scholarly journals Modeling and optimization of EDM machined AZ-91 Mg alloy using ANFIS-VIKOR method

2021 ◽  
Author(s):  
Jitendra Kumar ◽  
◽  
Shailesh Gautam ◽  
S. K. Rajput ◽  
Tarun Soota ◽  
...  
Keyword(s):  
Fuzzy Inference ◽  
Tool Electrode ◽  
Positive Polarity ◽  
Vikor Method ◽  
Inference System ◽  
Single Output ◽  
Factor Combination ◽  
Relative Errors ◽  
Pulse On Time ◽  
Pulse Off Time

In this paper, machining of AZ-91 magnesium alloy was performed in EDM using different tool electrode (Cu, CuW and graphite). To perform experiments, Taguchi L18 design of experiments was used to reduce experimental runs. EDM process parameters viz. polarity, current (Ip), pulse-on-time (Ton), pulse-off-time (Toff), and tool electrode material were considered in experimental design to measure the responses (MRR, Ra). Multi-input-single-output adaptive neuro fuzzy inference system (ANFIS) model was developed to predict responses, and predicted results were found in good agreement with the experimental results. Maximum MRR (0.089 g/min) was found at positive polarity, Ip-5 A, Ton-50 μs, Toff - 30 μs, and Cu tool, and minimum Ra (0.08 μm) was at parameters positive polarity, Ip-4 A, Ton-30 μs, Toff -20 μs, and Cu tool. Relative errors between experiential results and ANFIS predicted results were 1.17 % for MRR, and 2.20 % for Ra. Multi response optimization ANFIS-VIKOR method was successfully developed and gave compromise solution for MRR and Ra corresponding to minimum ANFIS-VIKOR index (Qi). A factor level analysis was performed to evaluate optimal factor combination for ANFIS-VIKOR index, and it shows that current (Ip) have a significant effect.

scholarly journals Verification of Fuzzy Inference System for Cutting Speed while WEDM for the Abrasion-Resistant Steel Creusabro by Conventional Statistical Methods

Metals ◽  
2020 ◽  
Vol 10 (1) ◽  
pp. 92 ◽  
Author(s):  
Katerina Mouralova ◽  
Pavel Hrabec ◽  
Libor Benes ◽  
Jan Otoupalik ◽  
Josef Bednar ◽  
...  
Keyword(s):  
Discharge Current ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Cutting Speed ◽  
Resistant Steel ◽  
Inference System ◽  
Pulse On Time ◽  
Pulse Off Time ◽  
Off Time ◽  
Wire Feed

Wire electrical discharge machining is an unconventional machining method for the production of complex-shaped and very precise parts. Because of the high energy consumption of this machining process, it is necessary to maximize the cutting speed for its appropriate implementation. The abrasion-resistant steel Creusabro 4800 was chosen as the test material for this experiment, which is widely used especially for machines working in mines and quarries. In order to maximize the cutting speed, a fuzzy inference system (FIS) has been built, which uses 18 expert propositions to “model” the cutting speed based on five selected input parameters: gap voltage, pulse on time, pulse off time, discharge current, and wire feed. The obtained results were further verified by a design of experiment consisting of 33 tests for five selected input factors. Using the fuzzy inference system, the optimum machine parameters setup was found to maximize the cutting speed, in which the gap voltage = 60 V, pulse on time = 10 µs, pulse off time = 30 µs, wire feed = 10 m∙min−1 and discharge current = 35 A. The predicted value of the cutting speed using the fuzzy inference system is 6.471 mm∙min−1.

scholarly journals Ranking patients on the kidney transplant waiting list based on fuzzy inference system

2022 ◽  
Vol 23 (1) ◽  
Author(s):  
Nasrin Taherkhani ◽  
Mohammad Mehdi Sepehri ◽  
Roghaye Khasha ◽  
Shadi Shafaghi
Keyword(s):  
Kidney Transplantation ◽  
Fuzzy Inference System ◽  
Scoring System ◽  
Fuzzy Inference ◽  
Waiting List ◽  
Analytic Hierarchy ◽  
Inference System ◽  
Kidney Allocation ◽  
Single Output ◽  
Stage Renal Disease

