scholarly journals Metaheuristic Approach of Multi-Objective Optimization during EDM Process

2018 ◽  
Vol 3 (3) ◽  
pp. 301-314 ◽  
Author(s):  
Goutam Kumar Bose ◽  
Pritam Pain
Keyword(s):  
Regression Analysis ◽  
Removal Rate ◽  
Test Results ◽  
Multi Objective Optimization ◽  
Machining Processes ◽  
Multi Objective ◽  
Contour Plots ◽  
The Individual ◽  
Pulse On Time ◽  
Edm Process

In modern-day manufacturing Electric Discharge Machining (EDM) process has successfully placed itself in the domain of precision machining and generating complex geometries where secondary machining processes are eliminated. In this research paper, a die sinking EDM is applied to machine mild steel in order to measure the different multi-objective results like Material Removal Rate (MRR) and Over Cut (OC). This contradictory objective is accomplished by using the control parameters like a pulse on time, duty factor, gap current and spark gap employing copper tool with lateral flushing. Here the individual objective function of the responses is created through regression analysis. Primarily the contradictory objectives are optimized by employing Taguchi Methodology, then Regression analysis is done on the test results. Additionally, the experimental results are optimized using Response Surface Methodology (RSM). It is followed by a multi-objective optimization through Overlaid contour plots and Desirability functions to ascertain the best parametric combination amongst the set of feasible alternatives.

Nature-Inspired Metaheuristic Approach for Multi-Objective Optimization During WEDM Process

2018 ◽  
pp. 91-122
Author(s):  
Goutam Kumar Bose ◽  
Pritam Pain
Keyword(s):  
Removal Rate ◽  
Experimental Result ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
The Individual ◽  
Pulse On Time ◽  
Pulse Off Time ◽  
Wedm Process ◽  
Grey Relation ◽  
Off Time

In this research paper Wire-Electric Discharge Machining (WEDM) is applied to machine AISI-D3 material in order to measure the performance of multi-objective responses like high material removal rate and low roughness. This contradictory objective is accomplished by the control parameters like Pulse on Time (Ton), Pulse off Time (Toff), Wire Feed (W/Feed) and Wire Tension (W/Ten) employing brass wire. Here the orthogonal array is used to developed 625 parametric combinations. The optimization of the contradictory responses is carried out in a metaheuristic environment. Artificial Neural Network is employed to train and validate the experimental result. Primarily the individual responses are optimized by employing Firefly algorithm (FA). This is followed by a multi-objective optimization through Genetic algorithm (GA) approach. As the results obtained through GA infer a domain of solutions, therefore Grey Relation Analysis (GRA) is applied where the weights are considered through Fuzzy set theory to ascertain the best parametric combination amongst the set of feasible alternatives.

scholarly journals Multi-Objective Optimization of Wire Electrical Discharge Machining of AL 2124/SIC Composite

2019 ◽  
Vol 9 (1) ◽  
pp. 2155-2163
Keyword(s):  
Electrical Discharge ◽  
Electrical Discharge Machining ◽  
Removal Rate ◽  
High Strength ◽  
Wire Electrical Discharge Machining ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Predicted Values ◽  
Pulse On Time ◽  
Pulse Off Time

The growing demand for the use of high strength to weight alloys in industries for manufacturing complex structures challenges the machinability of such advanced materials. In the present investigation, the machinability of SiC particle reinforced Al 2124 composite was studied on Wire electrical discharge machining (WEDM). The process parameters namely pulse on-time (Ton), pulse off time (Toff), peak current (IP), and servo voltage (SV) were optimized by utilizing the central composite design layout. The output responses such as kerf and material removal rate (MRR) were studied in detail. The single and multi-objective optimization was studied for a combination effect using Derringer’s desirability approach and Genetic Algorithm (GA). The experimental and predicted values for each response were validated at the optimized condition. The experimental results were found in line with the predicted values. Multi objective optimization of kerf and MRR by GA showing better result compared to RSM.

