Modeling and Analysis of Hot Extrusion Metal Forming Process Using Artificial Neural Network and ANOVA

2010 ◽  
Author(s):  
Iman Zohourkari ◽  
Saeed Assarzadeh ◽  
Mehdi Zohoor
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Analysis Of Variance ◽  
Neural Model ◽  
Operating Conditions ◽  
Optimum Operating ◽  
Extrusion Force ◽  
Data Set ◽  
Artificial Neural ◽  
Anova Technique

In this paper, a feed-forward back-propagation artificial neural network (BP-ANN) and analysis of variance (ANOVA) are applied to a hot metal extrusion process, establishing a black box model as well as analyzing the effects of relevant process parameters on required forging load, under different operating conditions. Some finite element simulation data on extruding ck-45 steel, adopted from a published research paper, were used to train the neural model employing Levenberg-Marquardt learning algorithm. Die angle (15°–75°), friction coefficient between billet-die material pair (0.4–0.8), punch velocity (168–203 mm/s), and billet temperature (1000°C–1260°C) were selected as the inputs, while the extrusion load (tone) was considered as the network’s output. Based on the results during modeling attempts, a 4-10-10-1 size neural network has been decided on as the appropriate architecture of the process model. Testing predictive accuracy of the developed model was also done using a new data set (8 data samples), which has not been used in the training phase. The comparative errors with respect to the desired FEM simulations are all in acceptable ranges (less than 12%) thereby the network’s generalization capabilities were confirmed. Having established the appropriate neural model, analysis of variance (ANOVA) technique was then applied to the original training data base to find and recognize the level of importance of each parameters and their possible dual interactions on the extrusion loading force within 95% of confidence interval (α = 0.05). Based on the obtained inferences, the best optimal combination of parametric settings which leads to the minimum required extruding load was then revealed and recommended. The optimally minimized extrusion force was then predicted by the trained network model. Neural network tool box (NNET) of the Matlab software and design of experiments module of Minitab software were employed as platforms to develop neural simulations and ANOVA technique, respectively. The overall results indicate the feasibility and effectiveness of the proposed approach in a real manufacturing environment and eliminate the need to carry out expensive as well as time consuming trial and error experimentations to reach to the optimum operating conditions.

scholarly journals Fixed bed utilization for the isolation of xylene vapor: Kinetics and optimization using response surface methodology and artificial neural network

2020 ◽  
Vol 26 (2) ◽  
pp. 200105-0
Author(s):  
Kaushal Naresh Gupta ◽  
Rahul Kumar
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Response Surface Methodology ◽  
Response Surface ◽  
Flow Rate ◽  
Operating Conditions ◽  
Optimum Operating ◽  
Operating Parameters ◽  
Bed Height ◽  
Artificial Neural

This paper discusses the isolation of xylene vapor through adsorption using granular activated carbon as an adsorbent. The operating parameters investigated were bed height, inlet xylene concentration and flow rate, their influence on the percentage utilization of the adsorbent bed up to the breakthrough was found out. Mathematical modeling of experimental data was then performed by employing a response surface methodology (RSM) technique to obtain a set of optimum operating conditions to achieve maximum percentage utilization of bed till breakthrough. A fairly high value of R2 (0.993) asserted the proposed polynomial equation’s validity. ANOVA results indicated the model to be highly significant with respect to operating parameters studied. A maximum of 76.1% utilization of adsorbent bed was found out at a bed height of 0.025 m, inlet xylene concentration of 6,200 ppm and a gas flow rate of 25 mL.min-1. Furthermore, the artificial neural network (ANN) was also employed to compute the percentage utilization of the adsorbent bed. A comparison between RSM and ANN divulged the performance of the latter (R2 = 0.99907) to be slightly better. Out of various kinetic models studied, the Yoon-Nelson model established its appropriateness in anticipating the breakthrough curves.

