Particle swarm optimization technique for determining optimal machining parameters of different work piece materials in turning operation

Raja, S. Bharathi; Baskar, N.

doi:10.1007/s00170-010-2958-y

Cited by 82 publications

(14 citation statements)

References 25 publications

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“…This algorithm has the advantages of good accuracy, fast convergence and easy implementation and is adopted in this article. 18 Each particle represents a set of controller parameters. The algorithm first randomly generates a particle swarm, and the optimisation performance of each particle is evaluated using a multi-objective function.…”

Section: Multi-objective Nonlinear Optimisationmentioning

confidence: 99%

Investigation of the controller parameter optimisation for a servomechanism

Bao

Zhang

Mei

et al. 2015

Proceedings of the Institution of Mechanical Engineers, Part B:

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A novel controller parameter optimisation method to effectively improve the motion accuracy of a servomechanism is proposed in this article. This method overcomes the drawbacks of conventional controller parameter optimisation methods, such as inefficiency and dependence on experience. The servomechanism model was established, and a performance analysis was performed. Moreover, the design requirements were developed. The robust stability was adopted to solve the problem induced by great variations in the modelling elements. Subsequently, a multi-objective nonlinear optimisation problem was developed and solved by an intelligent optimisation algorithm. To achieve the desired performance, the multi-objective optimisation function adopted a comprehensive command response performance function and an external disturbance rejection response function. To validate the effectiveness and feasibility of this method by simulation and experiment, an open servomechanism platform was established. The simulated and experimental results show that the tracking error, protrusion error and performance indicators are all reduced greatly under different working conditions. This method can effectively improve the motion accuracy and has the advantages of high efficiency, high practicality and high flexibility.

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Section: Multi-objective Nonlinear Optimisationmentioning

confidence: 99%

Investigation of the controller parameter optimisation for a servomechanism

Bao

Zhang

Mei

et al. 2015

Proceedings of the Institution of Mechanical Engineers, Part B:

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show abstract

“…Tables I and II that the values range from the 10 3 to 10 -2 grades, therefore it is extremely difficult to find one single optimization polynomial that would sufficiently describe all the dependent values f (x 1 , x 2 , x 3 ) which are in fact cutting force, surface roughness, and tool life. Several models have been tested, yet the best polynomial that proved to be as accurate as possible for the proposed paper turned out to be composed of ten coefficients that affected the influences of independent variables x 1 , x 2 , and x 3 : (10) where the symbols stand for: f (x 1 , x 2 , x 3 ) -output parameters F c (cutting force), R a (surface roughness), or T (tool life), x 1 -cutting speed v c , x 2 -feed rate f, x 3 -cutting depth a p , k 1 , k 2 , …, k 10 -coefficients.…”

Section: Prediction Modelmentioning

confidence: 99%

“…The PSO method has been extensively researched by several researchers previously [1][2][3], therefore PSO serves as a good comparison regarding GSA research when determining its accuracy and run parameters, including optimization time. Genetic programming and genetic algorithm approaches are also popular for optimization methods [4,5].…”

Section: Introductionmentioning

confidence: 99%

Modelling of a Turning Process Using the Gravitational Search Algorithm

Hrelja¹,

Klančnik²,

Balič³

et al. 2014

Int. j. simul. model.

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This paper proposes the modelling of a turning process using a gravitational search algorithm (GSA). GSA is an optimization algorithm based on Newton's law of universal gravitation and mass interactions. In order to sufficiently describe the turning process, at least three independent variables are required: cutting speed, feed-rate, and cutting depth. Independent variables have impacts on dependent variables, which were in our case cutting force, surface roughness, and tool-life. The values of independent and dependent variables obtained by measurements serve as a knowledge database for feeding the GSA optimization process. During our research the GSA was used for optimizing the numerical coefficients of predefined polynomial models for describing the observed output variables. The accuracies of the obtained prediction models were proved by means of a testing data set that was excluded from the training data. The research showed that the obtained results were comparable with the other optimization algorithms such as particle swarm optimization (PSO). However, the optimization time required for GSA optimization was, in certain cases, significantly shorter.

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“…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. Bharti R.S et al [11] suggested the machining performance viz. machining time and surface roughness in CNC machining after conducting experiments on brass, aluminium, copper, and mild steel.…”

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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Particle swarm optimization technique for determining optimal machining parameters of different work piece materials in turning operation

Cited by 82 publications

References 25 publications

Investigation of the controller parameter optimisation for a servomechanism

Investigation of the controller parameter optimisation for a servomechanism

Modelling of a Turning Process Using the Gravitational Search Algorithm

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

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