Optimization of electric discharge machining using simulated annealing

Tzeng

Int J Adv Manuf Technol

et al. 2011

This study analyzes variations in metal removal rate (MRR) and quality performance of roughness average (R a ) and corner deviation (CD) depending on parameters of wire electrical discharge machining (WEDM) process in relation to the cutting of pure tungsten profiles. A hybrid method including response surface methodology (RSM) and back-propagation neural network (BPNN) integrated simulated annealing algorithm (SAA) were proposed to determine an optimal parameter setting. The results of 18 experimental runs via a Taguchi orthogonal table were utilized to train the BPNN to predict the MRR, R a , and CD properties. Simultaneously, RSM and SAA approaches were individually applied to search for an optimal setting. In addition, analysis of variance was implemented to identify significant factors for the processing parameters. Furthermore, the field-emission scanning electron microscope images show that a lot of built-edge layers were presented on the finishing surface after the WEDM process. Finally, the optimized result of BPNN with integrated SAA was compared with that obtained by an RSM approach. Comparisons of the results of the algorithms and confirmation experiments show that both RSM and BPNN/SAA methods are effective tools for the optimization of parameters in WEDM process.

Section: Introductionmentioning

confidence: 99%

Optimization of wire electrical discharge machining process parameters for cutting tungsten

Tzeng

Int J Adv Manuf Technol

et al. 2011

Advanced Multiresponse Process Optimisation

“…Yen et al (2006) designed ANN and SA-based method to perform multiresponse optimisation in injection moulding. Yang et al (2009) used ANN and SA to optimise dual-response electric discharge machining process. Chen et al (2010) presented ANN&SA-based method for the optimisation of wire electrical discharge machining in a presence of three responses.…”

Section: Multiresponse Optimisation Based On Simulated Annealingmentioning

confidence: 99%

Review of Multiresponse Process Optimisation Methods

Šibalija

Majstorovic

2015

The review of methods used for multiresponse process parameter design and similar multiobjective optimisation problems is discussed in this chapter, implying the following classification: (1) conventional methods based on statistical or mathematical techniques: (i) experimental design techniques (response surface methodology, Taguchi's robust parameter design and related approaches), and (ii) iterative mathematical search techniques; (2) non-conventional methods based on artificial intelligence techniques: (i) fuzzy logic, (ii) artificial neural networks, (iii) metaheuristic search techniques (genetic algorithm, simulated annealing, particle swarm optimisation, ant colony optimisation, tabu search, and recent evolutionary algorithms such as artificial bee colony algorithm, biogeography-based optimisation, and teaching-learning-based optimisation), and (iv) expert systems.

International Conference on Advancements and Recent Innovations in Mechanical, Production and Industrial Engineering

“…al. [30] proposed an optimization methodology for the selection of best process parameters in electrodischarge machining. There are four process parameters selected for EDM machining which are discharge current, source voltage, pulse-on time and pulse-off time.…”

Section: Simulated Annealing-mentioning

confidence: 99%

Optimization of Electro Discharge Machining of Superalloys and Composites: A Review

2015

Abstract-Electro discharge machining (EDM) processis a well-established advanced machining process for producing complex geometry with close tolerances in these materials that are extremely difficult-to-machine by conventional machining processes. EDM process offers a better alternative or sometimes the only alternative for generating accurate three dimensional complex shapes of macro, micro and nano features in difficult-tomachine materials among other advanced machining processes. The success of the EDM process depends upon the selection of appropriate process parameters. The selection of preferred process parameters plays a significant role to ensure quality of product and to reduce the machining cost in computer controlled machining process. Hence, Optimization methods applied for machining process are necessary for continuous improvement of the machining process. The objective of this paper is to offer comprehensive knowledge concerning the optimization of electro discharge machining of superalloys and composites materials.