neural network process


A neural network is a series of algorithms that endeavors to recognize underlying relationships in a set of data through a process that mimics the way the human brain operates. Neural networks can adapt to changing input; so the network generates the best possible result without needing to redesign the output criteria

Neural network process modelling for turning of steel parts using conventional and wiper inserts
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In this paper, the effects of insert design in turning of steel parts are presented. Surface finishing has been investigated in finish turning of AISI 1045 steel using conventional and wiper design inserts. Regression models and neural network models are developed for

Material removal rate prediction of electrical discharge machining process using artificial neural network
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Page 1. Journal of Mechanics Engineering and Automation 1 (2011) 298-302 Material Removal Rate Prediction of Electrical Discharge Machining Process Using Artificial Neural Network Azli Yahya, Trias Andromeda, Ameruddin

Gram Optimization using Taguchi Method of Parameter Design and Neural Network Process Model in Packaging Industry
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Costumer satisfaction is best achieved by improvement of quality product. One way to improve the quality of a product is to optimize the process output. This research paper describes the methods of manufacturing process optimization, using the basis of Taguchi

A Method of Transfer Functions and Block Diagrams to Study the Contribution of Variables in Artificial Neural Network Process Models
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In this paper is proposed the use of transfer functions and block diagram algebra to describe cause and effect relationships in artificial neural network process models. Explicit formulae are derived for feedforward neural networks with an arbitrary number of inputs, outputs and

Cascade-Correlation Neural Network Modeling of the Abrasive Flow Machining Process
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Prediction of nonlinear cutting process behavior using neural-network
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Process of training neural network is possible if has been decided about model structure. Not only it is necessary to choose a set of regressors but also network architecture is required. This issue is much more difficult in the nonlinear case then in the linear case

Neural Network Process Planning in Cold Forging of Different Materials
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A neural network (NN) approach is applied to process planning in multiple-step cold forging using different work materials: high and low formability steels. The scope of the approach is the identification of the technologically feasible cold forging working sequences for the

Neural Network Process Modelling for Turning of Aluminium (6061) using Cemented Carbide Inserts
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This paper deals with study using soft computing techniques, namely Artificial Neural Networks ANN, in predicting the surface roughness in turning process. Some of machining variables that have a major impact on the surface roughness in turning process such as

Use of ramp tests to obtain inverse neural network process models
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ACKNOWLEDGMENTS I would like to thank my thesis adviser Dr. Rob Whiteley for his guidance, suggestions, patience, and encouragement throughout my research experience. I want to give special thanks to my wife Megan. She is understanding and always takes the

OPTIMISATION OF THE NEURAL NETWORK PROCESS FOR AN IMPROVED BRIDGE DETERIORATION MODEL
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Infrastructure maintenance is a vital aspect for any country to ensure safety and reliability of its infrastructure and the population which use these assets. To ensure that the highest degree of maintenance is performed and recorded for infrastructure, Bridge Management

Artificial Neural Network Modeling For Material Removal Rate In Traveling Wire Electro-Chemical Spark Machining Process
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Using results from the FEM simulation, a three layer Back Propagation (BP) neuralnetwork process model was developed to predict MRR. The BP neuralnetworkprocess model was found to accurately predict TW ECSM process response for chosen process conditions

Analysis of Surface Roughness in Turning Process Using Neural Network
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been taken. Data 1 is taken from the paperNeuralNetworkProcess Modelling for Turning of Aluminium (6061) using Cemented Carbide Inserts, [16] and Data 2 is taken from the paper Optimization of surface Page 4. Analysis

Controlling and improving quality of the fertiliser production process using neural network models
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Predicting the moisture content, the quality of the produced fertiliser can be enhanced either by reheating, adding chemicals, or both. Keywords: fertiliser industry; MLP neural network; RBF neuralnetwork ; process control; quality improvement

Neural Network Predictive R2R Control to CMP Process
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DESIGNING A FAULT DIAGNOSIS SYSTEM IN A PID PROCESS CONTROL SYSTEM USING FUZZY-NEURAL NETWORK
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Set point for level V mV A B Fig 1.2 Block diagram of FDS being implemented in a PID Process Control System Using Fuzzy- NeuralNetworkPROCESS UNDER FUZZY NEURAL CONTROL Pre- processing and Feature extraction Fuzzy Diagnostic System ALARM

ARTIFICIAL NEURAL NETWORK SYSTEM AS AN ALTERNATIVE FOR THE PREDICTION OF PROCESS PARAMETERS IN ELECTRICAL DISCHARGE
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By increasing the machining voltage, the pulse energy increases and hence the metal removal rate. Page 5. 5 Figure 1 Flow chart of the neuralnetworkprocess . Figure 2 Schematic diagram of the developed neural network. Yes Decide number of nodes Initialize weights values



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