Optimizing Surface Roughness in Face Milling Using a New Meta-Heuristic Method of Harmony Search

2010 ◽  
Author(s):  
M. R. Razfar ◽  
R. Farshbaf Zinati ◽  
M. Haghshenas
Keyword(s):  
Neural Network ◽  
Surface Roughness ◽  
Artificial Neural Network ◽  
Stainless Steel ◽  
Search Algorithm ◽  
Harmony Search ◽  
Harmony Search Algorithm ◽  
Face Milling ◽  
Statistical Experimental Design ◽  
Artificial Neural

The focus of this study is on a new approach for determination of the optimal cutting parameters leading to minimum surface roughness in face milling of X20Cr13 stainless steel by coupling Neural Network (NN) and Harmony Search (HS) algorithm. In this regard, advantages of statistical experimental design technique, experimental measurements, artificial neural network and Harmony Search algorithm were exploited in an integrated manner. For this purpose, numerous experiments on X20Cr13 stainless steel were conducted to obtain surface roughness values. A predictive model for surface roughness was created using a feed forward artificial neural network exploiting experimental data. The optimization problem was solved by Harmony Search algorithm. Additional experiments were performed to validate optimum surface roughness value predicted by HS algorithm. From the obtained results, it is clearly seen that the Harmony Search algorithm is an efficient and accurate method in approaching the global minimum of surface roughness in face milling.

Prediction of machining performance using RSM and ANN models in hard turning of martensitic stainless steel AISI 420

2019 ◽  
Vol 233 (13) ◽  
pp. 4439-4462 ◽  
Author(s):  
Abderrahmen Zerti ◽  
Mohamed Athmane Yallese ◽  
Oussama Zerti ◽  
Mourad Nouioua ◽  
Riad Khettabi
Keyword(s):  
Neural Network ◽  
Surface Roughness ◽  
Artificial Neural Network ◽  
Stainless Steel ◽  
Response Surface Methodology ◽  
Martensitic Stainless Steel ◽  
Machining Parameters ◽  
Steel Aisi ◽  
Artificial Neural ◽  
Stainless Steel Aisi

The purpose of this experimental work is to study the impact of the machining parameters ( Vc, ap, and f) on the surface roughness criteria ( Ra, Rz, and Rt) as well as on the cutting force components ( Fx, Fy, and Fz), during dry turning of martensitic stainless steel (AISI 420) treated at 59 hardness Rockwell cone. The machining tests were carried out using the coated mixed ceramic cutting-insert (CC6050) according to the Taguchi design (L25). Analysis of the variance (ANOVA) as well as Pareto graphs made it possible to quantify the contributions of ( Vc, ap, and f) on the output parameters. The response surface methodology and the artificial neural networks approach were used for output modeling. Finally, the optimization of the machining parameters was performed using desirability function (DF) minimizing the surface roughness and the cutting forces simultaneously. The results indicated that the roughness is strongly affected by the feed rate ( f) with contributions of (80.71%, 80.26%, and 81.80%) for ( Ra, Rz, and Rt) respectively, and that the depth of cut ( ap) is the factor having the major influence on the cutting forces ( Fx = 53.76%, Fy = 50.79%, and Fz = 65.31%). Furthermore, artificial neural network and response surface methodology models correlate very well with experimental data. However, artificial neural network models show better accuracy. The optimum machining setting for multi-objective optimization is Vc = 80 m/min, f = 0.08 mm/rev and ap = 0.141 mm.

Constrained Optimization of Surface Roughness in Longitudinal Turning via Novel Modified Harmony Search

2011 ◽  
Author(s):  
Reza Farshbaf Zinati ◽  
Mohammad Reza Razfar
Keyword(s):  
Surface Roughness ◽  
Search Algorithm ◽  
Harmony Search ◽  
Harmony Search Algorithm ◽  
Cutting Condition ◽  
Aisi 1045 Steel ◽  
Aisi 1045 ◽  
Artificial Neural ◽  
Longitudinal Turning ◽  
1045 Steel

The present research deals with a modified optimization algorithm of harmony search coupled with artificial neural networks (ANNs) to predict the optimal cutting condition. To this end, several experiments were carried out on AISI 1045 steel to attain required data for training of ANNs. Feed forward artificial neural network was utilized to create predictive models of surface roughness and cutting forces exploiting experimental data, and Modified Harmony Search algorithm (MHS) was used to find the constrained optimum of the surface roughness. Furthermore, Simple Harmony Search algorithm (SHS) and Genetic Algorithm (GA) were used for solving the same optimization problem to illustrate the capabilities of MHS algorithm. The obtained results demonstrate that MHS algorithm is more effective and authoritative in approaching the global solution than the SHS algorithm and GA.

Artificial Intelligence Model of Surface Roughness for End Milling Operation of Steel and its Verification by Genetic Algorithm

2011 ◽  
Vol 110-116 ◽  
pp. 3459-3464
Author(s):  
Mohammed Anayet Ullah Patwari ◽  
A.K.M. Nurul Amin
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Surface Roughness ◽  
Artificial Neural Network ◽  
End Milling ◽  
Depth Of Cut ◽  
Efficient Production ◽  
Statistical Experimental Design ◽  
Artificial Neural ◽  
Coated Carbide

Surface roughness is important for evaluating the machined surface quality. In this work, an Artificial Neural Network (ANN) surface roughness prediction model was developed by coupling it with Response Surface Methodology (RSM). For this interpretation, advantages of statistical experimental design techniques, experimental measurements, and artificial neural network were exploited in an integrated manner. Cutting experiments were designed based on small centre composite design technique to develop a RSM model. The input cutting parameters were: cutting speed, feed, and axial depth of cut, and the output parameter was surface roughness. The predictive model was created using a feed-forward back-propagation neural network exploiting the experimental data. The network was trained with pairs of inputs/outputs datasets generated by end milling medium carbon steel with TiN coated carbide inserts. The model can be used for the analysis and prediction of the complex relationships between cutting conditions and surface roughness, in metal-cutting operations, with the ultimate goal of efficient production. The ANN model was verified with the optimized parameters predicted by a coupled genetic algorithm (GA) and RSM technique also developed by the authors.

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