NC end milling optimization using evolutionary computation

Tandon, Vipul; El-Mounayri, Hazim; Kishawy, Hossam A.

doi:10.1016/s0890-6955(01)00151-1

Cited by 152 publications

(51 citation statements)

References 28 publications

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“…Tandon, V.,El-Mounayri. H. and Kishawy, H., [9] optimized feed and speed CNC milling process. By using PSO technique .Rao, R. V., Savsani, V. J., and Vakharia, D. P., [10] introduced a new optimization method known as Teaching -Learning Based Optimization (TLBO).This algorithm has not only solved many bench mark design problems and given effective and efficient result compared the result with other non-traditional optimization techniques such as PSO, ACO, SA, GA, etc.…”

Section: Page 250mentioning

confidence: 99%

Optimization of Milling Conditions by Using Particle Swarm Optimization Technique: A Review

Bahirje¹,

Potdar²

2014

IJETT

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Abstract-a review of publications associated with the optimization of milling conditions by particle swarm analysis method. Milling is the most common form of machining, a material removal process, which can create a variety of features on a part by cutting away the unwanted material In order to optimize the cutting conditions, the empirical relationships between input and output variables should be established in order to predict the output. Optimization of these predictive models helps us to select appropriate input variables for achieving the best output performance. In this review paper the study is covered regarding the optimization of different input parameters and results are analyzed.

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Section: Page 250mentioning

confidence: 99%

Optimization of Milling Conditions by Using Particle Swarm Optimization Technique: A Review

Bahirje¹,

Potdar²

2014

IJETT

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“…The violation of constraint 26, to some extent, does not impose direct risk on the process. Therefore, this constraint is going to be applied in the algorithm as the following penalty function: (27) Consequently, the following fitness function has been assumed:…”

Section: Optimisation Of Final Segmentsmentioning

confidence: 99%

“…The problem of CNC operation optimisation in metalworking has been well explored in the literature. Many researchers concentrate on the determination of optimal cutting speed and feed rate at milling [1,21,27,30,31,33]. In the above-mentioned papers, these parameters are assumed to be constant for particular operation and part.…”

Section: Introductionmentioning

confidence: 99%

Optimisation of CNC routing operations of wooden furniture parts

Gawronski

2012

Int J Adv Manuf Technol

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The aim of this paper is to develop mathematical model and computational procedure for optimisation of feed rate at CNC routing operations of wooden furniture parts. The proposed approach takes into consideration special characteristics of solid wood, like changes in cutting forces and obtained surface quality along with cutting direction. A multistage optimisation procedure is developed, which employs binary search method and genetic algorithm. Because of the problem of full specification of feasible region in the form of inequalities, a fuzzy logic is used for correction of intermediate results within computational procedure. The developed method is verified by optimisation of routing operation of a typical furniture part, i.e. commode top. As a result of optimisation, the processing time is reduced by 54 %.

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“…Evolutionary computation algorithms such genetic algorithms and particle swarm optimization are usually utilized for optimization of multilayer perceptron based models. Tandon et al (2002) optimized machining parameters in end milling to minimize machining time by combining a feed forward neural network force model with particle swarm optimization.…”

Section: Literature Reviewmentioning

confidence: 99%

Feature Selection in Extrusion Beltline Moulding Process Using Particle Swarm Optimization

Bon¹,

Ogier²,

Razali³

et al. 2008

CIS

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Optimization is necessary for the control of any process to achieve better product quality, high productivity with low cost. The beltline moulding process is difficult task due to its low defects, making the material sensitive to reject. The efficient beltline moulding process involves the optimal selection of operating parameters to maximize the number of production while maintaining the required quality limiting beltline surface damage. In this research, objective is to obtain optimum process parameters, which satisfies given limit, minimizes number of defects and maximizes the productivity at the same time. A recently developed optimization algorithm called particle swarm optimization is used to find optimum process parameters. Accordingly, the results indicate that a system where multilayer perceptron is used to model and predict process outputs and particle swarm optimization is used to obtain optimum process parameters can be successfully applied to beltline moulding process through Particle Swarm Optimization (PSO). Results obtained are superior in comparison with Genetic Algorithm (GA) approach.

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NC end milling optimization using evolutionary computation

Cited by 152 publications

References 28 publications

Optimization of Milling Conditions by Using Particle Swarm Optimization Technique: A Review

Optimization of Milling Conditions by Using Particle Swarm Optimization Technique: A Review

Optimisation of CNC routing operations of wooden furniture parts

Feature Selection in Extrusion Beltline Moulding Process Using Particle Swarm Optimization

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