Neural Network Modeling for Face Milling Operation

2019 ◽  
Vol 11 (2) ◽  
Author(s):  
B. Sureshkumar ◽  
V. Vijayan ◽  
S. Dinesh ◽  
K. Rajaguru
Keyword(s):  
Neural Network ◽  
Network Models ◽  
Face Milling ◽  
Future Research ◽  
Neural Network Modeling ◽  
Depth Of Cut ◽  
Work Piece ◽  
Neural Network Models ◽  
Feed Force ◽  
Milling Operation

Milling operation is one of the important manufacturing processes in production industry. Study and analysis of milling process parameters such as spindle speed, feed rate and depth of cut are important for process planning engineers. The responses are temperature, surface roughness, machining time, feed force, thrust force and cutting force. The main aim of this study is to find out the effects of these parameters in face milling operation on Monel k 400 work piece materials with tungsten carbide insert. The theoretical investigation is carried out with neural network modelling and the 3-1-6 structure neural network models are considered. Developed neural network models show best agreement with experimental values. For same type of operation, result of these experiments shall be useful for future research purpose.

Modeling and Prediction of Foreign Currency Exchange Markets

2006 ◽  
pp. 139-151
Author(s):  
Joarder Kamruzzaman ◽  
Ruhul A. Sarker ◽  
Rezaul K. Begg
Keyword(s):  
Neural Network ◽  
Foreign Currency ◽  
Performance Metrics ◽  
Network Models ◽  
Research Direction ◽  
Global Market ◽  
Future Research ◽  
Neural Network Models ◽  
Currency Exchange

In today’s global market economy, currency exchange rates play a vital role in national economy of the trading nations. In this chapter, we present an overview of neural network-based forecasting models for foreign currency exchange (forex) rates. To demonstrate the suitability of neural network in forex forecasting, a case study on the forex rates of six different currencies against the Australian dollar is presented. We used three different learning algorithms in this case study, and a comparison based on several performance metrics and trading profitability is provided. Future research direction for enhancement of neural network models is also discussed.

A Method of Using Neural Networks and Inverse Kinematics for Machine Tools Error Estimation and Correction

1997 ◽  
Vol 119 (2) ◽  
pp. 247-254 ◽  
Author(s):  
J. Mou
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Machine Tool ◽  
Inverse Kinematics ◽  
Network Models ◽  
Thermal Conditions ◽  
Depth Of Cut ◽  
Time Varying ◽  
Neural Network Models ◽  
Cutting Processes

A method using artificial neural networks and inverse kinematics for machine tool error correction is presented. A generalized error model is derived, by using rigid body kinematics, to describe the error motion between the cutting tool and workpiece at discrete temperature conditions. Neural network models are then built to track the time-varying machine tool errors at various thermal conditions. The output of the neural network models can be used to periodically modify, using inverse kinematics technique, the error model’s coefficients as the cutting processes proceeded. Thus, the time-varying positioning errors at other points within the designated workspace can be estimated. Experimental results show that the time-varying machine tool errors can be estimated and corrected with desired accuracy. The estimated errors resulted from the proposed methodology could be used to adjust the depth of cut on the finish pass, or correct the probing data for process-intermittent inspection to improve the accuracy of workpieces.

scholarly journals Artificial neural network modeling of jatropha oil fueled diesel engine for emission predictions

2009 ◽  
Vol 13 (3) ◽  
pp. 91-102 ◽  
Author(s):  
Thirunavukkarasu Ganapathy ◽  
Parkash Gakkhar ◽  
Krishnan Murugesan
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Network Models ◽  
Neural Network Modeling ◽  
Data Sets ◽  
Nox Emissions ◽  
Jatropha Oil ◽  
Neural Network Models ◽  
Artificial Neural ◽  
Artificial Neural Network Models

