Multi-Objective Optimization of Abrasive Flow Machining Processes Using Polynomial Neural Networks and Genetic Algorithms

Alitavoli, M.; Nariman-Zadeh, N.; Khakhali, A.; Mehran, mohammad Mohseni

doi:10.1080/10910340600996126

Cited by 31 publications

(9 citation statements)

References 27 publications

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“…Its application areas include aircraft valves bodies and spools, turbine components, automotive parts, finishing of dies, medical instruments, electronic components etc. Ali-Tavoli et al (2007) adopted group method of data handling (GMDH)-type NN and GA to study the effects of number of cycles and abrasive concentration of an AFM process on MRR and SR. Applying real-coded GA, performed optimization of an AFM process.…”

Section: Abrasive Flow Machining Processmentioning

confidence: 99%

Non-conventional optimization techniques in optimizing non-traditional machining processes: A review

Debroy¹,

Chakraborty²

2013

msl

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With the ever-increasing demands for high surface finish and complex shape geometries, conventional metal removal methods are now being replaced by non-traditional machining (NTM) processes. These NTM processes use energy in its direct form to remove materials in the form of atoms or molecules to obtain the required accuracy and burr-free machined surface. In order to exploit the optimal capabilities of the NTM processes, it is often required to determine the best possible combinations of their controllable parameters. Different nonconventional optimization techniques have been used for dealing with these process optimization problems because of their inherent advantages and capabilities for arriving at the almost global optimal solutions. This paper reviews the applications of different nonconventional optimization techniques for parametric optimization of NTM processes. It is observed that electrical discharge machining processes have been optimized most number of times, followed by wire electrical discharge machining processes. In most of the cases, the past researchers have preferred to maximize material removal rate. Genetic algorithm has been found to be the most popular non-conventional optimization technique.

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Section: Abrasive Flow Machining Processmentioning

confidence: 99%

Non-conventional optimization techniques in optimizing non-traditional machining processes: A review

Debroy¹,

Chakraborty²

2013

msl

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“…Recently, there has been a great increase in the application of metamodels instead of the complex analytics models that are limited by assumptions [33][34]. Several metamodeling techniques with various degrees of complexity have been extensively applied, such as the response surface methodology [35][36][37][38], artificial neural network [39][40][41][42], radial basis function [43][44][45], and kriging [46][47][48][49]. Some of these techniques are suitable for global approximations, i.e., can be used for representing the complete design space, while others are more suitable for local approximations of a part of the design space.…”

Section: Introductionmentioning

confidence: 99%

“…The existing studies to predict springback in the air bending process mostly use training data from experiments with real-life systems. Consequently, these studies considered only inadequate materials and tool geometry [37,[39][40][41]50].…”

Section: Introductionmentioning

confidence: 99%

Prediction of Springback in the Air Bending Process Using a Kriging Metamodel

Khadra

El-Morsy

2016

Eng. Technol. Appl. Sci. Res.

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This paper addresses the use of the kriging‏ approach to predict the springback in the air bending process. The materials and the geometrical parameters, which significantly affect the springback, were considered as inputs, and the springback angle was considered as the response. A verified nonlinear finite element model was used to generate the training data required to create the kriging‏ metamodel. The training examples were selected based on computer-generated D-optimal designs. A comparison between the kriging approaches and the response surface methodology is conducted and discussed. The results showed that kriging accurately predicts the finite element springback results. Comparing the accuracy of kriging with a response surface methodology shows that kriging with a 2nd degree polynomial and exponential correlation function predicts the springback more accurately than the response surface methodology.

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“…In metal cutting, classical solutions of the typical optimization problem involve maximizing material removal rate (MRR) subject to constraints on such parameters as speed, feed, and tool life (e.g., [16]). GA techniques have gained popularity in many applications including those geared towards solving machining-related multi-objective optimization problems [17][18][19]. Wang and Jawahir [20] developed a comprehensive optimization criterion for turning operations considering the effect of progressive tool wear and using genetic algorithms to select the best cutting conditions and tool geometry.…”

Section: Introductionmentioning

confidence: 99%

A methodology for the optimization of PCD compact core drilling in basalt rock

Hamade

Pušavec

Manthri

et al. 2011

Int J Adv Manuf Technol

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This work presents an optimization technique using genetic algorithm for efficient core drilling in basalt rock. Optimization of the compact core-drilling problem is based on maximizing a desirability function which accounts for (a) maximizing the drilling feed while minimizing toolwear progression, (b) minimizing the thrust force and torque (power), and (c) satisfying realistic constraints related to process parameters. The resulting set of optimized cutting parameters is sought in order to make the tool last longer while effectively drilling with high productivity. A room temperature model to relate the experimental data on changing drill forces and torques required by the progressive tool wear, and developed in a previous paper, is used in this study.

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Multi-Objective Optimization of Abrasive Flow Machining Processes Using Polynomial Neural Networks and Genetic Algorithms

Cited by 31 publications

References 27 publications

Non-conventional optimization techniques in optimizing non-traditional machining processes: A review

Non-conventional optimization techniques in optimizing non-traditional machining processes: A review

Prediction of Springback in the Air Bending Process Using a Kriging Metamodel

A methodology for the optimization of PCD compact core drilling in basalt rock

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