scholarly journals Multi-Response Optimization of Electrical Discharge Drilling Process of SS304 for Energy Efficiency, Product Quality, and Productivity

Materials ◽  
2020 ◽  
Vol 13 (13) ◽  
pp. 2897 ◽  
Author(s):  
Trung-Thanh Nguyen ◽  
Van-Tuan Tran ◽  
Mozammel Mia
Keyword(s):  
Energy Efficiency ◽  
Electrical Discharge ◽  
Fuzzy Inference ◽  
Removal Rate ◽  
Integrative Approach ◽  
Technical Solution ◽  
Drilling Process ◽  
Analytic Hierarchy ◽  
Inference System ◽  
Electrical Discharge Drilling

The electrical discharge drilling (EDD) process is an effective machining approach to produce various holes in difficult-to-cut materials. However, the energy efficiency of the EDD operation has not thoroughly been considered in published works. The aim of the current work is to optimize varied parameters for enhancing the material removal rate (MRR), saving drilled energy (ED), and decreasing the expansion of the hole (HE) for the EDD process. Three advanced factors, including the gap voltage adjustor (GAP), capacitance parallel connection (CAP), and servo sensitivity selection (SV), are considered. The predictive models of the performances were proposed with the support of the adaptive neuro-based fuzzy inference system (ANFIS). An integrative approach combining the analytic hierarchy process (AHP) and the neighborhood cultivation genetic algorithm (NCGA) was employed to select optimal factors. The findings revealed the optimal values of the CAP, GAP, and SV were 6, 5, and 4, respectively. Moreover, the ED and HE were decreased by 16.78% and 28.68%, while the MRR was enhanced by 89.72%, respectively, as compared to the common setting values. The explored outcome can be employed as a technical solution to enhance the energy efficiency, drilled quality, and productivity of the EDD operation.

scholarly journals On the Influence of Electrical Discharge Drilling Parameters and Per-formance Measures of Inconel 718 Superalloy - a Study

Mechanika ◽  
2021 ◽  
Vol 27 (6) ◽  
pp. 483-491
Author(s):  
Jayaraj JEEVAMALAR ◽  
Sundaresan RAMABALAN ◽  
Jayaraj JANCIRANI
Keyword(s):  
Inconel 718 ◽  
Electrical Discharge ◽  
Removal Rate ◽  
Experimental Results ◽  
Drilling Process ◽  
Machining Parameters ◽  
Characteristic Analysis ◽  
Peak Current ◽  
Electrical Discharge Drilling ◽  
The Impact

In order to achieve higher productivity and product quality, the investigation of machining parameters on Electrical Discharge Drilling and surface characteristic analysis are most critical for manufacturing industries. The intention of this article is to assess the impact on performance matrices including Material Removal Rate, and Surface Roughness of input factors of peak current, pulse-on and off duration while drilling with a rotary hollow copper tool on Inconel 718 under Tungsten powder suspended kerosene. Analysis of Variance has been implemented using MINITAB release 18 software to identify the most significant input factors. An Artificial Neural Network was used for validating the experimental results of the drilling process. The additional intention of this research is to discover the significance of influencing input parameters and analyze the quality surface of the workpiece were observed by microscope tests. The experimental results indicated that the peak current and pulse-on period have an effect on the performance of the drilling process considerably.

scholarly journals Using a Fuzzy Inference System to Obtain Technological Tables for Electrical Discharge Machining Processes

Mathematics ◽  
2020 ◽  
Vol 8 (6) ◽  
pp. 922 ◽  
Author(s):  
C. J. Luis Pérez
Keyword(s):  
Fuzzy Inference System ◽  
Electrical Discharge ◽  
Material Removal ◽  
Fuzzy Inference ◽  
Electrical Discharge Machining ◽  
Removal Rate ◽  
Operating Conditions ◽  
Electrode Wear ◽  
Machining Parameters ◽  
Inference System

