An Economical Method for Artificial Neural Network Process Modeling by the Model-Modifier Approach

2000 ◽  
Author(s):  
Sanjay Bhatikar
Keyword(s):  
Process Modeling ◽  
Vapor Deposition ◽  
Chemical Vapor ◽  
Similar Type ◽  
Training Data ◽  
Development Effort ◽  
Ann Model ◽  
Economical Method ◽  
Network Process ◽  
Ann Models

Abstract In this paper we present our model-modifier approach as an economical method for the development of accurate manufacturing equipment models. The model modifier method leverages knowledge from one ANN model to another of a similar type, thus reducing the development effort required as compared to starting from scratch. The economy afforded by this knowledge-sharing technique was evaluated on a Chemical Vapor Deposition (CVD) reactor. The results show that the model-modifier approach is a valid method for transferring knowledge between similar ANN models and that significant savings in training data accrue from this approach. In our case, a highly accurate ANN model was developed with a mere one-fifth of the data that would have been required without this approach. Further, we have also shown that an ANN model developed by the model-modifier approach can be easily and reliably utilized for process optimization.

Ultrananocrystalline Diamond in the Laboratory and the Cosmos

MRS Bulletin ◽  
2001 ◽  
Vol 26 (10) ◽  
pp. 771-776 ◽  
Author(s):  
Dieter M. Gruen
Keyword(s):  
Soviet Union ◽  
Vapor Deposition ◽  
Former Soviet Union ◽  
Diamond Films ◽  
Chemical Vapor ◽  
The United States ◽  
Development Effort ◽  
Important Research ◽  
Ultrananocrystalline Diamond ◽  
High Pressure High Temperature

Diamond is one of the most intriguing and potentially useful materials known to science. It is the hardest substance that we know, and it has the highest sound velocity and the highest thermal conductivity of any material. Because diamond is so difficult to fabricate, the challenge is to take meaningful advantage of its extraordinary properties. The chemical vapor deposition (CVD) of diamond overcomes many of the fabrication problems and has become the focus of an important research and development effort worldwide. Although the General Electric Corp. succeeded about 50 years ago in the synthesis of diamond by high-pressure, high-temperature techniques, the low-pressure, or CVD, methods were developed only about 20 years ago in the former Soviet Union, Japan, and the United States. This presentation will deal with a more recent development in diamond CVD that allows one to control the crystallite size in such a way as to synthesize phase-pure nanocrystalline diamond films, which have many unique and fascinating properties not shared by other forms of diamond.

scholarly journals Toward a State-of-the-Art of Fly-Rock Prediction Technology in Open-Pit Mines Using EANNs Model

2019 ◽  
Vol 9 (21) ◽  
pp. 4554 ◽  
Author(s):  
Hoang Nguyen ◽  
Xuan-Nam Bui ◽  
Trung Nguyen-Thoi ◽  
Prashanth Ragam ◽  
Hossein Moayedi
Keyword(s):  
State Of The Art ◽  
Promising Result ◽  
Absolute Error ◽  
Original Data ◽  
Training Data ◽  
Open Pit ◽  
Training Dataset ◽  
Percentage Error ◽  
Ann Model ◽  
Ann Models

Fly-rock induced by blasting is an undesirable phenomenon in quarries. It can be dangerous for humans, equipment, and buildings. To minimize its undesirable hazards, we proposed a state-of-the-art technology of fly-rock prediction based on artificial neural network (ANN) models and their robust combination, called EANNs model (ensemble of ANN models); 210 fly-rock events were recorded to develop and test the ANN and EANNs models. Of thi sample, 80% of the whole dataset was assigned to develop the models, the remaining 20% was assigned to confirm the models developed. Accordingly, five ANN models were designed and developed using the training dataset (i.e., 80% of the whole original data) first; then, their predictions on the training dataset were ensembled to generate a new training dataset. Subsequently, another ANN model was developed based on the new set of training data (i.e., EANNs model). Its performance was evaluated through a variety of performance indices, such as MAE (mean absolute error), MAPE (mean absolute percentage error), RMSE (root-mean-square error), R2 (correlation coefficient), and VAF (variance accounted for). A promising result was found for the proposed EANNs model in predicting blast-induced fly-rock with a MAE = 2.777, MAPE = 0.017, RMSE = 4.346, R2 = 0.986, and VAF = 98.446%. To confirm the performance of the proposed EANNs model, another ANN model with the same structure was developed and tested on the training and testing datasets. The findings also indicated that the proposed EANNs model yielded better performance than those of the ANN model with the same structure.

Atmospheric pressure chemical vapor deposition of ZnO: Process modeling and experiments

2014 ◽  
Vol 555 ◽  
pp. 163-168 ◽  
Author(s):  
J. van Deelen ◽  
A. Illiberi ◽  
B. Kniknie ◽  
E.H.A. Beckers ◽  
P.J.P.M. Simons ◽  
...  
Keyword(s):  
Chemical Vapor Deposition ◽  
Process Modeling ◽  
Vapor Deposition ◽  
Atmospheric Pressure ◽  
Chemical Vapor ◽  
Pressure Chemical ◽  
Modeling And Experiments

ANN-Model for Plasma Chemical Vapor Deposition Process

2013 ◽  
Vol 699 ◽  
pp. 92-95
Author(s):  
Zhen Yu Li ◽  
Hong Sheng Li
Keyword(s):  
Chemical Vapor Deposition ◽  
Vapor Deposition ◽  
Chemical Vapor ◽  
Deposition Process ◽  
Chemical Vapor Deposition Process ◽  
Ann Model ◽  
Plasma Chemical ◽  
Plasma Chemical Vapor Deposition ◽  
Process Gas ◽  
Vapor Deposition Process

Plasma Chemical Vapor Deposition process is one of the main process to make optical fiber core-rod. In this process, gas flow, pressure, furnace temperature, reflective microwave power , resonator moving speed and deposition rate are dependant aspects. An artificial neural network model is set up to describe the relationship of variables in the process, and also verified by the experiment and production. Based on the ANN-model, the process recipe setting is illustrated in the paper.

Reaction couples of ceramic thin films prepared by CVD

1988 ◽  
Vol 46 ◽  
pp. 798-799
Author(s):  
D.W. Susnitzky ◽  
S.R. Summerfelt ◽  
C.B. Carter
Keyword(s):  
Thin Film ◽  
Vapor Deposition ◽  
Chemical Vapor ◽  
Deposition Process ◽  
Solid State Reactions ◽  
Spatial Distributions ◽  
Diffusion Couples ◽  
Kinetics Studies ◽  
Ceramic Thin Films ◽  
Initial Stage

Solid-state reactions have traditionally been studied in the form of diffusion couples. This ‘bulk’ approach has been modified, for the specific case of the reaction between NiO and Al2O3, by growing NiAl2O4 (spinel) from electron-transparent Al2O3 TEM foils which had been exposed to NiO vapor at 1415°C. This latter ‘thin-film’ approach has been used to characterize the initial stage of spinel formation and to produce clean phase boundaries since further TEM preparation is not required after the reaction is completed. The present study demonstrates that chemical-vapor deposition (CVD) can be used to deposit NiO particles, with controlled size and spatial distributions, onto Al2O3 TEM specimens. Chemical reactions do not occur during the deposition process, since CVD is a relatively low-temperature technique, and thus the NiO-Al2O3 interface can be characterized. Moreover, a series of annealing treatments can be performed on the same sample which allows both Ni0-NiAl2O4 and NiAl2O4-Al2O3 interfaces to be characterized and which therefore makes this technique amenable to kinetics studies of thin-film reactions.

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