Multi-Objective Optimization of Electrical Discharge Machining Parameters for 2024 Aluminum Alloy Using Grey-Taguchi Method

2020 ◽  
Vol 998 ◽  
pp. 55-60
Author(s):  
Jurapun Phimoolchat ◽  
Apiwat Muttamara
Keyword(s):  
Electrical Discharge Machining ◽  
Removal Rate ◽  
Electrode Wear ◽  
Machining Parameters ◽  
Multi Objective Optimization ◽  
2024 Aluminum Alloy ◽  
Multi Objective ◽  
Effect Estimation ◽  
Voltage Performance ◽  
Pulse On Time

This paper focused on Grey relational analysis (GRA) to optimize EDM parameters through multi-objective optimization for Al2024 aluminum and electrode graphite ISO-63 was used as a cutting tool. The process parameters pulse on time, duty factor, pulse current and open voltage. Performance characteristics examined included material removal rate (MRR), electrode wear ratio (EWR) and surface roughness (SR). Taguchi’s 27 experimental designs, often called an orthogonal array (OA), was utilized to ignore interaction and concentrate on main effect estimation. GRA was performed to optimize input parameters levels. Results were that MRR increased from 35.00 to 35.11 mm3/min, EWR decreased from 11.63 to 10.89 mm3/min, and SR decreased from 5.01 to 4.97 μm. Taguchi and GRA resulted in clear improvements in MRR, EWR, and SR.

Performance Investigation of Powder Mixed Electro Discharge Machining of Hypoeutectic Al-Si Alloy Using Brass Electrode

2019 ◽  
Vol 6 (2) ◽  
pp. 31-43
Author(s):  
Baliram Rajaram Jadhav ◽  
M. S. Sohani ◽  
Shailesh Shirguppikar
Keyword(s):  
Electrical Discharge Machining ◽  
Removal Rate ◽  
Input Parameter ◽  
Optimal Solution ◽  
Machining Parameters ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Pulse On Time ◽  
Taguchi’S Design Of Experiments ◽  
Grey Relation

The aim of this study is the multi- objective optimization of process parameters of Al- Si alloy in powder mixed electrical discharge machining for obtaining minimum surface roughness, minimum tool wear rate, and maximum material removal rate. The important machining parameters were selected as discharge current, voltage and pulse-on time. Experiments were conducted by selecting different operating levels for the three parameters according to Taguchi's Design of Experiments. The multi-objective optimization was performed using Grey Relation Analysis to determine the optimal solution. The Grey Relation Grade values were then analysed using analysis of variance to determine the most contributing input parameter. On analysis it was found that peak current, pulse-on time, and voltage had an influence of 94.73%, 3.32% and 0.36%, respectively, on the multi-performance characteristics.

Multi-Objective Optimization of PMEDM Input Factors for Processing Cylindrical Shaped Parts

2021 ◽  
Vol 1018 ◽  
pp. 71-77
Author(s):  
Tran Thi Hong ◽  
Nguyen Van Cuong ◽  
Tran Ngoc Hien ◽  
Bui Thanh Danh ◽  
Le Hong Ky ◽  
...  
Keyword(s):  
Removal Rate ◽  
Electrode Wear ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Powder Concentration ◽  
Grey Relational ◽  
Pulse On Time ◽  
Pulse Off Time ◽  
The Impact ◽  
Optimum Input

The present work deals with multi-objective optimization of powder mixed electric discharge machining (PMEDM) when processing cylindrically shaped parts. In this work, the pulse on time Ton, the pulse off time Toff, the powder concentration Cp, the pulse current IP, and the server voltage SV were chosen for the optimization problem. Also, the surface roughness (SR), the material removal rate (MRR), and the electrode wear (EW) were chosen as three objectives for the investigation. Besides, the Taguchi method and the grey relational analysis (GRA) were applied for optimizing simultaneously three the SR, the MRR and the EW to find the optimum input factors. The impact of the process parameters on the overall goal was weighed. Additionally, optimum input factors of PMEDM process for multi-objectives were recommended.

scholarly journals Experimental and Thermal Investigation on Powder Mixed EDM Using FEM and Artificial Neural Networks

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Venkata N. Raju Jampana ◽  
P. S. V. Ramana Rao ◽  
A. Sampathkumar
Keyword(s):  
Neural Network ◽  
Removal Rate ◽  
Machining Process ◽  
Electric Discharges ◽  
Process Performance ◽  
Test Results ◽  
Machining Processes ◽  
Thermal Investigation ◽  
Edm Process ◽  
Powder Mixed Edm

