MODELLING AND SIMULATION OF SURFACE ROUGHNESS IN FACE MILLING

被引：51

作者：

Simunovic, G. ^{[1
]}

Simunovic, K. ^{[1
]}

Saric, T. ^{[1
]}

机构：

[1] Univ Osijek, Mech Engn Fac Slavonski Brod, HR-35000 Slavonski Brod, Croatia

来源：

INTERNATIONAL JOURNAL OF SIMULATION MODELLING | 2013年 / 12卷 / 03期

关键词：

Face Milling; Central Composite Design; Regression; Neural Networks; Modelling and Simulation; NEURAL-NETWORK; MACHINING PERFORMANCE; REGRESSION-ANALYSIS; CUTTING PARAMETERS; TOOL WEAR; PREDICTION; OPTIMIZATION; SYSTEM;

D O I：

10.2507/IJSIMM12(3)1.219

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

The paper presents a research on machined surface roughness in face milling of aluminium alloy on a low power cutting machine. Based on the results of the central-composite plan of experiment with varying machining parameters (number of revolutions - spindle speed n, feed rate f and depth of cut a) and the roughness observed as output variable, two models have been developed: a regression model and a model based on the application of neural networks (NN model). The regression model (coefficient of determination of 0.965 or 0.952 adjusted) with insignificant lack of fit, provides a very good fit and can be used to predict roughness throughout the region of experimentation. Likewise, the model based on the application of neural networks approximates well the experimental results with the level of RMS (Root Mean Square) error in the phase of validation of 4.01 %.

引用

页码：141 / 153

页数：13

共 29 条

[11] Regression analysis, support vector machines, and Bayesian neural network approaches to modeling surface roughness in face milling [J].

Lela, B. ;

Bajic, D. ;

Jozic, S. .

INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY, 2009, 42 (11-12) :1082-1088

[12] Artificial Neural Networks for Surface Roughness Prediction when Face Milling Al 7075-T7351 [J].

Munoz-Escalona, Patricia ;

Maropoulos, Paul G. .

JOURNAL OF MATERIALS ENGINEERING AND PERFORMANCE, 2010, 19 (02) :185-193

[13] An integrated study of surface roughness for modelling and optimization of cutting parameters during end milling operation [J].

Oktem, H. .

INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY, 2009, 43 (9-10) :852-861

[14] Prediction of surface roughness in CNC end milling by machine vision system using artificial neural network based on 2D Fourier transform [J].

Palani, S. ;

Natarajan, U. .

INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY, 2011, 54 (9-12) :1033-1042

[15] Study of the main variability aspects of surface texture of steel in face milling [J].

Petropoulos, G ;

Pandazaras, C ;

Stamos, I .

PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART J-JOURNAL OF ENGINEERING TRIBOLOGY, 2003, 217 (J2) :175-179

[16]

Rajmohan T., 2011, Advances in Production Engineering & Management, V6, P281

[17] Optimization of cutting conditions for surface roughness in CNC end milling [J].

Raju, Kantheti Venkata Murali Krishnam ;

Janardhana, Gink Ranga ;

Kumar, Podaralla Nanda ;

Rao, Vanapalli Durga Prasada .

INTERNATIONAL JOURNAL OF PRECISION ENGINEERING AND MANUFACTURING, 2011, 12 (03) :383-391

[18] Optimum surface roughness prediction in face milling by using neural network and harmony search algorithm [J].

Razfar, Mohammad Reza ;

Zinati, Reza Farshbaf ;

Haghshenas, Mahdiar .

INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY, 2011, 52 (5-8) :487-495

[19] Prediction of workpiece surface roughness using soft computing [J].

Samanta, B. ;

Erevelles, W. ;

Omurtag, Y. .

PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART B-JOURNAL OF ENGINEERING MANUFACTURE, 2008, 222 (10) :1221-1232

[20] Surface roughness prediction in machining using soft computing [J].

Samanta, B. .

INTERNATIONAL JOURNAL OF COMPUTER INTEGRATED MANUFACTURING, 2009, 22 (03) :257-266

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