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

2010 ◽  
Vol 53 (5-8) ◽  
pp. 453-461 ◽  
Author(s):  
P. S. Sivasakthivel ◽  
V. Velmurugan ◽  
R. Sudhakaran
Keyword(s):  
Response Surface Methodology ◽  
Response Surface ◽  
Vibration Amplitude ◽  
End Milling ◽  
Machining Parameters

scholarly journals Multi-response Optimization of End Milling Parameters for Al-Zn-Mg/SiC Co-continuous Composite Using Response Surface Methodology

2019 ◽  
Vol 25 (4) ◽  
pp. 471-477
Author(s):  
Prasanth ACHUTHAMENON SYLAJAKUMARI ◽  
Ramesh RAMAKRISHNASAMY ◽  
Gopalakrishnan PALANIAPPAN ◽  
Ramu MURUGAN
Keyword(s):  
Response Surface Methodology ◽  
Response Surface ◽  
Regression Models ◽  
Ceramic Composite ◽  
End Milling ◽  
Depth Of Cut ◽  
Machining Parameters ◽  
Linear Regression Models ◽  
Desirability Analysis ◽  
Response Optimization

A co-continuous ceramic composite (C4) was manufactured by gravity infiltration. The effect of varying machining parameters namely, speed, feed and depth of cut during end milling of C4, on the multi-responses of surface roughness, tool wear and depth of cut was investigated using response surface methodology. Non-linear regression models were generated and optimal machining parameters were determined using desirability analysis. Confirmation experiments performed, validated the models with a ± 5 % error in prediction.

Surface Roughness Prediction in End Milling of Machinable Glass Ceramic and Optimization by Response Surface Methodology

2011 ◽  
Vol 189-193 ◽  
pp. 1376-1381
Author(s):  
Moola Mohan Reddy ◽  
Alexander Gorin ◽  
Khaled A. Abou El Hossein
Keyword(s):  
Surface Roughness ◽  
Response Surface Methodology ◽  
Response Surface ◽  
Glass Ceramic ◽  
End Milling ◽  
Removal Rate ◽  
Cutting Speed ◽  
Lower Surface ◽  
Depth Of Cut ◽  
Machining Parameters

This paper presents the prediction of a statistically analyzed model for the surface roughness,R_a of end-milled Machinable glass ceramic (MGC). Response Surface Methodology (RSM) is used to construct the models based on 3-factorial Box-Behnken Design (BBD). It is found that cutting speed is the most significant factor contributing to the surface roughness value followed by the depth of cut and feed rate. The surface roughness value decreases for higher cutting speed along with lower feed and depth of cut. Additionally, the process optimization has also been done in terms of material removal rate (MRR) to the model’s response. Ideal combinations of machining parameters are then suggested for common goal to achieve lower surface roughness value and higher MRR.

Surface Roughness Prediction in the End Milling Parameters of Aluminum Nitride Ceramic by Response Surface Methodology

2010 ◽  
Vol 154-155 ◽  
pp. 626-633
Author(s):  
Moola Mohan Reddy ◽  
Alexander Gorin ◽  
Khaled A. Abou-El-Hossein
Keyword(s):  
Surface Roughness ◽  
Response Surface Methodology ◽  
Response Surface ◽  
End Milling ◽  
Cubic Boron Nitride ◽  
Cutting Speed ◽  
Machining Parameters ◽  
Roughness Model ◽  
Roughness Prediction ◽  
Grinding Tool

The present experimental study aimed to examine the selected machining parameters on Surface roughness in the machining of alumina nitride ceramic. The influence of cutting speed and feed rate were determined in end milling by using Cubic boron nitride grinding tool. The predictive surface roughness model has been developed by response surface methodology. The response surface contours with respect to input parameters are presented with the help of Design expert software. The adequacy of the model was tested by ANOVA.

Quantification of tool chatter and metal removal rate using wavelet denoising and statistical approach

2018 ◽  
Vol 49 (2) ◽  
pp. 62-81 ◽  
Author(s):  
Shailendra Kumar ◽  
Bhagat Singh
Keyword(s):  
Response Surface Methodology ◽  
Response Surface ◽  
Metal Removal ◽  
Removal Rate ◽  
Statistical Significance ◽  
Depth Of Cut ◽  
Machining Parameters ◽  
Metal Removal Rate ◽  
Chatter Index ◽  
Tool Chatter

Tool chatter is an unavoidable phenomenon encountered in machining processes. Acquired raw chatter signals are contaminated with various types of ambient noises. Signal processing is an efficient technique to explore chatter as it eliminates unwanted background noise present in the raw signal. In this study, experimentally recorded raw chatter signals have been denoised using wavelet transform in order to eliminate the unwanted noise inclusions. Moreover, effect of machining parameters such as depth of cut ( d), feed rate ( f) and spindle speed ( N) on chatter severity and metal removal rate has been ascertained experimentally. Furthermore, in order to quantify the chatter severity, a new parameter called chatter index has been evaluated considering aforesaid denoised signals. A set of 15 experimental runs have been performed using Box–Behnken design of experiment. These experimental observations have been used to develop mathematical models for chatter index and metal removal rate considering response surface methodology. In order to check the statistical significance of control parameters, analysis of variance has been performed. Furthermore, more experiments are conducted and these results are compared with the theoretical ones in order to validate the developed response surface methodology model.

Parameter Optimization of Ball End Milling Process on Inconel 718 Using RSM and TLBO Algorithm

2015 ◽  
Vol 15 (3) ◽  
pp. 293-300 ◽  
Author(s):  
Nandkumar N. Bhopale ◽  
Nilesh Nikam ◽  
Raju S. Pawade
Keyword(s):  
Surface Roughness ◽  
Response Surface Methodology ◽  
Response Surface ◽  
Surface Integrity ◽  
Inconel 718 ◽  
Tool Path ◽  
End Milling ◽  
Spindle Speed ◽  
Depth Of Cut ◽  
Ball End Milling

AbstractThis paper presents the application of Response Surface Methodology (RSM) coupled with Teaching Learning Based Optimization Technique (TLBO) for optimizing surface integrity of thin cantilever type Inconel 718 workpiece in ball end milling. The machining and tool related parameters like spindle speed, milling feed, axial depth of cut and tool path orientation are optimized with considerations of multiple response like deflection, surface roughness, and micro hardness of plate. Mathematical relationship between process parameters and deflection, surface roughness and microhardness are found out by using response surface methodology. It is observed that after optimizing the process that at the spindle speed of 2,000 rpm, feed 0.05 mm/tooth/rev, plate thickness of 5.5 mm and 15° workpiece inclination with horizontal tool path gives favorable surface integrity.

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