Multi-Objective Optimization of Die-Sinking EDM Process on AISI P-20 Tool Steel Using Cuckoo Search and Genetic Algorithm

2017 ◽  
pp. 111-129
Author(s):  
Goutam Kumar Bose ◽  
Pritam Pain ◽  
Sayak Mukhopadhyay
Keyword(s):  
Genetic Algorithm ◽  
Tool Steel ◽  
Process Parameters ◽  
Electrical Discharge Machining ◽  
Search Algorithm ◽  
Removal Rate ◽  
Research Work ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Machining Processes

Electrical Discharge Machining (EDM) is nontraditional machining processes applied for precise machining and developing intricate geometries on work materials which are difficult to machine by conventional process. The present research work emphases on the die sinking EDM of AISI P20 tool steel, to study the effect of machining parameters such as pulse on time (POT), pulse off time (POF), discharge current (GI) and spark gap (SG) on performance response like Material removal rate (MRR), Surface Roughness (Ra) and Overcut (OC) using square-shaped Cu tool with Lateral flushing. The experimentation is performed using L27 orthogonal array and significant process parameters are ascertained using Regression analysis. The influence of the important process parameters on individual responses are detected using Cuckoo search algorithm. The present chapter is aimed at multi-response optimization i.e. higher MRR, lower Ra and minimum OC, which is conceded out using Genetic Algorithm.

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.

Optimization Through Nature-Inspired Soft-Computing and Algorithm on ECG Process

2017 ◽  
pp. 489-519 ◽  
Author(s):  
Goutam Kumar Bose ◽  
Pritam Pain
Keyword(s):  
Soft Computing ◽  
Search Algorithm ◽  
Removal Rate ◽  
Research Work ◽  
Cuckoo Search ◽  
Lower Surface ◽  
Cuckoo Search Algorithm ◽  
Depth Of Cut ◽  
Machining Parameters ◽  
Response Optimization

In the present research work selection of significant machining parameters depending on nature-inspired algorithm is prepared, during machining alumina-aluminum interpenetrating phase composites through electrochemical grinding process. Here during experimentation control parameters like electrolyte concentration (C), voltage (V), depth of cut (D) and electrolyte flow rate (F) are considered. The response data are initially trained and tested applying Artificial Neural Network. The paradoxical responses like higher material removal rate (MRR), lower surface roughness (Ra), lower overcut (OC) and lower cutting force (Fc) are accomplished individually by employing Cuckoo Search Algorithm. A multi response optimization for all the response parameters is compiled primarily by using Genetic algorithm. Finally, in order to achieve a single set of parametric combination for all the outputs simultaneously fuzzy based Grey Relational Analysis technique is adopted. These nature-driven soft computing techniques corroborates well during the parametric optimization of ECG process.

scholarly journals TEACHING-LEARNING-BASED PARAMETRIC OPTIMIZATION OF AN ELECTRICAL DISCHARGE MACHINING PROCESS

2020 ◽  
Vol 18 (2) ◽  
pp. 281 ◽  
Author(s):  
Vidyapati Kumar ◽  
Sunny Diyaley ◽  
Shankar Chakraborty
Keyword(s):  
Electrical Discharge ◽  
Electrical Discharge Machining ◽  
Search Algorithm ◽  
Removal Rate ◽  
Differential Evolution Algorithm ◽  
Cuckoo Search ◽  
Machining Process ◽  
Machining Processes ◽  
Tlbo Algorithm ◽  
Teaching Learning

Due to several unique features, electrical discharge machining (EDM) has proved itself as one of the efficient non-traditional machining processes for generating intricate shape geometries on various advanced engineering materials in order to fulfill the requirement of the present day manufacturing industries. In this paper, the machining capability of an EDM process is studied during standard hole making operation on pearlitic SG iron 450/12 grade material, while considering gap voltage, peak current, cycle time and tool rotation as input parameters. On the other hand, material removal rate, surface roughness, tool wear rate, overcut and circularity error are treated as responses. Based on single- and multi-objective optimization models, this process is optimized using the teaching-learning-based optimization (TLBO) algorithm, and its performance is contrasted against firefly algorithm, differential evolution algorithm and cuckoo search algorithm. It is revealed that the TLBO algorithm supersedes the others with respect to accuracy and consistency of the derived optimal solutions, and computational efforts.

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.

Optimization Through Nature-Inspired Soft-Computing and Algorithm on ECG Process

2020 ◽  
pp. 784-813
Author(s):  
Goutam Kumar Bose ◽  
Pritam Pain
Keyword(s):  
Soft Computing ◽  
Search Algorithm ◽  
Removal Rate ◽  
Research Work ◽  
Cuckoo Search ◽  
Lower Surface ◽  
Cuckoo Search Algorithm ◽  
Depth Of Cut ◽  
Machining Parameters ◽  
Response Optimization

In the present research work selection of significant machining parameters depending on nature-inspired algorithm is prepared, during machining alumina-aluminum interpenetrating phase composites through electrochemical grinding process. Here during experimentation control parameters like electrolyte concentration (C), voltage (V), depth of cut (D) and electrolyte flow rate (F) are considered. The response data are initially trained and tested applying Artificial Neural Network. The paradoxical responses like higher material removal rate (MRR), lower surface roughness (Ra), lower overcut (OC) and lower cutting force (Fc) are accomplished individually by employing Cuckoo Search Algorithm. A multi response optimization for all the response parameters is compiled primarily by using Genetic algorithm. Finally, in order to achieve a single set of parametric combination for all the outputs simultaneously fuzzy based Grey Relational Analysis technique is adopted. These nature-driven soft computing techniques corroborates well during the parametric optimization of ECG process.

Electrical Discharge Machining Electrodes Manufactured via Selective Laser Sintering Technique Using New Composite Materials

2013 ◽  
Author(s):  
Fred Lacerda Amorim ◽  
Tiago Czelusniak ◽  
Camila Higa
Keyword(s):  
Electrical Discharge ◽  
Electrical Discharge Machining ◽  
Removal Rate ◽  
Selective Laser Sintering ◽  
Research Work ◽  
Laser Sintering ◽  
Superior Performance ◽  
Future Research ◽  
Machining Processes ◽  
Functional Components

The cost of a part manufactured by Electrical Discharge Machining (EDM) is mainly determined by electrode cost. The production of electrodes by conventional machining processes is complex, time consuming and can account for over fifty percent of the total EDM process costs. The emerging Additive Manufacturing (AM) technologies provide the possibility of direct fabrication of EDM electrodes. Selective Laser Sintering (SLS) is an alternative AM technique because it has the possibility to directly produce functional components, reducing the tool-room lead time and total EDM costs. The main difficulty of manufacturing an EDM electrode using SLS is the selection of an appropriate material, once both processes require different material properties. The current work focused on the investigation of appropriate materials that fulfill EDM and SLS process demands. Three new metal-matrix materials composed of Mo-CuNi, TiB2-CuNi and ZrB2-CuNi were developed and electrodes under adequate SLS conditions were manufactured. EDM experiments using different discharge energies were carried out and the performance evaluated in terms of material removal rate and volumetric relative wear. The results showed the powder systems composed of Mo-CuNi, TiB2-CuNi and ZrB2-CuNi revealed to be successfully processed by SLS and the EDM experiments demonstrated that the newly composite electrodes possess superior performance when compared to copper powder electrodes made with SLS. The work also suggests important topics for future research work on this field.

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