neural network process 2019
Process Neural Network: Theory and Applications” proposes the concept and model of a process neural network for the first time, showing how it expands the mapping relationship
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
A decision support system for supplier selection using fuzzy analytic network process (Fuzzy ANP) and artificial neural network integration
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Selection of appropriate supplier (s) for success of an organization is particularly a valuable necessity, hence apart from the common criteria such as logistics, service and quality, this paper discusses most of the key decision variables which can play a critical role in case of
Multi-criteria inventory classification through integration of fuzzy analytic hierarchy process and artificial neural network
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A systematic approach to the inventory control and classification have a significant influence on company competitiveness. In practice, all inventories cannot be controlled with equal attention. To efficiently control the inventory items and to determine the suitable
Surface roughness prediction for CNC milling process using artificial neural network
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In CNC milling process , proper setting of cutting parameter is important to obtain better surface roughness. Unfortunately, conventional try and error method is time consuming as well as high cost. The purpose for this research is to develop mathematical model using
Optimization of operating parameters for EDM process based on the taguchi method and artificial neural network
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In this paper the complexity of electrical discharge machining process which is very difficult to determine optimal cutting parameters for improving cutting performance has been reported. Optimization of operating parameters is an important step in machining
Neural network -based control strategies applied to a fed-batch crystallization process
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This paper is focused on issues of process modeling and two model based control strategies of a fed-batch sugar crystallization process applying the concept of artificial neural networks (ANNs). The control objective is to force the operation into following optimal supersaturation
Estimating Fuzzy Analytic Network Process (FANP) Comparison Matrix Weights Using Artificial Neural Network
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Decision support systems developed in recent years in order to make the decision making process are more reliable. These systems are specifically used at board level, where decision making is particularly important. Fuzzy Analytic Network Process (FANP) can be
Optimization of resistance spot welding process using Taguchi method and a neural network
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There has been a significant increase in the use of high-strength steel sheet in the automobile industry to permit reductions in thickness and thus in vehicle weight. 1 The substitution of high-strength steel sheet for thicker plain carbon steels helps to lower weight
A hybrid genetic programming-artificial neural network approach for modeling of vibratory finishing process
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Finishing processes are gaining importance over the last decades as manufacturers seek to improve process efficiency while meeting increasingly severe cost and product requirements. A number of researchers have employed conventional modeling techniques
Material removal rate prediction of electrical discharge machining process using artificial neural network
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This article presents an Artificial Neural Network (ANN) architecture to model the Electrical Discharge Machining (EDM) process . It is aimed to develop the ANN model using an input- output pattern of raw data collected from an experimental of EDM process , whereas several
A statistical neural network framework for risk management process
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This paper enhances the currently available formal risk management models and related frameworks by providing an independent mechanism for checking out their results. It provides a way to compare the historical data on the risks identified by similar projects to the
Predicting surface roughness of AISI 4140 steel in hard turning process through artificial neural network , fuzzy logic and regression models
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In this study, the average surface roughness values obtained when turning AISI 4140 grade tempered steel with a hardness of 51 HRC, were modeled using fuzzy logic, artificial neural networks (ANN) and multi-regression equations. Input variables consisted of cutting speed
Process monitoring of abrasive flow machining using a neural network predictive model
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This paper discusses the preliminary development of a neural network based process monitor and off-line controller for abrasive flow machining of automotive engine intake manifolds. The process is only observable indirectly, yet the time at which machining
Process control using a neural network combined with the conventional PID controllers
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A neural controller for process control is proposed that combines a conventional multi-loop PID controller with a neural network . The concept of target signal based on feedback error is used for on-line learning of the neural network . This controller is applied to distillation
Designing and optimizing a back propagation neural network to model a thin layer drying process
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In the present study, the application of a back propagation) neural network for the prediction of moisture content of barberry fruit (berberis vulgaris) during drying was investigated. The important parameters, namely, pretreatment (no pretreatment, heat shocking, olive oil+
Investigation the deterioration process of organic coating using changing rate of phase angle at high frequency united to neural network
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Electrochemical impedance spectroscopy (EIS) of an organic coating system in 3.5% NaCl solution was measured and a new parameter k (f) named changing rate of phase angle at high frequency is extracted directly from Bode plots. The changing rate of phase angle at
Prediction of surface roughness for end milling process using Artificial Neural Network
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In machining, surface quality is one of the most commonly specified customer requirements in which the major indication of surface quality on machined parts is surface roughness. The aim is prediction of surface roughness by using artificial neural networks. The neural
Optimization of prediction error in CO2 laser cutting process by Taguchi artificial neural network hybrid with genetic algorithm
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Simulation and prediction of CO2 laser cutting of Perspex glass has been done by feed forward back propagation Artificial Neural Network (ANN). Experimental data of Taguchi orthogonal array L9 was used to train the ANN model. The simulation results were evaluated
Artificial neural network based model in effluent treatment process
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During the past 30 years the industrial sector in India has quadrupled in size, thus it increases the pressure on wastewater treatment industries to produce higher quality treated water at a lower cost. The efficiency of a treatment process closely relates to the operation of
Artificial neural network model for evaluation of the ploughing process performance
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(ANN) model is applied for evaluating the performance of the simple multi-flat plate ploughing tines using the error back propagation learning algorithm. The present preliminary study is necessary to get some technical and design information helping in