Technological tables are very important in electrical discharge machining to determine optimal operating conditions for process variables, such as material removal rate or electrode wear. Their determination is of great industrial importance and their experimental determination is very important because they allow the most appropriate operating conditions to be selected beforehand. These technological tables are usually employed for electrical discharge machining of steel, but their number is significantly less in the case of other materials. In this present research study, a methodology based on using a fuzzy inference system to obtain these technological tables is shown with the aim of being able to select the most appropriate manufacturing conditions in advance. In addition, a study of the results obtained using a fuzzy inference system for modeling the behavior of electrical discharge machining parameters is shown. These results are compared to those obtained from response surface methodology. Furthermore, it is demonstrated that the fuzzy system can provide a high degree of precision and, therefore, it can be used to determine the influence of these machining parameters on technological variables, such as roughness, electrode wear, or material removal rate, more efficiently than other techniques.

scholarly journals A Mathematical Model to Assess the Material Removal Rate during Gas-Assisted Electrical Discharge Drilling Process

2020 ◽  
Vol 5 (6) ◽  
pp. 1333-1344
Author(s):  
Nishant Kumar Singh ◽  
Yashvir Singh ◽  
Abhishek Sharma
Keyword(s):  
Mathematical Model ◽  
Material Removal Rate ◽  
Electrical Discharge ◽  
Material Removal ◽  
Removal Rate ◽  
Drilling Process ◽  
High Carbon ◽  
Electrical Discharge Drilling ◽  
Pulse On Time ◽  
The Impact

This research work investigates the use of Gas-Assisted Electrical Discharge Drilling (GAEDD) of high carbon-chromium die steel. The poor material removal rate (MRR) is one of the profound drawbacks of the traditional Electrical Discharge Drilling (EDD) process. Hence, over the years researchers have been feeling the requisite to develop an advanced strategy that can enhance the MRR. This study has examined the utilization of compressed gas in conventional EDM under the constraint state to assess MRR. The impact of procedure parameters likedischarge current, pulse on time, duty cycle, electrode speed, and discharge gas pressure, on MRR, has been explored too. In addition, Variance Analysis (ANOVA) was performed to determine the significant parameters affecting the MRR. During the examination, a mathematical model was established MRR employing Buckingham π-theorem while the GAEDD was being applied. The experiment and anticipated values of the model show a noteworthy impact of the coefficient of thermal expansion in GAEDD of high carbon-chromium steel. In addition, the Response Surface Method (RSM) model has also been evolved. The comparative analysis of the models developed shows considerable agreement in anticipation. Moreover, the semi-empirical model appears to be even more adaptable especially in comparison to the RSM-based model. In fact, the conclusion of this work is that the dimensional analysis model is an effective and reliable strategy to precise EDD response prediction.

A Multiobjective Genetic-Algorithm-Based Optimization of Micro-Electrical Discharge Drilling

2021 ◽  
pp. 676-698
Author(s):  
Deepak Rajendra Unune ◽  
Amit Aherwar
Keyword(s):  
Genetic Algorithm ◽  
Process Parameters ◽  
Inconel 718 ◽  
Electrical Discharge ◽  
Electrical Discharge Machining ◽  
Removal Rate ◽  
Machining Processes ◽  
Micro Electrical Discharge Machining ◽  
Electrical Discharge Drilling ◽  
Machining Rate

Inconel 718 superalloy finds wide range of applications in various industries due to its superior mechanical properties including high strength, high hardness, resistance to corrosion, etc. Though poor machinability especially in micro-domain by conventional machining processes makes it one of the “difficult-to-cut” material. The micro-electrical discharge machining (µ-EDM) is appropriate process for machining any conductive material, although selection of machining parameters for higher machining rate and accuracy is difficult task. The present study attempts to optimize parameters in micro-electrical discharge drilling (µ-EDD) of Inconel 718. The material removal rate, electrode wear ratio, overcut, and taper angle have been selected as performance measures while gap voltage, capacitance, electrode rotational speed, and feed rate have been selected as process parameters. The optimum setting of process parameters has been obtained using Genetic Algorithm based multi-objective optimization and verified experimentally.

