Effect of input parameters on residual stress in dry machining of hardened steel [En31] with CBN cutting tool �Coactive Neuro �Fuzzy Interface System Approach

2011 ◽  
Vol 1 (2) ◽  
pp. 44-48
Author(s):  
M. V. R. D. Prasad ◽  
G. Ranga Janardhana
Keyword(s):  
Residual Stress ◽  
System Approach ◽  
Cutting Tool ◽  
Hardened Steel ◽  
Dry Machining ◽  
Neuro Fuzzy ◽  
Interface System ◽  
Input Parameters

A COMPARATIVE STUDY OF MULTIPLE REGRESSION AND MACHINE LEARNING TECHNIQUES FOR PREDICTION OF NANOFLUID HEAT TRANSFER

2021 ◽  
pp. 1-22
Author(s):  
Eyup Kocak ◽  
Ulku Ece Ayli ◽  
Hasmet Turkoglu
Keyword(s):  
Heat Transfer ◽  
Nusselt Number ◽  
Multiple Regression ◽  
Volume Fraction ◽  
Particle Diameter ◽  
Machine Learning Techniques ◽  
Exponential Factor ◽  
Nano Fluid ◽  
Neuro Fuzzy ◽  
Interface System

Abstract The aim of this paper is to introduce and discuss prediction power of the multiple regression technique, Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Interface System (ANFIS) methods for predicting the forced convection heat transfer characteristics of a turbulent nano fluid flow a pipe. Water and Al2O3 mixture is used as the nano fluid. Utilizing FLUENT software, numerical computations were performed with volume fraction ranging between 0.3% and 5%, particle diameter ranging between 20 and 140 nm and Reynolds number ranging between 7000 and 21000. Based on the computationally obtained results, a correlation is developed for Nusselt number using the multiple regression method. Also, based on the CFD results different ANN architectures with different number of neurons in the hidden layers and several training algorithms (Levenberg-Marquardt, Bayesian Regularization, Scaled Conjugate Gradient) are tested to find the best ANN architecture. In addition, Adaptive Neuro-fuzzy Interface System (ANFIS) is also used to predict the Nusselt number. In the ANFIS, number of clusters, exponential factor and Membership Function (MF) type are optimized. The results obtained from multiple regression correlation, ANN and ANFIS were compared. According to the obtained results, ANFIS is a powerful tool with a R2 of 0.9987 for predictions.

Effect of Ion Implantation on the Residual Stress, Tribological and Maching Behavior of Cemented Carbide Cutting Tool Inserts Coated with TiN by CVD and PVD Methods

1997 ◽  
Vol 138-140 ◽  
pp. 177-194
Author(s):  
Anthony J. Perry ◽  
James R. Treglio ◽  
Daniel E. Geist ◽  
Deepak G. Bhat ◽  
S. Prasad Boppana ◽  
...  
Keyword(s):  
Residual Stress ◽  
Ion Implantation ◽  
Cutting Tool ◽  
Cemented Carbide ◽  
Carbide Cutting Tool
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