Abstract Background Kidney transplantation is the best treatment for people with End-Stage Renal Disease (ESRD). Kidney allocation is the most important challenge in kidney transplantation process. In this study, a Fuzzy Inference System (FIS) was developed to rank the patients based on kidney allocation factors. The main objective was to develop an expert system, which would mimic the expert intuitive thinking and decision-making process in the face of the complexity of kidney allocation. Methods In the first stage, kidney allocation factors were identified. Next, Intuitionistic Fuzzy Analytic Hierarchy Process (IF-AHP) has been used to weigh them. The purpose of this stage is to develop a point scoring system for kidney allocation. Fuzzy if-then rules were extracted from the United Network for Organ Sharing (UNOS) dataset by constructing the decision tree, in the second stage. Then, a Multi-Input Single-Output (MISO) Mamdani fuzzy inference system was developed for ranking the patients on the waiting list. Results To evaluate the performance of the developed Fuzzy Inference System for Kidney Allocation (FISKA), it was compared with a point scoring system and a filtering system as two common approaches for kidney allocation. The results indicated that FISKA is more acceptable to the experts than the mentioned common methods. Conclusion Given the scarcity of donated kidneys and the importance of optimal use of existing kidneys, FISKA can be very useful for improving kidney allocation systems. Countries that decide to change or improve the kidney allocation system can simply use the proposed model. Furthermore, this model is applicable to other organs, including lung, liver, and heart.

Parametric investigation of tool wear rate in EDM of Fe-based shape memory alloy: microstructural analysis and optimization using genetic algorithm

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Ranjit Singh ◽  
Ravi Pratap Singh ◽  
Rajeev Trehan
Keyword(s):  
Tool Wear ◽  
Tool Electrode ◽  
Tool Wear Rate ◽  
Sem Images ◽  
Peak Current ◽  
Content Type ◽  
Desirability Approach ◽  
Pulse On Time ◽  
Pulse Off Time ◽  
Off Time

Purpose This study aims to experimentally investigate the influence of considered process parameters, i.e. pulse on time, pulse off time, peak current and gap voltage, on tool wear rate (TWR) in electrical discharge machining (EDM) of iron (Fe)-based shape memory alloy (SMA) through designed experiments. The parametric optimization for TWR has also been attempted using the desirability approach and genetic algorithm (GA). Design/methodology/approach The response surface methodology (RSM) in the form of Box–Behnken design has been used to scheme out the experiments. The influence of considered process inputs has also been observed through variance analysis. The reliability and fitness of the developed mathematical model have been established with test results. Microstructure analysis of machined samples has also been evaluated and analyzed using a scanning electron microscope (SEM). SEM images revealed the surface characteristics such as micro-cracks, craters and voids on the tool electrode surface. SEM images provide information about the surface integrity and type of wear on the surface of the tool electrode. Findings The input parameters, namely, pulse on time and pulse off time, are major influential factors impacting the TWR. High TWR has been reported at large pulse on time and small pulse off time conditions whereas higher TWR is reported at high peak current input settings. The maximum and minimum TWR values obtained are 0.073 g/min and 0.017 g/min, respectively. The optimization with desirability approach and GA reveals the best parametric values for TWR i.e. 0.01581 g/min and 0.00875 g/min at parametric combination as pulse on time = 60.83 µs, pulse off time = 112.16 µs, peak current = 18.64 A and gap voltage = 59.55 V, and pulse on time = 60 µs, pulse off time = 120 µs, peak current = 12 A and gap voltage = 40 V, correspondingly. Research limitations/implications Proposed work has no limitations. Originality/value SMAs have been well known for their superior and excellent properties, which make them an eligible candidate of paramount importance in real-life industrial applications such as orthopedic implants, actuators, micro tools, stents, coupling, sealing elements, aerospace components, defense instruments, manufacturing elements and bio-medical appliances. However, its effective and productive processing is still a challenge. Tool wear study while processing of SMAs in EDM process is an area which has been less investigated and of major concern for exploring the various properties of the tool and wear in it. Also, the developed mathematical model for TWR through the RSM approach will be helpful in industrial revelation.

scholarly journals Developing an ANFIS-PSO Model to Predict Mercury Emissions in Combustion Flue Gases