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.

Estimating the Total Organic Carbon for Unconventional Shale Resources During the Drilling Process: A Machine Learning Approach

2021 ◽  
pp. 1-26
Author(s):  
Ahmed Mahmoud ◽  
Hany Gamal ◽  
Salaheldin Elkatatny ◽  
Ahmed Alsaihati
Keyword(s):  
Machine Learning ◽  
Organic Carbon ◽  
Total Organic Carbon ◽  
Gamma Ray ◽  
Fuzzy Inference ◽  
Support Vector ◽  
Drilling Process ◽  
Validation Data ◽  
Inference System ◽  
Data Points

Abstract Total organic carbon (TOC) is an essential parameter that indicates the quality of unconventional reservoirs. In this study, four machine learning (ML) algorithms of the adaptive neuro-fuzzy inference system (ANFIS), support vector regression (SVR), functional neural networks (FNN), and random forests (RF) were optimized to evaluate the TOC. The novelty of this work is that the optimized models predict the TOC from the bulk gamma-ray (GR) and spectral GR logs of uranium, thorium, and potassium only. The ML algorithms were trained on 749 datasets from Well-1, tested on 226 datasets from Well-2, and validated on 73 data points from Well-3. The predictability of the optimized algorithms was also compared with the available equations. The results of this study indicated that the optimized ANFIS, SVR, and RF models overperformed the available empirical equations in predicting the TOC. For validation data of Well-3, the optimized ANFIS, SVR, and RF algorithms predicted the TOC with AAPE's of 10.6%, 12.0%, and 8.9%, respectively, compared with the AAPE of 21.1% when the FNN model was used. While for the same data, the TOC was assessed with AAPE's of 48.6%, 24.6%, 20.2%, and 17.8% when Schmoker model, ΔlogR method, Zhao et al. correlation, and Mahmoud et al. correlation was used, respectively. The optimized models could be applied to estimate the TOC during the drilling process if the drillstring is provided with GR and spectral GR logging tools.

scholarly journals A Forecast and Mitigation Model of Construction Performance by Assessing Detailed Engineering Maturity at Key Milestones for Offshore EPC Mega-Projects

2019 ◽  
Vol 11 (5) ◽  
pp. 1256 ◽  
Author(s):  
Myung-Hun Kim ◽  
Eul-Bum Lee ◽  
Han-Suk Choi
Keyword(s):  
Fuzzy Inference ◽  
Risk Mitigation ◽  
Qualitative Comparative Analysis ◽  
Major Elements ◽  
Cost Overrun ◽  
Analytic Hierarchy ◽  
Cost Performance ◽  
Inference System ◽  
Trade Off ◽  
Statistical Confidence

The main subject of this research is to develop a forecast and mitigation model of schedule and cost performance during a detailed engineering stage of offshore engineering, procurement and construction (EPC) projects. The weight factors of major elements in detailed engineering completion rating index system (DECRIS) were measured using a fuzzy inference system (FIS) and an analytic hierarchy process (AHP). At five key engineering milestones, from an EPC contract being awarded to the start of construction, detailed engineering maturities were assessed in fourteen historical offshore EPC projects using the DECRIS model. DECRIS cutoff scores for successful project execution were defined at the key engineering milestones. A schedule and cost performance was forecasted and validated through comparison of DECRIS and other models using statistical confidence of a fuzzy set qualitative comparative analysis (fsQCA) and a regression analysis. As a mitigation method for engineering risks to EPC contractors, engineering resource enhancement is recommended for trade-off optimization of cost overrun using a Monte Carlo simulation. The main contribution of this research is that EPC contractors could continuously forecast construction costs and schedule performance utilizing the DECRIS model, and could review the adequacy of engineering resources, assessing the trade-off between said resources and cost/schedule risk mitigation.

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