scholarly journals Prediction of Surface Roughness in End Milling Process Using Intelligent Systems: A Comparative Study

2011 ◽  
Vol 2011 ◽  
pp. 1-18 ◽  
Author(s):  
Abdel Badie Sharkawy
Keyword(s):  
Surface Roughness ◽  
Prediction Accuracy ◽  
End Milling ◽  
Milling Process ◽  
Depth Of Cut ◽  
Machining Parameters ◽  
Validation Data ◽  
Genetic Tuning ◽  
Inference Systems ◽  
End Milling Process

A study is presented to model surface roughness in end milling process. Three types of intelligent networks have been considered. They are (i) radial basis function neural networks (RBFNs), (ii) adaptive neurofuzzy inference systems (ANFISs), and (iii) genetically evolved fuzzy inference systems (G-FISs). The machining parameters, namely, the spindle speed, feed rate, and depth of cut have been used as inputs to model the workpiece surface roughness. The goal is to get the best prediction accuracy. The procedure is illustrated using experimental data of end milling 6061 aluminum alloy. The three networks have been trained using experimental training data. After training, they have been examined using another set of data, that is, validation data. Results are compared with previously published results. It is concluded that ANFIS networks may suffer the local minima problem, and genetic tuning of fuzzy networks cannot insureperfectoptimality unless suitable parameter setting (population size, number of generations etc.) and tuning range for the FIS, parameters are used which can be hardly satisfied. It is shown that the RBFN model has the best performance (prediction accuracy) in this particular case.

scholarly journals Comparative study on Mamdani-Type Fuzzy Inference Systems and Regression model for end milling process using AA 6082T6

2021 ◽  
Vol 2070 (1) ◽  
pp. 012209
Author(s):  
Natarajan Tamiloli ◽  
Velpuri Venkat Raman
Keyword(s):  
Surface Roughness ◽  
Regression Analysis ◽  
Fuzzy Inference ◽  
End Milling ◽  
Milling Process ◽  
Machining Parameters ◽  
Product Profile ◽  
Inference Systems ◽  
Traditional Approaches ◽  
End Milling Process

Abstract The goal of this observes is to take a look at the effect of machining parameters on surface roughness in end milling. An incipient technique in modelling surface roughness that makes use of synthetic perspicacity implements is defined in this paper. This paper fixates on growing empirical models utilizing fuzzy logic and regression analysis. The values of surface roughness presaged with the aid of using those fashions are then in comparison. The effects confirmed that the proposed gadget can considerably boom the precision of the product profile whilst in comparison to the traditional approaches, like regression analysis. The effects designate that the regression modelling method may be effectively applied for the presage of surface roughness in dry machining.

Intelligent Monitoring and Prediction of Surface Roughness in Ball-End Milling Process

2011 ◽  
Vol 121-126 ◽  
pp. 2059-2063 ◽  
Author(s):  
Somkiat Tangjitsitcharoen ◽  
Angsumalin Senjuntichai
Keyword(s):  
Surface Roughness ◽  
Cutting Force ◽  
Prediction Accuracy ◽  
End Milling ◽  
Milling Process ◽  
Ball End Milling ◽  
Roughness Model ◽  
Force Ratio ◽  
Tool Diameter ◽  
End Milling Process

In order to realize the intelligent machines, the practical model is proposed to predict the in-process surface roughness during the ball-end milling process by utilizing the cutting force ratio. The ratio of cutting force is proposed to be generalized and non-scaled to estimate the surface roughness regardless of the cutting conditions. The proposed in-process surface roughness model is developed based on the experimentally obtained data by employing the exponential function with five factors of the spindle speed, the feed rate, the tool diameter, the depth of cut, and the cutting force ratio. The prediction accuracy and the prediction interval of the in-process surface roughness model at 95% confident level are calculated and proposed to predict the distribution of individually predicted points in which the in-process predicted surface roughness will fall. All those parameters have their own characteristics to the arithmetic surface roughness and the surface roughness. It is proved by the cutting tests that the proposed and developed in-process surface roughness model can be used to predict the in-process surface roughness by utilizing the cutting force ratio with the highly acceptable prediction accuracy.

