Multi-criteria end milling parameters optimization of AISI D2 steel using genetic algorithm

Alrashdan, Abdalla; Bataineh, Omar; Shbool, Mohammad A.

doi:10.1007/s00170-014-5921-5

Cited by 22 publications

(7 citation statements)

References 31 publications

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“…Guo et al (2012) developed a surface roughness based method to optimize the machining parameters with respect to energy minimization. Alrashdan et al (2014) proposed a multi-objective approach to optimize the process parameters of milling AISI D2 steel with maximizing surface quality while minimizing electrical energy consumption. Camposeco-Negrete (2015) applied the response surface method to optimize the parameters of turning process in terms of energy and surface roughness.…”

Section: Introductionmentioning

confidence: 99%

An operation-mode based simulation approach to enhance the energy conservation of machine tools

Guo

Duflou

Qian

et al. 2015

Journal of Cleaner Production

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Section: Introductionmentioning

confidence: 99%

An operation-mode based simulation approach to enhance the energy conservation of machine tools

Guo

Duflou

Qian

et al. 2015

Journal of Cleaner Production

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“…The effect of different machining parameters was studied by Mori et al 21 to improve the energy efficiency of end-milling, drilling and face-milling processes. Alrashdan et al 22 used multi-criteria optimisation technique to optimise the cutting conditions of speed, depth of cut and feed in end-milling of AISI D2 steel by surface roughness and energy consumption.…”

Section: Introductionmentioning

confidence: 99%

Multi-objective optimisation for minimum quantity lubrication assisted milling process based on hybrid response surface methodology and multi-objective genetic algorithm

Mumtaz

Imran

et al. 2019

Advances in Mechanical Engineering

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Parametric modelling and optimisation play an important role in choosing the best or optimal cutting conditions and parameters during machining to achieve the desirable results. However, analysis of optimisation of minimum quantity lubricationassisted milling process has not been addressed in detail. Minimum quantity lubrication method is very effective for cost reduction and promotes green machining. Hence, this article focuses on minimum quantity lubrication-assisted milling machining parameters on AISI 1045 material surface roughness and power consumption. A novel low-cost power measurement system is developed to measure the power consumption. A predictive mathematical model is developed for surface roughness and power consumption. The effects of minimum quantity lubrication and machining parameters are examined to determine the optimum conditions with minimum surface roughness and minimum power consumption. Empirical models are developed to predict surface roughness and power of machine tool effectively and accurately using response surface methodology and multi-objective optimisation genetic algorithm. Comparison of results obtained from response surface methodology and multi-objective optimisation genetic algorithm depict that both measured and predicted values have a close agreement. This model could be helpful to select the best combination of end-milling machining parameters to save power consumption and time, consequently, increasing both productivity and profitability.

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“…Bharathi and Baska [8] discussed the methods for the selection of machining parameters in a machining process. Abdalla Alrashdan et al [9] analysed the most important machining parameters (spindle speed, feed rate, and depth of cut) which affect the machining process. Ahilana and Kumanan [10] conducted experiments based on the central composite design (CCD).…”

Section: Introductionmentioning

confidence: 99%

An Integrated Approach of RSM and MOGA for the Prediction of Temperature Rise and Surface Roughness in the End Milling of Al 6061-T6

Zeelanbasha¹,

Senthil²,

Kumar

2018

TFAMENA

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Cutting temperature, machining parameters, workpiece material, and cutting tool geometry have a significant influence on the achievement of the desired quality of product at a satisfactory cost. The aim of the present study was to develop an empirical model for predicting temperature rise (Tr) and surface roughness (Ra) in terms of spindle speed (N), feed rate (F), axial depth of cut (D a), radial depth of cut (D r), and radial rake angle (γ). The experiment was conducted on Al 6061-T6 by using a high-speed steel (HSS) end cutter based on the central composite design of response surface methodology (RSM). A second order mathematical model in terms of machining parameters was developed. The Analysis of Variance (ANOVA) was used to study the performance characteristics in the machining process. The values of Prob>F less than 0.05 indicate that the model terms are significant. The experimental results indicate that the formation of surface defect in the end milling of Al 6061-T6 results from the re-deposited tool material, plucking, feed marks, micro-pits, and chip layer formation. The high quality of the surface texture is obtained in the combined conditions of high spindle speed, optimal feed rate, lower axial and radial depths of cut, and radial rake angle. Multi objective genetic algorithm (MOGA) has been applied to optimize the machining parameters that simultaneously minimize temperature rise and surface roughness. A set of Pareto-optimal solutions provides flexibility to the manufacturer and the process engineer to select the best setting based on the quality requirements and applications. A verification and validation process shows that the predicted values were found to be in good agreement with the observed values.

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Multi-criteria end milling parameters optimization of AISI D2 steel using genetic algorithm

Cited by 22 publications

References 31 publications

An operation-mode based simulation approach to enhance the energy conservation of machine tools

An operation-mode based simulation approach to enhance the energy conservation of machine tools

Multi-objective optimisation for minimum quantity lubrication assisted milling process based on hybrid response surface methodology and multi-objective genetic algorithm

An Integrated Approach of RSM and MOGA for the Prediction of Temperature Rise and Surface Roughness in the End Milling of Al 6061-T6

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