Search for Optimum Operating Conditions for a Water Purification Process Integrated to a Heat Transformer with Energy Recycling using Artificial Neural Network Inverse Solved by Genetic and Particle Swarm Algorithms

2012 ◽  
Vol 7 (1) ◽  
Author(s):  
Youness El Hamzaoui ◽  
Bassam Ali ◽  
J. Alfredo Hernandez ◽  
Obed Cortez Aburto ◽  
Outmane Oubram
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Water Purification ◽  
Particle Swarm ◽  
Optimization Methods ◽  
Operating Conditions ◽  
Optimum Operating ◽  
Purification Process ◽  
Artificial Neural ◽  
Heat Transformer

The coefficient of performance (COP) for a water purification process integrated to an absorption heat transformer with energy recycling was optimized using the artificial intelligence. The objective of this paper is to develop an integrated approach using artificial neural network inverse (ANNi) coupling with optimization methods: genetic algorithms (GAs) and particle swarm algorithm (PSA). Therefore, ANNi was solved by these optimization methods to estimate the optimal input variables when a COP is required. The paper adopts two cases studies to accomplish the comparative study. The results illustrate that the GAs outperforms the PSA. Finally, the study shows that the GAs based on ANNi is a better optimization method for control on-line the performance of the system, and constitutes a very promising framework for finding a set of “good solutions”.

scholarly journals Comparison of Optimization of Exergy Efficiency of a Crude Distillation Unit Using Artificial Neural Network (ANN) and Response Surface Methods (RSM)

2020 ◽  
pp. 1-14
Author(s):  
M. N. Braimah
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Exergy Efficiency ◽  
Operating Conditions ◽  
Distillation Unit ◽  
Optimum Operating ◽  
Base Case ◽  
Operating Variables ◽  
Port Harcourt ◽  
Artificial Neural

The study carried out simulation of the Crude Distillation Unit (CDU) of the New Port Harcourt Refinery (NPHR) and performed exergy analysis of the Refinery. The Crude Distillation Unit (CDU) of the New Port Harcourt refinery was simulated using HYSYS (2006.5). The Atmospheric Distillation Unit (ADU) which is the most inefficient unit and where major separation of the crude occurs was focused on. The simulation result was exported to Microsoft Excel Spreadsheet for exergy analysis. The ADU was optimized using statistical method and Artificial Neural Network. Box-Behnken model was applied to the sensitive operating variables that were identified. The statistical analysis of the RSM was carried out using Design Expert (6.0). Matlab software was used for the Artificial Neural Network. All the operating variables were combined to give the best optimum operating conditions. Exergy efficiency of the ADU was 51.9% and 52.4% when chemical exergy was included and excluded respectively. The optimum operating conditions from statistical optimization (RSM) are 586.1 K for liquid inlet temperature, 595.5 kPa for liquid inlet pressure and condenser pressure of 124 kPa with exergy efficiency of 69.6% which is 33.0% increment as compared to the base case. For the ANN optimization, the exergy efficiency of the ADU was estimated to be 70.6%. This gave an increase of 34.9% as compared to the base case. This study concluded that enormous improvement can be achieved both in design feasibility and improved efficiency if the feed operating parameters and other sensitive parameters are carefully chosen. Furthermore, ANN optimization gave better exergy efficiency of 70.6% than RSM optimization of 69.6%.

scholarly journals PREDICTION OF WEAR AND FRICTION COEFFICIENT OF BRAKE PADS DEVELOPED FROM PALM KERNEL FIBRES USING ARTIFICIAL NEURAL NETWORK

2017 ◽  
Vol 20 (3) ◽  
Author(s):  
IKPAMBESE KUMADEN KUNCY ◽  
ASHWE ABUGH ◽  
TULEUN LIVINUS TYOVENDA
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Friction Coefficient ◽  
Wear Rate ◽  
Network Architecture ◽  
Palm Kernel ◽  
Neural Model ◽  
Operating Conditions ◽  
Brake Pads ◽  
Artificial Neural

Artificial neural network prediction of wear rate and friction coefficient of brake pads developed from palm kernel fibres (PKF) was carried out in this study. Major input parameters including materials formulation, manufacturing conditions, and operating conditions were introduced into the neural model while wear rate and friction coefficient were the outputs. The network architecture of LM12 [4-3]<sub>2</sub> 2<strong> </strong>was selected for predicting the wear rate and coefficient of friction. The predicted wear rate and friction coefficient using ANN models were compared with the measured values using some statistical indicators. Results showed that the ANN predicted accurately the wear rate and friction coefficient of the developed automotive brake pads.

