scholarly journals Comparative Study of Electrical Discharge Machining, Electrical Discharge Drilling and Electrical Discharge Diamond Drilling of Nimonic 75

2021 ◽  
Vol 1104 (1) ◽  
pp. 012031
Author(s):  
Rajeev Kumar ◽  
Somnath Chattopadhyaya ◽  
Sanjeev Kumar ◽  
G.K. Singh ◽  
U.K. Vates
Keyword(s):  
Comparative Study ◽  
Electrical Discharge ◽  
Electrical Discharge Machining ◽  
Diamond Drilling ◽  
Electrical Discharge Drilling ◽  
Nimonic 75

Einsatz eines naturanalogen Algorithmus/Application of a natural analogue algorithm – Reproducible optimization of electrical-discharge drilling

2020 ◽  
Vol 110 (07-08) ◽  
pp. 467-470
Author(s):  
Eckart Uhlmann ◽  
Mitchel Polte ◽  
Jan Streckenbach ◽  
Mirsad Osmanovic ◽  
Julian Börnstein
Keyword(s):  
Industrial Application ◽  
Electrical Discharge ◽  
Electrode Materials ◽  
Electrical Discharge Machining ◽  
Optimization Method ◽  
Hardened Steel ◽  
Evolution Strategy ◽  
Natural Analogue ◽  
Electrical Discharge Drilling ◽  
Materials Used

Dieser Beitrag stellt die Ergebnisse zur Untersuchung der Reproduzierbarkeit des Optimierungsverfahrens Evolutionsstrategie (ES) am Beispiel des funkenerosiven Bohrens vor. Dazu wurden zwei ES-Typen untersucht. Als Elektrodenwerkstoffe kamen Messing für das Werkzeug und für das Werkstück gehärteter Stahl zum Einsatz. Im Ergebnis konnte die Erosionsdauer reproduzierbar um 37 % verringert werden. Dieser Nachweis bildet die Grundlage für den industriellen Einsatz der Evolutionsstrategie beim funkenerosiven Bohren.   This article describes the results of the investigation on the reproducibility of the optimization method of evolution strategy (ES), exemplified by electrical-discharge drilling. Two types of ES were examined. The electrode materials used were brass for the tool and hardened steel for the workpiece. The erosion duration could be reduced by 37 % in a reproduceable manner. This verification is the basis for the industrial application of the ES for the optimization of electrical discharge machining.

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.

Analysis of Dimensional Accuracy (Over Cut) and Surface Quality (Roughness) in Electrical Discharge Machining of Inconel-718 Alloy

2019 ◽  
Vol 969 ◽  
pp. 644-649
Author(s):  
Rakesh Kumar ◽  
Anand Pandey ◽  
Pooja Sharma
Keyword(s):  
Inconel 718 ◽  
Electrical Discharge ◽  
Electrical Discharge Machining ◽  
Dimensional Accuracy ◽  
Work Piece ◽  
Tool Electrode ◽  
Affecting Factors ◽  
Inconel 718 Alloy ◽  
Electrical Discharge Drilling ◽  
Pulse On Time

Inconel-718 is a nickel based super alloy (difficult-to-cut material) used in aerospace industry. Analysis of machining performances viz. Over Cut (OC) & Surface Roughness (SR) for Inconel-718 through rotary Cu-pin tool electrode have been carried out. Peak current (Ip), pulse-on time (Ton), tool rotation (Nt) & hole depth (h) were used as input factors in Electrical Discharge Drilling (EDD) of Inconel-718 work-piece. Effect of input parameters on performance characteristics like OC & SR were found by Taguchi’s L9 (34) orthogonal array. It is reveals that Ip & h are most affecting factors that affects OC & SR. The Scanning Electron Microscope image was used to measure diameter of hole on work-piece after machining.

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

2018 ◽  
pp. 728-749
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.

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