scholarly journals Workpiece Surface Temperature for In-process Surface Roughness Prediction using Response Surface Methodology

2011 ◽  
Vol 11 (2) ◽  
pp. 308-315 ◽  
Author(s):  
Adeel H. Suhail ◽  
N. Ismail ◽  
S.V. Wong ◽  
N.A. Abdul Jali
Keyword(s):  
Surface Roughness ◽  
Response Surface Methodology ◽  
Surface Temperature ◽  
Response Surface ◽  
Workpiece Surface ◽  
Surface Roughness Prediction ◽  
Roughness Prediction

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.

scholarly journals Investigation on Ti-6Al-4V Alloy Surface Characteristics Using Nano Grinding Wheel by Response Surface Methodology (RSM) with Artificial Neural Networking (ANN)

2020 ◽  
Author(s):  
Deborah Serenade Stephen ◽  
Sethuramalingam Prabhu
Keyword(s):  
Surface Roughness ◽  
Response Surface Methodology ◽  
Response Surface ◽  
Removal Rate ◽  
Research Work ◽  
Surface Characteristics ◽  
Grinding Wheel ◽  
Ti Alloy ◽  
Roughness Prediction ◽  
Artificial Neural

Abstract In this research work, surface characteristics of Ti-6Al-4V alloy have been investigated and the grinding process has been optimized using nano grinding wheel. Experiments have been conducted using L27 full factorial design. Surface roughness prediction model in nano grinding wheel for Ti alloy was developed using Response Surface Methodology (RSM) and compared with the model using Artificial Neural Network (ANN) methodology to predict the experimental behavior of the system. Grinding wheels with and without 3% nano Al2O3 powders were fabricated and their surface characteristics like surface roughness, material removal rate (MRR) and temperature were measured. Grinding was carried out on grade 5 Ti alloy with different wheels by varying input parameters. On comparing experimental and predicted results, it was found that the empirical values of surface roughness were close to the predicted values by 5%.

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