scholarly journals Optimization of Machining Parameters in Milling Process of Inconel 718 under Surface Roughness Constraints

2021 ◽  
Vol 11 (5) ◽  
pp. 2137 ◽  
Author(s):  
Tian-Yau Wu ◽  
Chi-Chen Lin
Keyword(s):  
Neural Network ◽  
Surface Roughness ◽  
Artificial Neural Network ◽  
Inconel 718 ◽  
Cutting Parameters ◽  
Pso Algorithm ◽  
Milling Process ◽  
Machining Parameters ◽  
Elman Neural Network ◽  
Swarm Optimization

The objective of this research is to investigate the feasibility of utilizing the Elman neural network to predict the surface roughness in the milling process of Inconel 718 and then optimizing the cutting parameters through the particle swarm optimization (PSO) algorithm according to the different surface roughness requirements. The prediction of surface roughness includes the feature extraction of vibration measurements as well as the current signals, the feature selection using correlation analysis and the prediction of surface roughness through the Elman artificial neural network. Based on the prediction model of surface roughness, the cutting parameters were optimized in order to obtain the maximal feed rate according to different surface roughness constraints. The experiment results show that the surface roughness of Inconel 718 can be accurately predicted in the milling process and thereafter the optimal cutting parameter combination can be determined to accelerate the milling process.

Experimental Investigation for the Multi-objective Optimization of Machining Parameters on AISI D2 Steel Using Particle Swarm Optimization Coupled with Artificial Neural Network

2020 ◽  
Vol 19 (03) ◽  
pp. 589-606 ◽  
Author(s):  
Vipin Gopan ◽  
K. Leo Dev Wins ◽  
Gecil Evangeline ◽  
Arun Surendran
Keyword(s):  
Neural Network ◽  
Surface Roughness ◽  
Artificial Neural Network ◽  
Cutting Force ◽  
Surface Finish ◽  
Fitness Function ◽  
Machining Parameters ◽  
Swarm Optimization ◽  
Fine Surface ◽  
Aisi D2

High Carbon High Chromium (or AISI D2) Steels, owing to the fine surface finish they produce upon grinding, find lot of applications in die casting. Machining parameters affect the surface finish significantly during the grinding operation. In this context, this work puts an effort to arrive at the optimum machining parameters relating to fine surface finish with minimum cutting force. The material removal caused by the abrasive grinding wheel makes the process a very complex and nonlinear machining operation. In many situations, traditional optimization techniques fail to provide realistic optimum conditions because of the associated complexity. In order to overcome this issue, particle swarm optimization (PSO) coupled with artificial neural network (ANN) is applied in this research work for parameter optimization with the objective of achieving minimum surface roughness and cutting force. The machining parameters selected for the investigation were table speed, cross feed and depth of cut and the responses were surface roughness and cutting force. ANNs, inspired from biological neural networks, are well capable of providing patterns, which are too complex in behavior. The ANN model developed was used as the fitness function for PSO to complete the optimization. Optimization was also carried out using conventional response surface methodology-genetic algorithm (RSM-GA) approach in which regression equation developed with RSM was considered as the fitness function for GA. Confirmatory experiments were conducted and the comparison showed that PSO coupled with ANN is a reliable tool for complex optimization problems.

scholarly journals Prediction of Surface Roughness Based on Cutting Parameters and Machining Vibration in End Milling Using Regression Method and Artificial Neural Network

2020 ◽  
Vol 10 (11) ◽  
pp. 3941 ◽  
Author(s):  
Yung-Chih Lin ◽  
Kung-Da Wu ◽  
Wei-Cheng Shih ◽  
Pao-Kai Hsu ◽  
Jui-Pin Hung
Keyword(s):  
Neural Network ◽  
Surface Roughness ◽  
Artificial Neural Network ◽  
Multiple Regression ◽  
Predictive Models ◽  
Prediction Accuracy ◽  
End Milling ◽  
Cutting Parameters ◽  
Milling Process ◽  
Cutting Depth