Optimization of Process Parameters for Cutting Force for Aluminium 7010 in CNC End Milling Process

2018 ◽  
Vol 10 (6) ◽  
Author(s):  
M. Kishanth ◽  
P. Rajkamal ◽  
D. Karthikeyan ◽  
K. Anand
Keyword(s):  
Surface Roughness ◽  
Cutting Force ◽  
Process Parameters ◽  
End Milling ◽  
Milling Process ◽  
Machining Process ◽  
Depth Of Cut ◽  
Optimization Of Process Parameters ◽  
Cnc End Milling ◽  
End Milling Process

In this paper CNC end milling process have been optimized in cutting force and surface roughness based on the three process parameters (i.e.) speed, feed rate and depth of cut. Since the end milling process is used for abrading the wear caused is very high, in order to reduce the wear caused by high cutting force and to decrease the surface roughness, the optimization is much needed for this process. Especially for materials like aluminium 7010, this kind of study is important for further improvement in machining process and also it will improve the stability of the machine.

Modeling & Analysis of End Milling Process Parameters Using Artificial Neural Networks

2014 ◽  
Vol 592-594 ◽  
pp. 2733-2737 ◽  
Author(s):  
G. Harinath Gowd ◽  
K. Divya Theja ◽  
Peyyala Rayudu ◽  
M. Venugopal Goud ◽  
M .Subba Roa
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Process Parameters ◽  
End Milling ◽  
Milling Process ◽  
Machining Process ◽  
Depth Of Cut ◽  
Machining Parameters ◽  
Artificial Neural ◽  
End Milling Process

For modeling and optimizing the process parameters of manufacturing problems in the present days, numerical and Artificial Neural Networks (ANN) methods are widely using. In manufacturing environments, main focus is given to the finding of Optimum machining parameters. Therefore the present research is aimed at finding the optimal process parameters for End milling process. The End milling process is a widely used machining process because it is used for the rough and finish machining of many features such as slots, pockets, peripheries and faces of components. The present work involves the estimation of optimal values of the process variables like, speed, feed and depth of cut, whereas the metal removal rate (MRR) and tool wear resistance were taken as the output .Experimental design is planned using DOE. Optimum machining parameters for End milling process were found out using ANN and compared to the experimental results. The obtained results provβed the ability of ANN method for End milling process modeling and optimization.

Monitoring of Surface Roughness Based on Cutting Force and Cutting Temperature in Ball-End Milling Process by Utilizing Response Surface Analysis

2015 ◽  
Vol 799-800 ◽  
pp. 324-328
Author(s):  
Panrawee Yaisuk ◽  
Somkiat Tangjitsitcharoen
Keyword(s):  
Surface Roughness ◽  
Cutting Force ◽  
Response Surface ◽  
Feed Rate ◽  
End Milling ◽  
Cutting Temperature ◽  
Milling Process ◽  
Depth Of Cut ◽  
Tool Diameter ◽  
End Milling Process

The surface roughness is monitored using the cutting force and the cutting temperature in the ball-end milling process by utilizing the response surface analysis with the Box-Behnken design. The optimum cutting condition is obtained referring to the minimum surface roughness, which is the spindle speed, the feed rate, the depth of cut, and the tool diameter. The models of cutting force ratio and the cutting temperature are proposed and developed based on the experimental results. It is understood that the surface roughness is improved with an increase in spindle speed, feed rate and depth of cut. The cutting temperature decreases with an increase in tool diameter. The model verification has showed that the experimentally obtained surface roughness model is reliable and accurate to estimate the surface roughness.

Study on Surface Roughness in Micro End Milling of Mold Material

2011 ◽  
Vol 325 ◽  
pp. 594-599 ◽  
Author(s):  
Hiroo Shizuka ◽  
Koichi Okuda ◽  
Masayuki Nunobiki ◽  
Yasuhito Inada
Keyword(s):  
Surface Roughness ◽  
End Milling ◽  
Milling Process ◽  
Cutting Conditions ◽  
Mold Material ◽  
Depth Of Cut ◽  
Milling Operations ◽  
Radial Depth ◽  
Micro End Milling ◽  
End Milling Process

