis in countries. Conclusion: Concluded that the fuzzy logic technique allows many realistic predictions of possible crises and collapses of the health system, but to obtain good results in it, having the knowledge of what is expected will generate the necessary notion to make the insertion of information accurately in the software.

Design and Modeling of Hybrid DC/AC Microgrid With Manifold Renewable Energy Sources

The increased demand and depletion of the fossil fuels for power generation led to the need for extracting power from the renewable energy resources (RERs). The microgrids (MGs) are designed with the help of effective power extracted from renewable sources such as solar, wind, tidal, and geothermal. The advent of DC MGs overcomes the conventional AC grids. The hybridization of the AC and DC MGs will provide more advantages for various levels of consumers. This article proposes the design and modeling of a hybrid DC/AC MG with the efficient use of RERs and it can reduce numerous power conversions. The solar energy is extracted through photovoltaic (PV) panels meritoriously using interval type 2 fuzzy logic technique as the maximum power point tracking algorithm. The AC grid is designed using wind energy source and tidal energy. The permanent magnet synchronous generator is used as the wind turbine. Various control mechanisms are employed in order to extract maximum power from the wind and tidal waves at varying conditions. These generated powers can supply the load and are connected to the utility grid. These are executed with the aid of MATLAB/SIMULINK software.

Prediction of formability of MIG and TIG welded joints by Erichsen cupping test and fuzzy-logic technique

The current investigation was carried out to evaluate the formability characteristics of tungsten inert gas (TIG) and metal inert gas (MIG) welded parts by conducting Erichsen tests. It is noted that in both the MIG and TIG welded parts, the fracture occurred irrespective of the sheet metal thickness. However, the formability of TIG welds was comparatively better than the MIG weld parts. Further, fuzzy-logic method was adopted to predict the Erichsen index value and compared with the experimental values. It was observed that fuzzy-logic predicted values were nearly accurate to the experimental ones, with a minor error of 1.4%. Fuzzy-logic technique is found to be a useful technique to predict the response and in saving time and costly trials on the shop floor, thus beneficial for industrial applications.

Deliberative architecture for smart sensors in the filtering operation of a water purification plant

The increase of applications for industrial smart sensors is booming, mainly due to the use of distributed automation architectures, industrial evolution and recent technological advances, which guide the industry to a greater degree of automation, integration and globalization. In this research work, an architecture for deliberative-type intelligent industrial sensors is proposed, based on the BDI (Belief Desire Intentions) model, adaptable to the measurement of different variables of the filtering process of a water purification plant. An intelligent sensor with functions of signal digitalization, self-calibration, alarm generation, communication with PLC, user interface for parameter adjustment, and analysis with data extrapolation have been arranged. For decision making, the use of fuzzy logic techniques has been considered, which allows imprecise parameters to be appropriately represented, simplifying decision problem solving in the industrial environment, generating stable and fast systems with low processing requirements. The proposed architecture has been modelled, simulated and validated using UML language in conjunction with Petri nets, which facilitate the representation of discrete system events, presenting them clearly and precisely. In the implementation and testing of the prototype, C/C ++ language has been used in an 8-bit microcontroller, experimentally corroborating the operation of the device, which allowed evaluating the behavior of a pseudo-intelligent agent based on the requirements of the water treatment plant, and also through comparisons with similar works developed by other researchers.

Design of an Efficient RPL Objective Function for Internet of Things Applications

Over the decade, a rapid growth in use of smart devices connected and communicating over the Internet is seen in various domains. The IPv6 Routing protocol for Low power and Lossy Networks (RPL) is the routing backbone of such IOT networks. RPL is a proactive, distance vector protocol which constructs the routes based on an Objective Function. The performance of RPL protocol largely depends on the design of Objective Function. Depending on application requirements, the RPL standard offers flexibility in design of the objective function and scope for improving the routing process. In this paper, an efficient Objective Function, RPL-FZ, is proposed. Speedy communication across nodes, low energy consumption and reliable data delivery is key to achieve quality of Service. Considering this, RPL-FZ uses relevant metrics like Residual Energy of Node, Delay and ETX (Expected Transmission count) to make the routing decisions. The metrics are combined using fuzzy logic technique to obtain a single metric Quality for each neighbor node. The neighbor with highest value of Quality is chosen as best parent to forward sensed data toward the collection unit. The proposed objective function RPL-FZ is integrated in the Contiki OS and network simulations are performed using the COOJA simulator. The performance evaluation reveals that RPL-FZ achieves 7% higher Packet Delivery rate, 8% lower energy consumption and 8% lesser latency as compared to single metric based standard objective functions OF0 and MRHOF.