This study presents surface roughness modeling for machined parts based on cutting parameters (spindle speed, cutting depth, and feed rate) and machining vibration in the end milling process. Prediction models were developed using multiple regression analysis and an artificial neural network (ANN) modeling approach. To reduce the effect of chatter, machining tests were conducted under varying cutting parameters as defined in the stable regions of the milling tool. The surface roughness and machining vibration level are modeled with nonlinear quadratic forms based on the cutting parameters and their interactions through multiple regression analysis methods, respectively. Analysis of variance was employed to determine the significance of cutting parameters on surface roughness. The results show that the combined effects of spindle speed and cutting depth significantly influence surface roughness. The comparison between the prediction performance of the multiple regression and neural network-based models reveal that the ANN models achieve higher prediction accuracy for all training data with R = 0.96 and root mean square error (RMSE) = 3.0% compared with regression models with R = 0.82 and RMSE = 7.57%. Independent machining tests were conducted to validate the predictive models; the results conclude that the ANN model based on cutting parameters with machining vibration has a higher average prediction accuracy (93.14%) than those of models with three cutting parameters. Finally, the feasibility of the predictive model as the base to develop an online surface roughness recognition system has been successfully demonstrated based on contour surface milling test. This study reveals that the predictive models derived on the cutting conditions with consideration of machining stability can ensure the prediction accuracy for application in milling process.

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.

End-Milling Machinability of Inconel 718

1996 ◽  
Vol 210 (1) ◽  
pp. 11-23 ◽  
Author(s):  
M Alauddin ◽  
M A El Baradie ◽  
M S J Hashmi
Keyword(s):  
Surface Roughness ◽  
Tool Life ◽  
Inconel 718 ◽  
End Milling ◽  
Milling Process ◽  
Machining Parameters ◽  
Life Tests ◽  
Life Values ◽  
Published Research ◽  
Carbide Inserts

Most published research works on machining Inconel 718 have been mainly concerned with turning, while the milling process has received little attention due to the complexity of the process. In this paper a series of end-milling experiments of Inconel 718 has been carried out in order to: (a) optimize cutting variables, (b) investigate tool life values and relationships and (c) investigate surface roughness. The machining parameters have been optimized by measuring cutting forces. Tool life tests have been carried out using carbide inserts and the surface roughness has been analysed.

Optimization of Cutting Parameters for AISI H13 Tool Steel by Taguchi Method and Artificial Neural Network

2015 ◽  
Vol 2 (2) ◽  
pp. 47-65 ◽  
Author(s):  
Hrishikesh Pathak ◽  
Sanghamitra Das ◽  
Rakesh Doley ◽  
Satadru Kashyap
Keyword(s):  
Neural Network ◽  
Surface Roughness ◽  
Artificial Neural Network ◽  
Tool Steel ◽  
Cutting Tool ◽  
Cutting Parameters ◽  
Depth Of Cut ◽  
H13 Tool Steel ◽  
Aisi H13 ◽  
Aisi H13 Tool Steel

In the present study an attempt has been made to investigate the effect of cutting parameters (cutting speed, feed rate, and depth of cut) on surface roughness and material removal rate (MRR) during dry turning operation of AISI H13 tool steel as per Taguchi's experimental design technique using an L9 orthogonal array. Signal to noise ratio (S/N) results and Analysis of Variance (ANOVA) were employed in order to investigate the optimal and significant cutting characteristics of H13 tool steel respectively. This paper focuses on optimizing the cutting parameters for minimum surface roughness and maximum MRR of H13 tool steel using high speed steel (HSS) as the cutting tool during turning. The results indicated that feed has a significant influence on surface finish and depth of cut on MRR when turning operation was carried out with HSS cutting tool. An artificial neural network model and regression equations were also developed to obtain minimum surface roughness and maximum MRR at different cutting conditions.

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