Random Forest (RF) and Artificial Neural Network (ANN) Algorithms for LULC Mapping

2020 ◽  
Vol 38 (4A) ◽  
pp. 510-514
Author(s):  
Tay H. Shihab ◽  
Amjed N. Al-Hameedawi ◽  
Ammar M. Hamza
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Artificial Neural Network ◽  
Random Forest ◽  
Satellite Image ◽  
Landsat 8 ◽  
Optical Remote Sensing ◽  
Data Set ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

In this paper to make use of complementary potential in the mapping of LULC spatial data is acquired from LandSat 8 OLI sensor images are taken in 2019.  They have been rectified, enhanced and then classified according to Random forest (RF) and artificial neural network (ANN) methods. Optical remote sensing images have been used to get information on the status of LULC classification, and extraction details. The classification of both satellite image types is used to extract features and to analyse LULC of the study area. The results of the classification showed that the artificial neural network method outperforms the random forest method. The required image processing has been made for Optical Remote Sensing Data to be used in LULC mapping, include the geometric correction, Image Enhancements, The overall accuracy when using the ANN methods 0.91 and the kappa accuracy was found 0.89 for the training data set. While the overall accuracy and the kappa accuracy of the test dataset were found 0.89 and 0.87 respectively.

scholarly journals A Unified Control Strategy of Distributed Generation for Grid-Connected and Islanded Operation Conditions Using an Artificial Neural Network

2021 ◽  
Vol 13 (11) ◽  
pp. 6388
Author(s):  
Karim M. El-Sharawy ◽  
Hatem Y. Diab ◽  
Mahmoud O. Abdelsalam ◽  
Mostafa I. Marei
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Control System ◽  
Distributed Generation ◽  
Control Strategy ◽  
Current Control ◽  
Operating Conditions ◽  
Pi Controllers ◽  
Artificial Neural ◽  
The Stability

This article presents a control strategy that enables both islanded and grid-tied operations of a three-phase inverter in distributed generation. This distributed generation (DG) is based on a dramatically evolved direct current (DC) source. A unified control strategy is introduced to operate the interface in either the isolated or grid-connected modes. The proposed control system is based on the instantaneous tracking of the active power flow in order to achieve current control in the grid-connected mode and retain the stability of the frequency using phase-locked loop (PLL) circuits at the point of common coupling (PCC), in addition to managing the reactive power supplied to the grid. On the other side, the proposed control system is also based on the instantaneous tracking of the voltage to achieve the voltage control in the standalone mode and retain the stability of the frequency by using another circuit including a special equation (wt = 2πft, f = 50 Hz). This utilization provides the ability to obtain voltage stability across the critical load. One benefit of the proposed control strategy is that the design of the controller remains unconverted for other operating conditions. The simulation results are added to evaluate the performance of the proposed control technology using a different method; the first method used basic proportional integration (PI) controllers, and the second method used adaptive proportional integration (PI) controllers, i.e., an Artificial Neural Network (ANN).

Development of a Hybrid Artificial Neural Network and Genetic Algorithm Model for Regime Identification of Slurry Transport in Pipelines

2009 ◽  
Vol 4 (1) ◽  
Author(s):  
Sandip K Lahiri ◽  
Kartik Chandra Ghanta
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Artificial Neural Network ◽  
Operating Conditions ◽  
Misclassification Error ◽  
Wide Range ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Pipeline Design ◽  
Hybrid Artificial Neural Network

Four distinct regimes were found existent (namely sliding bed, saltation, heterogeneous suspension and homogeneous suspension) in slurry flow in pipeline depending upon the average velocity of flow. In the literature, few numbers of correlations has been proposed for identification of these regimes in slurry pipelines. Regime identification is important for slurry pipeline design as they are the prerequisite to apply different pressure drop correlation in different regime. However, available correlations fail to predict the regime over a wide range of conditions. Based on a databank of around 800 measurements collected from the open literature, a method has been proposed to identify the regime using artificial neural network (ANN) modeling. The method incorporates hybrid artificial neural network and genetic algorithm technique (ANN-GA) for efficient tuning of ANN meta parameters. Statistical analysis showed that the proposed method has an average misclassification error of 0.03%. A comparison with selected correlations in the literature showed that the developed ANN-GA method noticeably improved prediction of regime over a wide range of operating conditions, physical properties, and pipe diameters.