Mathematics ◽  
2019 ◽  
Vol 7 (10) ◽  
pp. 965 ◽  
Author(s):  
Shahaboddin Shamshirband ◽  
Masoud Hadipoor ◽  
Alireza Baghban ◽  
Amir Mosavi ◽  
Jozsef Bukor ◽  
...  
Keyword(s):  
Power Plants ◽  
Fuzzy Inference ◽  
Pollution Assessment ◽  
Mercury Content ◽  
Operational Parameters ◽  
Inference System ◽  
Mercury Emissions ◽  
Power Stations ◽  
Predicted Values ◽  
Relative Errors

Accurate prediction of mercury content emitted from fossil-fueled power stations is of the utmost importance for environmental pollution assessment and hazard mitigation. In this paper, mercury content in the output gas of power stations’ boilers was predicted using an adaptive neuro-fuzzy inference system (ANFIS) method integrated with particle swarm optimization (PSO). The input parameters of the model included coal characteristics and the operational parameters of the boilers. The dataset was collected from 82 sample points in power plants and employed to educate and examine the proposed model. To evaluate the performance of the proposed hybrid model of the ANFIS-PSO, the statistical meter of MARE% was implemented, which resulted in 0.003266 and 0.013272 for training and testing, respectively. Furthermore, relative errors between the acquired data and predicted values were between −0.25% and 0.1%, which confirm the accuracy of the model to deal non-linearity and represent the dependency of flue gas mercury content into the specifications of coal and the boiler type.

scholarly journals Developing an ANFIS-PSO Model to Estimate Mercury Emission in Combustion Flue Gases

2019 ◽  
Author(s):  
Shahaboddin Shamshirband ◽  
Alireza Baghban ◽  
Masoud Hadipoor ◽  
Amir Mosavi
Keyword(s):  
Power Plants ◽  
Fuzzy Inference ◽  
Pollution Assessment ◽  
Mercury Content ◽  
Operational Parameters ◽  
Inference System ◽  
Power Stations ◽  
Neuro Fuzzy ◽  
Predicted Values ◽  
Relative Errors

Accurate prediction of mercury content emitted from fossil-fueled power stations is of utmost important for environmental pollution assessment and hazard mitigation. In this paper, mercury content in the output gas of power stations’ boilers was predicted using adaptive neuro-fuzzy inference system (ANFIS) method integrated with particle swarm optimization (PSO). The input parameters of the model include coal characteristics and the operational parameters of the boilers. The dataset has been collected from 82 power plants and employed to educate and examine the proposed model. To evaluate the performance of the proposed ANFIS-PSO model the statistical meter of MARE% was implemented, which resulted 0.003266 and 0.013272 for training and testing respectively. Furthermore, relative errors between acquired data and predicted values were between -0.25% and 0.1%, which confirm the accuracy of the model to deal nonlinearity and representing the dependency of flue gas mercury content into the specifications of coal and the boiler type.

scholarly journals Developing an ANFIS-PSO Based Model to Estimate Mercury Emission in Combustion Flue Gases

2019 ◽  
Author(s):  
Shahab Shahab ◽  
Alireza Baghban ◽  
Masoud Hadipoor
Keyword(s):  
Flue Gas ◽  
Power Plants ◽  
Fuzzy Inference ◽  
Mercury Content ◽  
Anfis Model ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Operational Information ◽  
Predicted Values ◽  
Relative Errors

Mercury content in the output gas from boilers was predicted using an Adaptive Neuro-Fuzzy Inference System (ANFIS). Input parameters were selected from coal characteristics and the operational configuration of boilers. The ANFIS approach is capable of developing a nonlinear model to represent the dependency of flue gas mercury content into the specifications of coal and also the boiler type. In this study, operational information from 82 power plants has been gathered and employed to educate and examine the proposed ANFIS model. Resulted values from the model were compared to the collected data and it indicates that the model possesses an extraordinary level of precision with a correlation coefficient of unity. The MARE% for training and testing parts were 0.003266 and 0.013272, respectively. Furthermore, relative errors between acquired data and predicted values were between -0.25% and 0.1% which confirm the accuracy of PSO-ANFIS model.