THE EFFECT OF MICROBIOLOGICAL FACTOR ON THE EVALUATION OF WATER QUALITY USING FUZZY LOGIC

In this study, it was aimed to reveal the importance of microbiological factors in water quality determination studies based on fuzzy logic and to determine the usability of management in water quality. Six critical parameters for water quality detection are included. In the study of Avsar [1], only bacteriological data were emphasized, and no information was obtained about quantitative data on water quality. It was found that fecal coliform was more effective on the changes in water quality compared to fecal coliform and temperature based fuzzy quality indexes. When the fuzzy quality values were determined, the effect of seasonal changes did not appear to be high, but the change in pH and coliform values affected the quality. This showed that water quality assessment would not be accurate without microbiological data. As a result, the proposed data can be considered accurate and reliable, so we recommend that microbiological data should be included in the evaluation of water quality determination studies. In addition, we can say that fuzzy logic technique will be a comprehensive and reliable technique in the assessment of the quality of the streams that are used in irrigation, fishing and recreational areas.

Fuzzy logic modelling of sustainable performance measure (MRR) during WEDM of Ti/6Al/4V alloy

Wire-cut electrical discharge machining (WEDM) has become an indispensable machine tool in modern manufacturing industries. The growing demand for Ti-alloy components produced by WEDM in defence, medial, automotive industries etc necessitate extensive study on performance characteristics of WEDM process. Soft-computing based fuzzy logic (FL) technique is considered as an effective modelling tool for prediction of performance measures. This paper presents development of FL prediction model for material removal rate (MRR) as a sustainable performance measure in WEDM of Ti/6Al/4V alloy. The Mamdani type fuzzy inference system consisting of 4-inputs, 1-output and 180rules was used for developing FL prediction model. Validation of the developed prediction through confirmation experiments showed average percentage error as 5.44%; while the minimum and maximum error was recorded as 1.54% and 7.69% respectively. With 94.56% model accuracy, predictive performance of the FL model was found better than regression model. Study of surface plots of FIS showed that higher levels of pulse-on-time coupled with low levels of pulse-off-time result in increased MRR values. This is attributed to high energy sparks produced due to longer duration for which current is supplied and faster flushing off molten material by di-electric fluid as duration between the two pluses is smaller. The observation is also supported by contour plots obtained by the experimental result.

Modelling of Dross Height in Plasma Jet Cutting Process of Aluminium Alloy 5083 Using Fuzzy Logic Technique

Modelling of Dross Height in Plasma Jet Cutting Process of Aluminium Alloy 5083 Using Fuzzy Logic Technique

A fuzzy logic PI control with feedforward compensation for hydrogen pressure in vehicular fuel cell system

In a vehicular fuel cell system, alternative load and frequent purge action can lead to anode pressure varies with the hydrogen mass flow fluctuation. It's crucial to control the pressure difference between anode and cathode within a reasonable range to avoid adverse phenomena such as membrane failure, reactant starvation, or even water management fault. In this paper, an improved proportional integrative (PI) controller by the fuzzy logic technique that considers the engineer experience and knowledge on the hydrogen supply system behavior is proposed for hydrogen pressure control, in which the PI parameters are tuned by a fuzzy decision process. Furthermore, load current and purge action regarded as input disturbances are applied for feedforward compensation to reduce the pressure response hysteresis. A hydrogen supply subsystem based on the proportional valve is modeled, and corresponding parameters are determined by analyzing the response time and steady pressure fluctuation. The performance of the conventional PI controller, the fuzzy logic PI (FLPI) controller and fuzzy logic PI with feedforward (FLPIF) controller is validated. The presented results indicated that the FLPI controller significantly improves the dynamic response of hydrogen pressure compared to the PI controller, and the FLPIF controller can further reduce overshoot caused by disturbance. Finally, the proposed FLPIF controller is implemented on a rapid prototype platform of the hydrogen supply subsystem and an actual fuel cell system, exhibiting satisfactory performance.

