Intelligent process modeling and optimization of die-sinking electric discharge machining

Joshi, Shrikrishna N.; Pande, S.S.

doi:10.1016/j.asoc.2010.11.005

Cited by 123 publications

(55 citation statements)

References 32 publications

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“…The usage of introduced temperature dependent material properties was one of the major features of their model which led to a better accuracy in predicting of MRR parameter. Joshi and Pande [9] introduced an intelligent process modeling and optimization of EDM process.…”

Section: Introductionmentioning

confidence: 99%

Mathematical and numerical modeling of the effect of input-parameters on the flushing efficiency of plasma channel in EDM process

Shabgard

Ahmadi

Seyedzavvar

et al. 2013

International Journal of Machine Tools and Manufacture

136

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a b s t r a c tIn the present study, the temperature distribution on the surface of workpiece and tool during a single discharge in the electrical discharge machining process has been simulated using ABAQUS code finite element software. The temperature dependency of material properties and the expanding of plasma channel radius with time have been employed in the simulation stage. The profile of temperature distribution has been utilized to calculate the dimensions of discharge crater. Based on the results of FEM and the experimental observations, a numerical analysis has been developed assessing the contribution of input-parameters on the efficiency of plasma channel in removing the molten material from molten puddles on the surfaces of workpiece and tool at the end of each discharge. The results show that the increase in the pulse current and pulse on-time have converse effects on the plasma flushing efficiency, as it increases by the prior one and decreases by the latter one. Later, the introduced formulas for plasma flushing efficiency based on regression model were utilized to predict the cardinal parameter of recast layer thickness on the electrodes which demands expensive empirical tests to be obtained.

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Section: Introductionmentioning

confidence: 99%

Mathematical and numerical modeling of the effect of input-parameters on the flushing efficiency of plasma channel in EDM process

Shabgard

Ahmadi

Seyedzavvar

et al. 2013

International Journal of Machine Tools and Manufacture

136

View full text Add to dashboard Cite

show abstract

“…Optimization and networks design applications are very broad, traversing from a specific area and ranging from engineering design (Schwabacher et al 1998;Coelho and Mariani 2008;Kashan 2011), process optimization (Egea et al 2010;Joshi and Pande 2011;Kwak and Kim 2012), scheduling system (Andersson et al 2007;Frantzénl et al 2011;Skobelev 2011), routing and flow control in networks and networking (Madan et al 2007;Shakkottai and Srikant 2007;Minoux 2010), to service oriented applications in finance (He et al 2008;Leibfritz and Maruhn 2009;Pennanen 2011), healthcare (Harrell and Lange 2001;Bagirov and Churilov 2003;José et al 2011), and bioinformatics (Hernandez and Kambhampati 2004;Nebro et al 2008;Arredondo et al 2011). For example, in the formulation of the scheduling system, optimization can be used to determine the course of vehicle systems to the various destinations, to determine the scheduling of jobs in the factory, scheduling lectures at universities, creating timetable, computer network design and planning strategies for finding an optimal decision.…”

Section: Optimization and Network Designmentioning

confidence: 99%

An introduction to swarming robotics: application development trends

2013

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Animals help to sustain the environment's life cycle and ecosystem. Without human intervention, these creatures carry out their 'spontaneous routine' jobs and contribute towards balance in nature. Any natural system that congregates as a result of some form of collective intelligence of nature is also known as swarm intelligence (SI). This metaphor inspires a variety of techniques to solve the problem of calculating, in most cases dealing with optimization problems and has sparked interest amongst scientists. It is very trying for a new researcher to understand the whole concept of swarming robotics (SR) and optimization algorithm (i.e. realizing the idea from animal's perception to the SR application). In addition, the existing algorithms are computationally complicated, difficult to be understood by beginners as there are too many parameters. Thus, in this paper, we simplify the three branches of the main applications which are frequently used for SI namely: (1) optimization and networks design, (2) prediction and forecasting, and (3) SR. This paper summarizes the basic understanding overview of swarming robotics and discusses their basic concepts and principles.

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“…Researchers had already solved the multiobjective optimization problems in machining processes using well-known posteriori approaches such as NSGA, NSGA-II, MOGA, and MODE (Mitra and Gopinath 2004;Kuriakose and Shunmugam 2005;Konak et al 2006;Mandal et al 2007;Kodali et al 2008;Kanagarajan et al 2008;Palanikumar et al 2009;Datta and Deb 2009;Yang and Natarajan 2010;Senthilkumar et al 2010Senthilkumar et al , 2011Joshi and Pande 2011;Mitra 2009;Acharya et al 2013). However, these algorithms require tuning of algorithm-specific parameters and improper tuning of algorithm-specific parameters may lead to non-Pareto optimal solutions.…”

Section: Multiobjective Optimization Of Machining Processesmentioning

confidence: 99%

Multiobjective Optimization of Machining Processes Using NSTLBO Algorithm

Rao

2015

Teaching Learning Based Optimization Algorithm

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Multiobjective optimization aspects of a traditional machining process namely surface grinding and five modern machining processes namely, wire-electro discharge machining process, micro-wire-electric discharge machining process, laser cutting process, electrochemical machining process, and electrochemical discharge machining process are presented in this chapter. A posteriori multiobjective optimization algorithm named as nondominated sorting teaching-learning-based optimization (NSTLBO) algorithm is proposed to solve the multiobjective optimization problems of the machining processes. A Pareto optimal set of solutions along with a Pareto front is presented for each of the considered machining processes.

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Intelligent process modeling and optimization of die-sinking electric discharge machining

Cited by 123 publications

References 32 publications

Mathematical and numerical modeling of the effect of input-parameters on the flushing efficiency of plasma channel in EDM process

Mathematical and numerical modeling of the effect of input-parameters on the flushing efficiency of plasma channel in EDM process

An introduction to swarming robotics: application development trends

Multiobjective Optimization of Machining Processes Using NSTLBO Algorithm

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