Multi-objective optimization of electro-discharge machining (EDM) parameter for sustainable machining

Mohanty, Uttam Kumar; Rana, Jaydev; Sharma, Abhay

doi:10.1016/j.matpr.2017.07.271

Cited by 16 publications

(6 citation statements)

References 4 publications

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“…Pulse time (Ton) [1]- [2]- [3]- [4]- [5]- [6]- [7]- [8]- [9]- [10]-[11]- [12]- [13]- [14]- [16] Rest time (Toff) [1]- [2]- [5]- [6]- [7]- [8]-[11]- [12]- [13]- [14] Discharge current (I) [1]- [3]- [4]- [5]- [6]- [7]- [8]- [9]- [10]-[11]- [12]- [14]- [16] Secondary Distance between electrode and work piece (GAP) [6] Duty cycle (DC) [2]- [10]- [12] ]- [16] Voltage (V) [2]- [3]- [4]- [8]- [10]- [12]- [13] Parameter (TUP) [9] Recently, there has been a sharp increase in the application of the experimental method based mainly on practical trials. Several experimental modeling techniques with varying degrees of complexity have been w...…”

Section: Principalmentioning

confidence: 99%

Identification of the effects and interactions of factors on the EDM process in order to model it using Taguchi method

KAMOUN

MESLAMENİ

2022

International Journal of Engineering and Innovative Research

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This article presents the identification of the influence of the effects and interactions of the machining parameters (EDM) of the machine (EROTECH basic 450) in order to model the material removal rate (MRR), the tool wear rate (TWR) and the roughness of the part (Ra). We look at all the machining parameters and collect the effects by the design of experiments method. The modeling carried out is thus carried out by the response surfaces method (RSM). We use the statistical method (ANOVA) analysis of variance to approve the robustness of the models and to verify that they are statistically significant. The Taguchi method was implemented to formulate mathematical models to predict EDM machining parameters. The prediction of responses by empirical models is compared with experimental validation tests and the results are satisfactory.

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Section: Principalmentioning

confidence: 99%

Identification of the effects and interactions of factors on the EDM process in order to model it using Taguchi method

KAMOUN

MESLAMENİ

2022

International Journal of Engineering and Innovative Research

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“…Satpathy et al [15] combined principal component analysis with technique for order of preference by similarity to ideal solution (TOPSIS) for multi-objective optimization of an EDM process, while taking into account peak current, pulse-on time, duty cycle and gap voltage as the input parameters, and MRR, TWR, ROC and SR as the responses. Applying VIKOR index as a multi-objective optimization tool for an EDM process, Mohanty et al [16] determined the optimal settings of current, pulse-on time and voltage for having better values of MRR, TWR, SR and ROC. Singh et al [17] utilized NSGA-II technique to optimize MRR and TWR in an EDM process while considering peak current, pulse-on time, pulses-off time and gap voltage as the input parameters.…”

Section: Review Of the Literaturementioning

confidence: 99%

Teaching-Learning-Based Parametric Optimization of an Electrical Discharge Machining Process

2020

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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.

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“…Energy consumption [12]- [21] Carbon emission [16]- [18], [20]- [22] Material and/or tool waste [19], [21], [23] Economic Cost [17], [21], [22] Productivity [12]- [14], [20]- [22], [24] Quality [12]- [15], [19], [21], [23]- [26] Social Health and safety [18] Labor and workforce training [21] On the contrary, the a posteriori approach, firstly, brings the set of non-dominated solutions (which are optimal in the wide sense that no other solution in the considered search space, can improve one of the objectives without worsening, at least, another one), which is known as the Pareto front and, after that, allows choosing the most convenient alternative from these solutions [27]. Pareto-based techniques have become the most suitable choice for solving multi-objective optimization problems [28] and has been widely applied for practical manufacturing cases [29].…”

Section: Environmentalmentioning

confidence: 99%

Towards Sustainability of Manufacturing Processes by Multiobjective Optimization: A Case Study on a Submerged Arc Welding Process

et al. 2020

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Optimization on the basis of sustainability brings important benefits to manufacturing process as sustainable productions constitute a crucial aspect in modern manufacturing. This paper presents a new formalized framework for optimizing the sustainability of manufacturing processes. Unlike previous approaches, the proposed technique combines a methodology for selecting the sustainability indicators and a multi-objective optimization for improving the three sustainability pillars (economy, environment and society). While selecting the significant sustainability indicators in the considered manufacturing process relies on the ABC judgment method, the Saaty's method enables weighting the chosen indicators in order to combine them into suitable economic, environmental and social sustainability indexes. Other technological aspects, usually taken as objectives in previous works, are considered constraints in the proposed approach. The optimization is performed by using nature inspired heuristics, which return the set of non-dominated solutions (also known as Pareto front), from which the most convenient alternative is chosen by the decision maker, depending on the specific conditions of the process. For illustrating the usage of the proposed framework, it is applied to the optimization of a submerged arc welding process. Compared with currently used welding parameters, the computed optimal solution outperforms the economic and environmental sustainability while keeps equal the social impact. The results show not only the effectiveness of the proposed approach, but also its flexibility by giving a set of possible solutions which can be chosen depending on how are ranked the sustainability pillars.

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Multi-objective optimization of electro-discharge machining (EDM) parameter for sustainable machining

Cited by 16 publications

References 4 publications

Identification of the effects and interactions of factors on the EDM process in order to model it using Taguchi method

Identification of the effects and interactions of factors on the EDM process in order to model it using Taguchi method

Teaching-Learning-Based Parametric Optimization of an Electrical Discharge Machining Process

Towards Sustainability of Manufacturing Processes by Multiobjective Optimization: A Case Study on a Submerged Arc Welding Process

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