An Adaptive Fast Terminal Sliding Mode Controller of Exercise-Assisted Robotic Arm for Elbow Joint Rehabilitation Featuring Pneumatic Artificial Muscle Actuator

Due to the time-varying nonlinear dynamic, uncertain model and hysteresis characteristics of the pneumatic artificial muscle (PAM) actuator, it is not easy to apply model-based control algorithms for monitoring, as well as controlling, the operation of systems driven by PAM actuators. Hence, the main aim of this work is to propose an intelligent controller named adaptive sliding controller adding compensator (ASC + C) to operate a robotic arm, featuring a pneumatic artificial muscle actuator, which assists rehabilitation exercise of the elbow joint function. The structure of the proposed controller is a combination between the fuzzy logic technique and Proportional Integral Derivative (PID) algorithm. In which, the input of fuzzy logic controller is the sliding surface, meanwhile, its output is the estimated value of the unknown nonlinear function, meaning that the model-based requirement is released. A PID controller works as a compensator with online learning ability and is designed to compensate because of the approximate error and hysteresis characteristic. Additionally, to improve convergence and to obtain stability, a fast terminal sliding manifold is introduced and online learning laws for parameters of the controller are attainted through the stable criterion of Lyapunov. Finally, an experimental apparatus is also fabricated to evaluate control response of the system. The experimental result confirmed strongly the ability of the proposed controller, which indicates that the ASC + C can obtain a steady state tracking error less than 5 degrees and a position response without overshoot.

Path Planning for Excavator Arm: Fuzzy Logic Control Approach

This paper proposes an algorithm to design the path for automated excavator arm. The path designing is divided into two phases. Firstly, the fuzzy logic technique is used to determine the change of the path to adapt with the variation of the material after each digging period. Secondly, the B-spline algorithm is used to build the optimal trajectory. By this, the excavator can maximize the dug weight although the shape of the material is variation. The effectiveness of the algorithm is verified through computer simulation and real-time experiment. The simulation and experimental results show that the generated optimal path keeps the dug weight around the expected value regardless of the change of the environment.

Development of a fuzzy model for differentiating peanut plant from broadleaf weeds using image features

A combination of decision tree (DT) and fuzzy logic techniques was used to develop a fuzzy model for differentiating peanut plant from weeds. Color features and wavelet-based texture features were extracted from images of peanut plant and its three common weeds. Two feature selection techniques namely Principal Component Analysis (PCA) and Correlation-based Feature Selection (CFS) were applied on input dataset and three Decision Trees (DTs) including J48, Random Tree (RT), and Reduced Error Pruning (REP) were used to distinguish between different plants. In all cases, the best overall classification accuracies were achieved when CFS-selected features were used as input data. The obtained accuracies of J48-CFS, REP-CFS, and RT-CFS trees for classification of the four plant categories namely peanut plant, Velvetleaf, False daisy, and Nicandra, were 80.83%, 80.00% and 79.17% respectively. Along with these almost low accuracies, the structures of the decision trees were complex making them unsuitable for developing a fuzzy inference system. The classifiers were also used for differentiating peanut plant from the group of weeds. The overall accuracies on training and testing datasets were respectively 95.56% and 93.75% for J48-CFS; 92.78% and 91.67% for REP-CFS; and 93.33% and 92.59% for RT-CFS DTs. The results showed that the J48-CFS and REP-CFS were the most appropriate models to set the membership functions and rules of the fuzzy classifier system. Based on the results, it can be concluded that the developed DT-based fuzzy logic model can be used effectively to discriminate weeds from peanut plant in the form of machine vision-based cultivating systems.

Fuzzy logic techniques and GIS-based delineation of groundwater potential zones: a case study of Anger river basin, Ethiopia

As the demand for drinking water worldwide increases for human consumption, agriculture and industrial uses, the need to assess the potential of groundwater and aquifer productivity also increases. Access to water supply in Ethiopia is among the lowest in the world. In some regions of Ethiopia, people make three–five round trips to get dirty water from the river per day. Each roundtrip takes 2–3 h and water is carried in around “50-lb jerrycans” according to an article by Tina Rosenberg for National Geographic. Therefore, delineation of groundwater potential in Ethiopia is a need of an hour. The objective of this study is to develop a spatial model using a fuzzy logic approach integrated with geographic information system (GIS) domain to demarcate the groundwater potential zones (GPZ) of Anger river basin Ethiopia. Nine thematic layers such as geology, slope gradient, drainage density, Roughness, profile curvature, plan curvature, wetness index, soil texture, soil type, and land use were prepared, analyzed and studied for GPZ delineation. The groundwater potential zone map thus obtained was categorized into five classes: very good, good, moderate, low and very low. The study reveals that about 28.44% of the Anger river basin is covered under very good GPZ. Very poor, poor, moderate and good GPZ are observed in 8.06%, 15.47%, 21.36%, and 26.67%, respectively. The area under very poor and poor potential zones is recorded only in very limited areas in the basin. This study proved the efficiency of the fuzzy logic approach coupled with GIS an efficient model for demarcation of GPZ and can be applied at a continental scale.

