Prediction of vibration amplitude from machining parameters by response surface methodology in end milling

Sivasakthivel, P. S.; Velmurugan, V.; Sudhakaran, R.

doi:10.1007/s00170-010-2872-3

Cited by 49 publications

(36 citation statements)

References 16 publications

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“…It is inferred that with increase of depth of cut from 2 to 10 μm, specific tool wear decreases from 225 to 100. This is because as the depth of cut increases, mechanical load between tool and workpiece increases, thus allowing diamond particles to protrude from bond matrix [5,16]. It is observed that at lower depth of cut (2 μm), specific tool wear is very high.…”

Section: Main Effectsmentioning

confidence: 77%

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Experimental studies on tool wear in μ-RUM process

Jain

Pandey

2016

Int J Adv Manuf Technol

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Tool wear in micro-manufacturing process is of paramount importance to meet stringent design requirements of workpiece in a cost-effective manner. In present work, tool wear in micro-rotary ultrasonic machining (μ-RUM) has been investigated for Ø300-μm peck drilling operation. Two sets of experiments have been conducted using electroplated hollow diamond tool and borosilicate glass as workpiece to evaluate the effects of μ-RUM parameters on tool wear. In the first set, the effects of tool-based parameters like grain size and thickness of hollow tool have been studied using full factorial design and optimum tool design was obtained. In second set, the effects of process-related parameters like spindle speed, distance traverse in each stroke, table feed rate, vibration amplitude, and vibration frequency on tool wear were studied using central rotatable composite design with optimum tool designed in first step. Analysis of variance has been used to study the significance of μ-RUM factors on tool wear . After investigation and analysis of the data, it has been concluded that thickness and grain size of hollow diamond tool had an inverse effect on tool wear. Tool with 100-μm thickness and 30-μm grain size was best possible tool for minimum tool wear. It has also been found that process parameters like traverse in each stroke, vibration amplitude, vibration frequency, and spindle speed have affected tool wear, but vibration frequency was most influencing process parameter and resulted in rapid tool wear if not selected properly.

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Section: Main Effectsmentioning

confidence: 77%

“…This is due to fact that at this feed rate of 0.3-0.5 mm/min, heat dissipation effects dominate over contact/friction between tool and workpiece. At 0.6-mm/min feed rate, tool undergoes rapid wear due to high load on tool [8,16].…”

Section: Main Effectsmentioning

confidence: 99%

Experimental studies on tool wear in μ-RUM process

Jain

Pandey

2016

Int J Adv Manuf Technol

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“…Given the high normal pressure produced by the chip on the tool rake face, the FE software has been set up adopting the Tresca's law for describing the tool-chip interaction. According to this model, the friction force saturates when the normal force on the tool rake face exceeds a fraction of the maximum admissible tangential stress = r}A/V3 (2) where T is the tangential friction stress (a FE solution parameter), TY the yield shear stress (assumed as A/\/3 according to Von Mises), and rj the friction factor (to be identified). Figure 2 shows the mesh and the boundary conditions of the 2D FE model.…”

Section: Numerical Modelmentioning

confidence: 99%

Identification of Johnson–Cook and Tresca's Parameters for Numerical Modeling of AISI-304 Machining Processes

Bosetti¹,

Bort

Bruschi

2013

Journal of Manufacturing Science and Engineering

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This paper presents a procedure for the identification of Johnson Cook model parameters and Tresca's law friction factor for orthogonal cutting of AISI 304. The process is described by a thermomechanical numerical model. The parameters are identified by minimizing the error in the prediction of cutting force, chip thickness, and chip curvature. Two optimization algorithms where tested: a pure Nelder-Mead method (NMM), and a hybrid procedure, in which the starting simplex for NMM is calculated by means of a genetic algorithm. The results emphasize the importance of the initial guess chosen in the optimization to obtain a reliable set of parameters. By using the optimized parameters in the numerical model, the cutting force, the chip thickness, and the chip curvature can be evaluated with an acceptable accuracy. The identified rheological and triboiogicai coefficients are validated for different orthogonal cutting conditions.

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“…The second order polynomial regression equation used to represent the response surface 'Y' for k factors [16] is given by (3) where b 0 is the average of responses, and b i , b ii and b ij are the coefficients which depend on the respective main and interaction effects of the parameters [17]. The statistical software SYSTAT 13 was used to determine the coefficients.…”

Section: Development Of Mathematical Model For Wear Ratementioning

confidence: 99%

Theoretical and experimental investigation of wear characteristics of aluminum based metal matrix composites using RSM

Selvi¹,

Rajasekar²

2015

J Mech Sci Technol

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The tribological properties such as wear rate, hardness of the aluminum-fly ash composite synthesized by stir casting were investigated by varying the weight % of fly ash from 5 to 20 with constant weight % of zinc and magnesium metal powder. A mathematical model was developed to predict the wear rate of aluminum metal matrix composites and the adequacy of the model was verified using analysis of variance. Scanning electron microscopy was used for the microstructure analysis which showed a uniform distribution of fly ash in the metal matrix. Energy -dispersive X-ray spectroscopy was used for the elemental analysis or chemical characterization of a sample. The results showed that addition of fly ash to aluminum based metal matrix improved both the mechanical and tribological properties of the composites. The fly ash particles improved the wear resistance of the metal matrix composites because the hardness of the samples taken increased as the fly ash content was increased.

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Prediction of vibration amplitude from machining parameters by response surface methodology in end milling

Cited by 49 publications

References 16 publications

Experimental studies on tool wear in μ-RUM process

Experimental studies on tool wear in μ-RUM process

Identification of Johnson–Cook and Tresca's Parameters for Numerical Modeling of AISI-304 Machining Processes

Theoretical and experimental investigation of wear characteristics of aluminum based metal matrix composites using RSM

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