Watermelon Sorting Process by Frequency Identification and Artificial Neural Network

2021 ◽  
Author(s):  
Komsan Wongkalasin ◽  
Teerapon Upachaban ◽  
Wacharawish Daosawang ◽  
Nattadon Pannucharoenwong ◽  
Phadungsak Ratanadecho
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Selection Process ◽  
Network Processor ◽  
Data Set ◽  
The Neural Network ◽  
Frequency Identification ◽  
Sorting Process ◽  
Artificial Neural ◽  
Reasonable Prediction

This research aims to enhance the watermelon’s quality selection process, which was traditionally conducted by knocking the watermelon fruit and sort out by the sound’s character. The proposed method in this research is generating the sound spectrum through the watermelon and then analyzes the response signal’s frequency and the amplitude by Fast Fourier Transform (FFT). Then the obtained data were used to train and verify the neural network processor. The result shows that, the frequencies of 129 and 172 Hz were suit to be used in the comparison. Thirty watermelons, which were randomly selected from the orchard, were used to create a data set, and then were cut to manually check and match to the fruits’ quality. The 129 Hz frequency gave the response ranging from 13.57 and above in 3 groups of watermelons quality, including, not fully ripened, fully ripened, and close to rotten watermelons. When the 172 Hz gave the response between 11.11–12.72 in not fully ripened watermelons and those of 13.00 or more in the group of close to rotten and hollow watermelons. The response was then used as a training condition for the artificial neural network processor of the sorting machine prototype. The verification results provided a reasonable prediction of the ripeness level of watermelon and can be used as a pilot prototype to improve the efficiency of the tools to obtain a modern-watermelon quality selection tool, which could enhance the competitiveness of the local farmers on the product quality control.

KNT-artificial neural network model for flux prediction of ultrafiltration membrane producing drinking water

2004 ◽  
Vol 50 (8) ◽  
pp. 103-110 ◽  
Author(s):  
H.K. Oh ◽  
M.J. Yu ◽  
E.M. Gwon ◽  
J.Y. Koo ◽  
S.G. Kim ◽  
...  
Keyword(s):  
Neural Network ◽  
Experimental Data ◽  
Artificial Neural Network ◽  
Correlation Coefficient ◽  
Network Model ◽  
Operating Conditions ◽  
Transmembrane Pressure ◽  
Feed Water ◽  
Permeate Flux ◽  
Artificial Neural

This paper describes the prediction of flux behavior in an ultrafiltration (UF) membrane system using a Kalman neuro training (KNT) network model. The experimental data was obtained from operating a pilot plant of hollow fiber UF membrane with groundwater for 7 months. The network was trained using operating conditions such as inlet pressure, filtration duration, and feed water quality parameters including turbidity, temperature and UV254. Pre-processing of raw data allowed the normalized input data to be used in sigmoid activation functions. A neural network architecture was structured by modifying the number of hidden layers, neurons and learning iterations. The structure of KNT-neural network with 3 layers and 5 neurons allowed a good prediction of permeate flux by 0.997 of correlation coefficient during the learning phase. Also the validity of the designed model was evaluated with other experimental data not used during the training phase and nonlinear flux behavior was accurately estimated with 0.999 of correlation coefficient and a lower error of prediction in the testing phase. This good flux prediction can provide preliminary criteria in membrane design and set up the proper cleaning cycle in membrane operation. The KNT-artificial neural network is also expected to predict the variation of transmembrane pressure during filtration cycles and can be applied to automation and control of full scale treatment plants.

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