Neural network modeling and analysis of the material removal process during laser machining

Yousef, Basem F.; Knopf, George K.; Bordatchev, Evgueni V.; Nikumb, Suwas

doi:10.1007/s00170-002-1441-9

Cited by 93 publications

(40 citation statements)

References 13 publications

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“…In their work these authors wanted to develop a model which they could use to select the laser processing parameters which would result in the required ablation depth and width of a conical shaped crater. The test results showed that the ANN modelled level of pulse energy corresponding to specific depth and diameter was consistent with the actual level of pulse energy to a high degree of accuracy due to the adaptive properties of ANN [26].…”

Section: Introductionmentioning

confidence: 82%

Comparison of ANN and DoE for the prediction of laser-machined micro-channel dimensions

Karazi

Issa

Brabazon

2009

Optics and Lasers in Engineering

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This paper presents four models developed for the prediction of the width and depth dimensions of CO 2 laser formed micro-channels in glass. A 3 3 statistical design of experiments (DoE) model was built and conducted with the power (P), pulse repetition frequency (PRF), and traverse speed (U) of the laser machine as the selected parameters for investigation. Three feed-forward, back-propagation Artificial Neural Networks (ANNs) models were also generated. These ANN models were varied to investigate the influence of variations in the number and the selection of training data. Model A was constructed with 24 data randomly selected from the experimental results, leaving three data points for model testing; Model B was constructed with the eight corner points of the experimental data space, and seven other randomly selected data, leaving 12 data points for testing; and Model C was constructed with 15 randomly selected data leaving 12 data points for testing.These models were developed separately for both micro-channel width and depth prediction. These ANN models were constructed in LabVIEW coding. The performance of these ANN models and the DoE model were compared. When compared with the actual results two of the ANN models showed greater average percentage error than the DoE model. The other ANN model showed an improved predictive capability that was approximately twice as good as that provided from the DoE model.

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Section: Introductionmentioning

confidence: 82%

Comparison of ANN and DoE for the prediction of laser-machined micro-channel dimensions

Karazi

Issa

Brabazon

2009

Optics and Lasers in Engineering

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“…Tables 1-4 further show the comparison of two primary surface roughness parameters, Ra and Rmax, between the MLP and RBF models. The mean squared error shown in these tables is defined as 1/2 (measured value − predicted value) 2 . The smaller the mean squared error, the higher the prediction accuracy.…”

Section: Testing Of Mlp and Rbf Modelsmentioning

confidence: 99%

Neural Network Modeling and Prediction of Surface Roughness in Machining Aluminum Alloys

Fang

Pai²,

Edwards³

2016

JCC

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Artificial neural network is a powerful technique of computational intelligence and has been applied in a variety of fields such as engineering and computer science. This paper deals with the neural network modeling and prediction of surface roughness in machining aluminum alloys using data collected from both force and vibration sensors. Two neural network models, including a Multi-Layer Perceptron (MLP) model and a Radial Basis Function (RBF) model, were developed in the present study. Each model includes eight inputs and five outputs. The eight inputs include the cutting speed, the ratio of the feed rate to the tool-edge radius, cutting forces in three directions, and cutting vibrations in three directions. The five outputs are five surface roughness parameters. Described in detail is how training and test data were generated from real-world machining experiments that covered a wide range of cutting conditions. The results show that the MLP model provides significantly higher accuracy of prediction for surface roughness than does the RBF model.

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“…This partially reflects the rarity of technologies that can measure and store temperature and time in some manner without internal or external sources of power. The difficulty is compounded in applications in metallurgy 1,2 , VLSI processing 3,4 and laser machining 5,6 , to name only a few, when the device must be unobtrusive and robust enough to be incorporated in a wide variety of heat treatments at temperatures that will destroy typical nonvolatile electronics.…”

Section: Introductionmentioning

confidence: 99%

Thermoluminescent microparticle thermal history sensors

et al. 2016

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While there are innumerable devices that measure temperature, the nonvolatile measurement of thermal history is far more difficult, particularly for sensors embedded in extreme environments such as fires and explosions. In this review, an extensive analysis is given of one such technology: thermoluminescent microparticles. These are transparent dielectrics with a large distribution of trap states that can store charge carriers over very long periods of time. In their simplest form, the population of these traps is dictated by an Arrhenius expression, which is highly dependent on temperature. A particle with filled traps that is exposed to high temperatures over a short period of time will preferentially lose carriers in shallow traps. This depopulation leaves a signature on the particle luminescence, which can be used to determine the temperature and time of the thermal event. Particles are prepared-many months in advance of a test, if desired-by exposure to deep ultraviolet, X-ray, beta, or gamma radiation, which fills the traps with charge carriers. Luminescence can be extracted from one or more particles regardless of whether or not they are embedded in debris or other inert materials. Testing and analysis of the method is demonstrated using laboratory experiments with microheaters and high energy explosives in the field. It is shown that the thermoluminescent materials LiF:Mg,Ti, MgB 4 O 7 :Dy,Li, and CaSO 4 :Ce,Tb, among others, provide accurate measurements of temperature in the 200 to 500°C range in a variety of high-explosive environments.

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Neural network modeling and analysis of the material removal process during laser machining

Cited by 93 publications

References 13 publications

Comparison of ANN and DoE for the prediction of laser-machined micro-channel dimensions

Comparison of ANN and DoE for the prediction of laser-machined micro-channel dimensions

Neural Network Modeling and Prediction of Surface Roughness in Machining Aluminum Alloys

Thermoluminescent microparticle thermal history sensors

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