Limited Power Point Tracking for a Small-Scale Wind Turbine Intended to Be Integrated in a DC Microgrid

Limited power point tracking (LPPT) is emerging as a new technology for power management controllers for small-scale wind turbines (SSWTs) thanks to its advantages in terms of operation flexibility, economy and system security. LPPT operates in such a way that power requested by the user can be extracted from the wind turbine while respecting constraints. However, operating in LPPT mode still requires a deep understanding to obtain a compromise between minimizing power oscillations and transient response. For that, three LPPT power control strategies for an SSWT intended to be integrated in a direct current (DC) urban microgrid are investigated. These methods concern perturb and observe (P&O) with fixed step size, P&O based on Newton’s method and P&O based on the fuzzy logic (FL) technique. The experimental results highlight that all methods function correctly and reach the limited power point (LPP). The FL method improves dynamic performances with more steady oscillations around LPP compared to fixed step size and Newton’s methods. The sudden variation of wind velocity and power lead us to conclude that the FL method ensures a good balance between reducing oscillation of wind turbine (WT) output power around the operating point and convergence of rising time toward LPP.

Effects of primary and secondary metallization techniques on the performance of electric discharge machining (EDM) electrode produced by additive manufacturing and composite coating

In the present research study on electric discharge machining (EDM) tool is developed using additive manufacturing with different primary and secondary coatings. Often conductive silver painting or electroless nickel coating are used for primary metallization and copper electroplating as subsequent metallization for rapid EDM tool development. The use of composite electroless nickel coating and electroplating is frequently applied on steels for improving wear, corrosion, and hardness as well as thermal stabilities. However, the use of the composite coating misses in EDM tooling. The rationale behind this investigation is to explore the impact of primary and secondary metallization on EDM tool performance, particularly employing tungsten base composite coatings. Tungsten is used as an alloying element to get the composite coating of nickel tungsten and copper tungsten during primary and secondary metallization respectively. Square shaped tools are 3D printed with digital Acrylonitrile Butadiene Styrene (ABS) materials on polyjet printer employing a full factorial experimental plan. EDM machining is performed with standard settings for an hour, and the results of tool wear rate (TWR), material removal rate (MRR), and surface finish (Ra) are compared to identify the best combination of primary and secondary metallization for the optimum tool performance. The multi-objective optimization of the process is carried out by converting the output responses into a single performance index as a gray fuzzy reasoning grade (GFRG) using the fuzzy logic technique. The multi- objective optimization concludes that the electroless nickel tungsten composite coating with addition of tungsten micropowder as primary coating at a high level of primary coating thickness and copper as secondary coating forms the best combination of parameters for optimum tool performance.

Study of normal tissue dosimetric benefit using asymmetric margin-based biological fuzzy decision making: volumetric modulated arc therapy of prostate cancer

AbstractAim:Radiation therapy has historically used margins for target volume to ensure dosimetric planning criteria. The size of margin for a given treatment site is still uncertain particularly for moving targets along with set-up variations leading to a fuzziness of target volume. In this study, we have estimated the dosimetric benefit of normal structures using biological-based optimal margins. The treatment margins are derived by knowledge-based fuzzy logic technique which is considering the radiotherapy uncertainties in treatment planning.Materials and methods:All treatment plans were performed using stepped increments of asymmetric margins to estimate prostate radiobiological indices such as tumour control probability (TCP) and normal tissue complication probability (NTCP). An absolute NTCP of 5% was considered to be the maximum acceptable value while TCP of 85% was considered to be the minimal acceptable limit for each volumetric modulated arc therapy (VMAT) plan of localised prostate cancer radiotherapy. Results were used to formulate rules and membership functions for Mamdani-type fuzzy inference system (FIS). In implementing the rules for the fuzzy system for ΔNTCP values above 10%, the PTV margin was not permitted to exceed 5 mm to avoid rectal complications due to margin selection. The new margins were applied in VMAT planning of prostate cancer for standard displacement errors. The dosimetric results of normal tissue predictors were estimated such as organ mean doses, rectum V60 (volume receiving 60 Gy), bladder V65 (volume receiving 65 Gy) and other clinically significant dose–volume indicators and compared with VMAT plans using current margin formulations.Results:Dosimetric results compared well to the results obtained by current techniques. Good agreement was obtained between proposed fuzzy model margins and currently used margins in lower error magnitude, but significant results were observed at higher error magnitude when organ toxicity concerned without compromising the target volumes.Findings:The new margins may be helpful to estimate possible outcomes of normal tissue complications and thus may improve complication free survival particularly when organ motion errors are inevitable, case by case.