Improve the Surface Characteristics of the Electric Discharge Machining Employing a Method Burnishing Process

2020 ◽  
Vol 38 (4A) ◽  
pp. 545-551
Author(s):  
Shukry Aghdeab ◽  
Ahmed G. Abdulameer ◽  
Ahmed B. Abdulwahhab ◽  
Majid H. Ismiel
Keyword(s):  
Electrical Discharge Machining ◽  
Removal Rate ◽  
Surface Characteristics ◽  
Tool Electrode ◽  
Internal Factors ◽  
Burnishing Process ◽  
Time Optimal ◽  
Pulse On Time ◽  
Pulse Off Time ◽  
Off Time

Electrical Discharge Machining (EDM) applies the concept of material eradication by utilizing electric spark erosion. The target of this exploration concentrates to examine the ideal procedure parameters of EDM on Aluminum 6061-T6as a workpiece with copper as a tool electrode. The effect of various process operators 'on machining rendering was examined. Internal factors with current (10, 20, 30) Ampere,  pulse on time (50, 100, 150) µs was used after which takes pulse off time (25, 50, 75) µs. All parameters applied for empirical acts with influence on Ra (surface roughness ).  The result showed that MRR" Material Removal Rate” is increment by expanding in current and pulse on time and it declines by expanding in pulse off time. Optimal condition are gained when using " Using current 30 Ampere, pulse on time is 150 µs and minimize assessment of pulse off time is 25 µs.

Impact Assessment of Input Factors on Tool Electrode Wear in Die Sinking EDM of Steel EN43CrMo4

2017 ◽  
Vol 756 ◽  
pp. 127-135
Author(s):  
Slavomíra Hašová ◽  
Ľuboslav Straka ◽  
František Špalek
Keyword(s):  
Electrode Material ◽  
Graphite Electrode ◽  
Tool Electrode ◽  
Electrode Wear ◽  
Workpiece Material ◽  
Relative Wear ◽  
Pulse On Time ◽  
Pulse Off Time ◽  
Off Time ◽  
Time Pulse

The paper deals with the wear of tool electrodes for Die Sinking EDM. In the experiment were used the electrodes of copper and graphite. The workpiece material was steel EN43CrMo4. For the assessment the effects of each parameter was used the statistical method Design of Experiments (DOE). Assessed input factors were the peak current, pulse on-time, pulse off-time, and the electrode material. The result of the effects of each input factors was tool electrode wear. For calculating the relative wear of electrodes was used the percentage of the ratio of the electrode and the workpiece weight. The results show that the expression of the relative wear in practice can be described as a method unusable due to the absorption of the graphite electrode used in the experiment.

Modeling and Response Optimization of Traveling Wire Electro-Chemical Spark Machining of Borosilicate Glass Using Hybrid Approach

2020 ◽  
Vol 19 (03) ◽  
pp. 425-447
Author(s):  
Basanta Kumar Bhuyan ◽  
Pravabati Bhuyan ◽  
Satish Mishra
Keyword(s):  
Borosilicate Glass ◽  
Removal Rate ◽  
Hybrid Approach ◽  
Kerf Width ◽  
Machining Process ◽  
Tool Electrode ◽  
Input Parameters ◽  
Pulse On Time ◽  
Pulse Off Time ◽  
Traveling Wire

Traveling Wire Electro-Chemical Spark Machining (TW-ECSM) process is a new innovative thermal erosion-based machining process suitable for cutting electrically nonconductive materials using tool electrode in the form of wire. This article attempts experimental modeling of TW-ECSM process using a hybrid methodology comprising Taguchi methodology (TM) and response surface methodology (RSM). The experiments were carried out on borosilicate glass using L[Formula: see text] orthogonal array (OA) considering the input parameters like applied voltage, pulse on-time, pulse off-time, electrolyte concentration and wire feed velocity along with process performances such as material removal rate (MRR), surface roughness (R[Formula: see text] and kerf width (K[Formula: see text]. The interaction influence of input parameters on process performances was also discussed. Further, multi-objective optimization (MOO) of response performances of TW-ECSM process is executed using a coupled approach of grey relational analysis (GRA) and principal component analysis (PCA). The optimal process parameter setting illustrates the improvement of MRR by 171%, diminution of Ra and K[Formula: see text] by 27% and 8% against the initial parameter settings. Moreover, irregular cutting of kerf width and surface characteristics were also scrutinized using scanning electron microscope (SEM).

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