Electric discharge machining (EDM) process is one of the earliest and most extensively used unconventional machining processes. It is a noncontact machining process that uses a series of electric discharges to remove material from an electrically conductive workpiece. This article is aimed to do a comprehensive experimental and thermal investigation of the EDM, which can predict the machining characteristic and then optimize the output parameters with a newly integrated neural network-based methodology for modelling and optimal selection of process variables involved in powder mixed EDM (PMEDM) process. To compare and investigate the effects caused by powder of differently thermo physical properties on the EDM process performance with each other as well as the pure case, a series of experiments were conducted on a specially designed experimental setup developed in the laboratory. Peak current, pulse period, and source voltage are selected as the independent input parameters to evaluate the process performance in terms of material removal rate (MRR) and surface roughness (Ra). In addition, finite element method (FEM) is utilized for thermal analysis on EDM of stainless-steel 630 (SS630) grade. Further, back propagated neural network (BPNN) with feed forward architecture with analysis of variance (ANOVA) is used to find the best fit and approximate solutions to optimization and search problems. Finally, confirmation test results of experimental MRR are compared using the values of MRR obtained using FEM and ANN. Similarly, the test results of experimental Ra also compared with obtained Ra using ANN.

scholarly journals Multi-Objective Multidisciplinary Design Optimization of a Robotic Fish System

2021 ◽  
Vol 9 (5) ◽  
pp. 478
Author(s):  
Hao Chen ◽  
Weikun Li ◽  
Weicheng Cui ◽  
Ping Yang ◽  
Linke Chen
Keyword(s):  
Design Optimization ◽  
Optimization Algorithm ◽  
Multidisciplinary Design Optimization ◽  
Robotic Fish ◽  
Multidisciplinary Design ◽  
Optimization Approach ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Cfd Method ◽  
The Individual

Biomimetic robotic fish systems have attracted huge attention due to the advantages of flexibility and adaptability. They are typically complex systems that involve many disciplines. The design of robotic fish is a multi-objective multidisciplinary design optimization problem. However, the research on the design optimization of robotic fish is rare. In this paper, by combining an efficient multidisciplinary design optimization approach and a novel multi-objective optimization algorithm, a multi-objective multidisciplinary design optimization (MMDO) strategy named IDF-DMOEOA is proposed for the conceptual design of a three-joint robotic fish system. In the proposed IDF-DMOEOA strategy, the individual discipline feasible (IDF) approach is adopted. A novel multi-objective optimization algorithm, disruption-based multi-objective equilibrium optimization algorithm (DMOEOA), is utilized as the optimizer. The proposed MMDO strategy is first applied to the design optimization of the robotic fish system, and the robotic fish system is decomposed into four disciplines: hydrodynamics, propulsion, weight and equilibrium, and energy. The computational fluid dynamics (CFD) method is employed to predict the robotic fish’s hydrodynamics characteristics, and the backpropagation neural network is adopted as the surrogate model to reduce the CFD method’s computational expense. The optimization results indicate that the optimized robotic fish shows better performance than the initial design, proving the proposed IDF-DMOEOA strategy’s effectiveness.

EDM Process Parameters Optimization for Al-TiO2 Nano Composite

2015 ◽  
Vol 2 (2) ◽  
pp. 17-30 ◽  
Author(s):  
Arvind Kumar Dixit ◽  
Richa Awasthi
Keyword(s):  
Discharge Current ◽  
Titanium Aluminide ◽  
Metal Removal ◽  
Removal Rate ◽  
Stir Casting ◽  
Parameters Optimization ◽  
Parameter Design ◽  
Nano Composite ◽  
Pulse On Time ◽  
Edm Process

Titanium aluminide reinforced aluminium based metal matrix nano composite was prepared by stir casting route. Experiments were conducted with Cu electrode using L9 orthogonal array based on the Taguchi method. Discharge current (Lv), Pulse on time (Ton) and Flushing pressure (FP) are selected to calculate Metal removal rate (MRR), Tool wear rate (TWR) and Surface roughness (SR) based on Taguchi's parameter design. Moreover, the signal-to-noise ratios associated with the observed values in the experiments were determined using MINITAB software for MRR, TWR and SR. PCR – TOPSIS method is used to optimize Taguchi's multi response. Optimum parameter setting is found at Discharge current (Lv) 10 A, Pulse on time (Ton) 150 µs and Flushing pressure (FP) 1 kg/cm2.

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