The effects of cutting conditions on the surface roughness in a micro-end-milling process of a mold material are described in this paper. Micro-end-milling operations were performed under different cutting conditions such as feed rate and depth of cut, in order to investigate the factors that had the greatest influence on the finished surface during micro-end-milling. It was revealed that the surface roughness begins to deteriorate when the radial depth of the cut exceeds the tool radius. In addition, it was found that this phenomenon is peculiar to micro-end-milling processes.

scholarly journals Investigation on Effect of Material Hardness in High Speed CNC End Milling Process

2015 ◽  
Vol 2015 ◽  
pp. 1-6 ◽  
Author(s):  
N. V. Dhandapani ◽  
V. S. Thangarasu ◽  
G. Sureshkannan
Keyword(s):  
Surface Roughness ◽  
High Speed ◽  
Cutting Tool ◽  
End Milling ◽  
Removal Rate ◽  
Milling Process ◽  
Depth Of Cut ◽  
Study Results ◽  
Cnc End Milling ◽  
End Milling Process

This research paper analyzes the effects of material properties on surface roughness, material removal rate, and tool wear on high speed CNC end milling process with various ferrous and nonferrous materials. The challenge of material specific decision on the process parameters of spindle speed, feed rate, depth of cut, coolant flow rate, cutting tool material, and type of coating for the cutting tool for required quality and quantity of production is addressed. Generally, decision made by the operator on floor is based on suggested values of the tool manufacturer or by trial and error method. This paper describes effect of various parameters on the surface roughness characteristics of the precision machining part. The prediction method suggested is based on various experimental analysis of parameters in different compositions of input conditions which would benefit the industry on standardization of high speed CNC end milling processes. The results show a basis for selection of parameters to get better results of surface roughness values as predicted by the case study results.

Optimization and Prediction of Cutting Force and Surface Roughness in End Milling Process of AISI 304 Stainless Steel

2015 ◽  
Vol 813-814 ◽  
pp. 362-367 ◽  
Author(s):  
Darshan A. Patel ◽  
Jitendra M. Mistry ◽  
Vrushit P. Kapatel ◽  
Dhaval R. Joshi
Keyword(s):  
Surface Roughness ◽  
Cutting Force ◽  
Feed Rate ◽  
End Milling ◽  
Milling Process ◽  
304 Stainless Steel ◽  
Depth Of Cut ◽  
Aisi 304 Stainless Steel ◽  
Aisi 304 ◽  
End Milling Process

The end milling process is most commonly used where the large amount material can be removed to produce almost final shape of component. The present work deals with the experimental study and optimization the machining parameter of AISI 304 stainless steel. The effects of spindle speed, feed rate and depth of cut have been studied on the cutting force and surface roughness using Taguchi’s 27 orthogonal arrays. Regression analyses were used to develop the model of response parameters. The analysis of the result shows, the surface roughness and the cutting force is increased with feed rate and depth of cut but decreased with increased the cutting speed. The ANOVA indicate the feed rate was the most dominate parameter on surface roughness and cutting force than speed and depth of cut.

Surface Roughness Prediction in Ball-End Milling Process for Aluminum by Using Air Blow Cutting

2011 ◽  
Vol 418-420 ◽  
pp. 1428-1434 ◽  
Author(s):  
Keerati Karunasawat ◽  
Somkiat Tangjitsitcharoen
Keyword(s):  
Surface Roughness ◽  
Cutting Force ◽  
End Milling ◽  
Cutting Speed ◽  
Milling Process ◽  
Depth Of Cut ◽  
Cutting Condition ◽  
Ball End Milling ◽  
Tool Diameter ◽  
End Milling Process

The objective of this research is to develop the surface roughness and cutting force models by using the air blow cutting of the aluminum in the ball-end milling process. The air blow cutting proposed in order to reduce the use of the cutting fluid. The surface roughness and cuttting force models are proposed in the exponential forms which consist of the cutting speed, the feed rate, the depth of cut, the tool diameter, and the air blow pressure. The coefficients of the surface roughness and cutting force models are calculated by utilizing the multiple regression with the least squared method at 95% significant level. The effects of cutting parameters on the cutting force are investigated and measured to analyze the relation between the surface roughness and the cutting conditions. The experimentally obtained results showed that the cutting force has the same trend with the surface roughness. The surface plots are constructed to determine the optimum cutting condition referring to the minimum surface roughness.

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