Performance-based optimization of multi-pass face milling operations using Tribes

Onwubolu, Godfrey C.

doi:10.1016/j.ijmachtools.2005.07.041

Cited by 42 publications

(6 citation statements)

References 19 publications

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“…Alauddin et al [1] predict tool life by using quadratic and linear regression models. Linear regression model was also found fit for the responses of surface roughness by Oktem et al [29]. Multiple linear regression modelsT haveT beenT appliedT forT predictionT ofT toolT wearT andT surfaceT finishT byT GopalsamyT etT al.T [43].T MustafaT [58]T predictedT surfaceT finishT andT forceT modelsT byT usingT TMT andT regressionT analysisT forT millingT ofT welded regions.…”

Section: Regression Analysismentioning

confidence: 95%

State of Art Optimization Techniques for Machining Parameters Optimization during Milling

Kumar¹,

Gupta²,

Chandna³

2019

IJEAT

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Optimization of machining parameters becomes more important; when high capital cost NC machines are employed for high precision and efficient machining. Minimizations of unit cost and time along with minimum tool and workpiece deflection, improved surface finish & tool life under certain boundary conditions are key objectives of the optimization problem. Optimization methods for milling include in-process parameters relationship with machining objectives and determination of optimal cutting conditions. Development of costeffective mathematical models is still a challenging task. However, there has been a considerable improvement in the techniques of modeling and optimization during the last two decades. In this paper, several modeling and optimization techniques reported for the milling operations have been reviewed and are for milling, classified for different criteria. Issues related to performance of several evolutionary algorithms, machining parameters, objectives and constraints have also been identified. From the survey of optimization techniques during milling operations it has been found that search techniques perform better than experimental approaches for optimization of process parameters. However, the experimental techniques play a vital role in prediction models for different machining objectives

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Section: Regression Analysismentioning

confidence: 95%

State of Art Optimization Techniques for Machining Parameters Optimization during Milling

Kumar¹,

Gupta²,

Chandna³

2019

IJEAT

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“…This feature of ATA and the position changing policy used in PSO are alike. Built upon the aforementioned concepts, the running flow of ATA can be described as follows (Onwubolu 2006;Chen et al 2006Chen et al , 2012Coelho and Bernert 2009):…”

Section: Fundamentals Of Artificial Tribe Algorithmmentioning

confidence: 99%

Emerging Biology-based CI Algorithms

Xing

Gao²

2013

Innovative Computational Intelligence: A Rough Guide to 134 Clever Algorithms

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In this chapter, a group of (more specifically 56 in total) emerging biology-based computational intelligence (CI) algorithms are introduced. We first, in Sect. 17.1, describe the organizational structure of this chapter. Then, from Sects. 17.2 to 17.57, each section is dedicated to a specific algorithm which falls within this category, respectively. The fundamentals of each algorithm and their corresponding performances compared with other CI algorithms can be found in each associated section. Finally, the conclusions drawn in Sect. 17.58 closes this chapter.

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“…Onwubolu & G.C [6] however used the Tribes optimization for determination of cutting parameters in multi pass milling process to obtain better results for a particular context. From the review of various papers, the optimization on milling process has started only in recent past.…”

Section: Literature Reviewmentioning

confidence: 99%

Optimization of Cutting Conditions in End Milling Process with the Approach of Particle Swarm Optimization

Pare

Agnihotri

Krishna

2011

IJMIE

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Milling is one of the progressive enhancements of miniaturized technologies which has wide range of application in industries and other related areas. Milling like any metal cutting operation is used with an objective of optimizing surface roughness at micro level and economic performance at macro level. In addition to surface finish, modern manufacturers do not want any compromise on the achievement of high quality, dimensional accuracy, high production rate, minimum wear on the cutting tools, cost saving and increase of the performance of the product with minimum environmental hazards. In order to optimize the surface finish, 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 paper, four input variables are selected and surface roughness is taken as output variable. Particle swarm optimization technique is used for finding the optimum set of values of input variables and the results are compared with those obtained by GA optimization in the literature.

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Performance-based optimization of multi-pass face milling operations using Tribes

Cited by 42 publications

References 19 publications

State of Art Optimization Techniques for Machining Parameters Optimization during Milling

State of Art Optimization Techniques for Machining Parameters Optimization during Milling

Emerging Biology-based CI Algorithms

Optimization of Cutting Conditions in End Milling Process with the Approach of Particle Swarm Optimization

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