Optimization of CNC ball end milling: a neural network-based model

El-Mounayri, Hazim; Kishawy, Hossam A.; Briceno, J.

doi:10.1016/j.jmatprotec.2004.07.097

Cited by 43 publications

(17 citation statements)

References 14 publications

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“…Finally, a new, more efficient and practical, neural network technique was introduced to replace the backpropagation neural network, and was successfully implemented for the case of ball-end milling. A very good match between predicted and experimentally measured process parameters was found [18].…”

Section: Introductionsupporting

confidence: 54%

Prediction of thrust forces and hole diameters using artificial neural networks in drilling of AISI D2 tool steel with cemented carbide tools

Akincioğlu¹,

Mendi²,

Çıçek³

et al. 2013

A P J Eng Sci

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In this study, the effects of cutting speed, feed rate and different types of coating materials on thrust force and hole diameter were investigated in drilling of AISI D2 cold work tool steel. In addition, the thrust forces and hole diameters were predicted by artificial neural networks (ANN) using experimental data. Uncoated, TiN, TiAlN monolayer and TiAlN/TiN multi-layer coated cemented carbide drills with diameter of 5 mm were used in drilling experiments. The holes were drilled at different combinations of four cutting speeds (50, 55, 60, 65 m/min), two feed rates (0.063 and 0.08 mm/rev), and fixed depth of cut (7 mm). Experimental results showed that the lowest thrust forces and hole diameters were obtained with TiAlN/TiN multi-layer coated drills. After ANN training, it was found that the R 2 values are very close to 1 for both training and test sets. RMSE values are smaller than 0.03, and mean error values are smaller than 5% for the test set. This case shows that ANN is a powerful method for prediction of thrust forces and hole diameters.

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Section: Introductionsupporting

confidence: 54%

Prediction of thrust forces and hole diameters using artificial neural networks in drilling of AISI D2 tool steel with cemented carbide tools

Akincioğlu¹,

Mendi²,

Çıçek³

et al. 2013

A P J Eng Sci

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show abstract

“…5,6 A lot of research work is focused on cutting parameter optimization, which used Taguchi method, response surface methodology, neural network, genetic algorithm (GA), and so on. [6][7][8][9][10][11][12][13] Cutting performance is closely related with the static and dynamic characteristics of CNC machine tool. The levels of requirements for these characteristics are higher along with the increment of cutting performance.…”

Section: -4mentioning

confidence: 99%

An integrated multidisciplinary design optimization method for computer numerical control machine tool development

Zhang

Song

Liu

et al. 2015

Advances in Mechanical Engineering

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Computer numerical control machine tool is a typical complex product related with multidisciplinary fields, complex structure, and high-performance requirements. It is difficult to identify the overall optimal solution of the machine tool structure for their multiple objectives. A new integrated multidisciplinary design optimization method is then proposed by using a Latin hypercube sampling, a Kriging approximate model, and a multi-objective genetic algorithm. Design space and parametric model are built by choosing appropriate design variables and their value ranges. Samples in design space are generated by optimal Latin hypercube method, and design variable contributions for design performance are discussed for aiding the designer's judgments. The Kriging model is built by using polynomial approximation according to the response outputs of these samples. The multidisciplinary design model is established based on three optimization objectives, that is, setting mass, optimum deformation, and first-order natural frequency, and two constraints, that is, second-order natural frequency and third-order natural frequency. The optimal solution is identified by using a multi-objective genetic algorithm. The proposed method is applied in a multidisciplinary optimization case study for a typical computer numerical control machine tool. In the optimal solution, the mass decreases by 3.35% and the first-order natural frequency increases by 4.34% in contrast to the original solution.

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“…Recently, there has been considerable interest in black box modeling method based on structures of neural networks (15) (16) , radial basis networks (16) , fuzzy wavelet networks (17) , and so on. In this paper, we consider a black box modeling method for human skill based on a structure of HHARX.…”

Section: Hharx Model and Its Milp-based Identification Algorithmmentioning

confidence: 99%

Abstraction and Implementation of Human Skill by Hybrid Dynamical System Theory-Application to an Automatic Driving System-

Zanma

Ishida

2006

IEEJ Trans.EIS

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Optimization of CNC ball end milling: a neural network-based model

Cited by 43 publications

References 14 publications

Prediction of thrust forces and hole diameters using artificial neural networks in drilling of AISI D2 tool steel with cemented carbide tools

Prediction of thrust forces and hole diameters using artificial neural networks in drilling of AISI D2 tool steel with cemented carbide tools

An integrated multidisciplinary design optimization method for computer numerical control machine tool development

Abstraction and Implementation of Human Skill by Hybrid Dynamical System Theory-Application to an Automatic Driving System-

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