Influence of milling strategy on the surface roughness in ball end milling of the aluminum alloy Al7075-T6

Vakondios, Dimitrios; Kyratsis, Panagiotis; Yaldız, Süleyman; Antoniadis, Aristomenis

doi:10.1016/j.measurement.2012.03.001

Cited by 63 publications

(37 citation statements)

References 12 publications

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“…Depth of cut influences the temperature over speed and feed rate. Dimitrios Vakondios et al [6] have examined the impact of various ball milling strategy selection on surface roughness of an Al7075-T6 alloy. In this paper, a number of experiments testing with different cutting conditions including axial and radial depth of cut and feed rate were performed and further analyzed using regression analysis and analysis of variance.…”

Section: IImentioning

confidence: 99%

A Comprehensive Study on the Process Parameters for Machining Aluminium 7075

P¹

2017

IJRASET

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This paper attempts to make a detail study on the machining parameters of Aluminium 7075 either in its pure form or in the form of composites. This grade of aluminium is known for its application in aerospace industry and hence reviewing about its machining parameters will lead to more development in the field of production. Surface roughness is a phenomenon which affects the product gradually and hence its influence on Al 7075 is important to deal with. The process parameters of speed, feed and depth of cut are the common interest in this study and their role in the various design of experiments techniques. From the discussions, it is revealed that feed rate is the most influencing parameter amongst all and hence this particular parameter could be very crucial in the scientific investigation pertaining to the use of Aluminium 7075.

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Section: IImentioning

confidence: 99%

A Comprehensive Study on the Process Parameters for Machining Aluminium 7075

P¹

2017

IJRASET

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“…In the scientific literature, there are studies based on the mathematical modeling such: [3] and [4], in which the implementation of the Taguchi method was analyzed in order to improve the product quality; [5] which used the Response Surface Methodology (RSM) as optimization technique, [6] used the regression analysis and [7] which used ANOVA and the regression analysis. The regression analysis allows to use the experimental data to calculate the equation coefficients that are describing the studied system at any point in the experimentally range.…”

Section: Introductionmentioning

confidence: 99%

Using regression analysis method to model and optimize the quality of chip-removing processed metal surfaces

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Pop²

2017

MATEC Web Conf.

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Abstract. The paper aim is to identify based on regression analysis, the quantitative relationship between the cutting process parameters (cutting speed, cutting depth and feed per tooth) and the arithmetic mean deviation of the surface profile, measured longitudinally and transversely on the cutting feed direction, which describe the system at any point in the chosen experimentally studied range. The equations coefficients means the influence of the variables on the pursued answer.

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“…Subramanian et al [8] used RSM to predict the surface roughness of Al7075-T651 work piece with high-speed steel end milling in terms of tool geometry. Vakondios et al [9] studied the effects of tool angles on surface roughness for the milling of Al7075 T6 by using ball nose end mills. Kurt et al [10] performed a grey relational analysis in the milling of Al7075-T651 to investigate and discuss the measurement errors in surface roughness.…”

Section: Introductionmentioning

confidence: 99%

Optimal Prediction and Design of Surface Roughness for CNC Turning of Al7075-T6 by Using the Taguchi Hybrid Qpso Algorithm

Chen

Huang

Yang

et al. 2016

Transactions of the Canadian Society for Mechanical Engineering

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This paper combines the Taguchi-based response surface methodology (RSM) with a multi-objective hybrid quantum-behaved particle swarm optimization (MOHQPSO) to predict the optimal surface roughness of Al7075-T6 workpiece through a CNC turning machining. First, the Taguchi orthogonal array L27 (36) was applied to determine the crucial cutting parameters: feed rate, tool relief angle, and cutting depth. Subsequently, the RSM was used to construct the predictive models of surface roughness (Ra, Rmax, and Rz). Finally, the MOHQPSO with mutation was used to determine the optimal roughness and cutting conditions. The results show that, compared with the non-optimization, Taguchi and classical multi-objective particle swarm optimization methods (MOPSO), the roughness Ra using MOHQPSO along the Pareto optimal solution are improved by 68.24, 59.31 and 33.80%, respectively. This reveals that the predictive models established can improve the machining quality in CNC turning of Al7075-T6.

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Influence of milling strategy on the surface roughness in ball end milling of the aluminum alloy Al7075-T6

Cited by 63 publications

References 12 publications

A Comprehensive Study on the Process Parameters for Machining Aluminium 7075

A Comprehensive Study on the Process Parameters for Machining Aluminium 7075

Using regression analysis method to model and optimize the quality of chip-removing processed metal surfaces

Optimal Prediction and Design of Surface Roughness for CNC Turning of Al7075-T6 by Using the Taguchi Hybrid Qpso Algorithm

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