This paper deals with artificial neural network modeling of diesel engine fueled with jatropha oil to predict the unburned hydrocarbons, smoke, and NOx emissions. The experimental data from the literature have been used as the data base for the proposed neural network model development. For training the networks, the injection timing, injector opening pressure, plunger diameter, and engine load are used as the input layer. The outputs are hydrocarbons, smoke, and NOx emissions. The feed forward back propagation learning algorithms with two hidden layers are used in the networks. For each output a different network is developed with required topology. The artificial neural network models for hydrocarbons, smoke, and NOx emissions gave R2 values of 0.9976, 0.9976, and 0.9984 and mean percent errors of smaller than 2.7603, 4.9524, and 3.1136, respectively, for training data sets, while the R2 values of 0.9904, 0.9904, and 0.9942, and mean percent errors of smaller than 6.5557, 6.1072, and 4.4682, respectively, for testing data sets. The best linear fit of regression to the artificial neural network models of hydrocarbons, smoke, and NOx emissions gave the correlation coefficient values of 0.98, 0.995, and 0.997, respectively.

Neural network modeling of carcass measurements to predict beef tenderness

2000 ◽  
Vol 80 (2) ◽  
pp. 311-318 ◽  
Author(s):  
B. D. Hill ◽  
S. D. M. Jones ◽  
W. M. Robertson ◽  
I. T. Major
Keyword(s):  
Neural Network ◽  
Network Models ◽  
Large Data ◽  
Research Centre ◽  
Neural Network Modeling ◽  
Marbling Score ◽  
Neural Network Models ◽  
Cooking Method ◽  
Beef Tenderness ◽  
Plant Weight

Neural network (NN) models were developed for predicting and classifying an objective measurement of tenderness using carcass data such as pre-slaughter information (sex, age, kill order), weights, pH, temperatures, lean color readings, lab-determined measurements, grade measurements and organ weights. Tenderness was expressed objectively as Warner-Bratzler shear (WBS) force measured on steaks, aged 6 d, from the longissimus thoracis et lumborum (LTL) muscle. Carcass data from experiments conducted between 1985 and 1995 at the Lacombe Research Centre were combined to form large data sets (n = 775–1177) for modeling. Neural network models to predict actual shear values showed limited potential (R2 = 0.37–0.45) and were only marginally better than a multiple linear regression (MLR) model (R2 = 0.34). Neural network models that classified carcasses into tenderness categories showed better potential (mean accuracy 51–53%). The best four-category (tender, probably tender, probably tough, tough) model classified tender and tough steaks with accuracies of 0.64 and 0.79, respectively. This model reduced tough and probably tough carcasses by 55% in our population. The model required the following 11 inputs, which, except for cooking method, are available by 24 h postmortem: sex, live plant weight, hot carcass weight, 24-h cooler shrink, 24-h pH, 24-h CIE color b*, 24-h CIE lightness L* × hue angle, rib eye area, grader's marbling score (AMSA%), grade, and cooking method. By implementing techniques outlined in this study in a plant situation, the current 23% unacceptable consumer rating for Canadian beef could be reduced to 10–12%. Key words: Neural networks, beef, tenderness, carcass measurements, longissimus muscle

Neural Network Forecasting of Tourism Demand

2005 ◽  
Vol 11 (3) ◽  
pp. 301-328 ◽  
Author(s):  
Sen Cheong Kon ◽  
Lindsay W. Turner
Keyword(s):  
Neural Network ◽  
Network Models ◽  
Accurate Method ◽  
Future Research ◽  
Data Series ◽  
Structural Method ◽  
Short Term ◽  
Neural Network Models ◽  
The Neural Network ◽  
Short Term Forecasting

In times of tourism uncertainty, practitioners need short-term forecasting methods. This study compares the forecasting accuracy of the basic structural method (BSM) and the neural network method to find the best structure for neural network models. Data for arrivals to Singapore are used to test the analysis while the naïve and Holt-Winters methods are used for base comparison of simpler models. The results confirm that the BSM remains a highly accurate method and that correctly structured neural models can outperform BSM and the simpler methods in the short term, and can also use short data series. These findings make neural methods significant candidates for future research.

DEVELOPMENT OF AN EXPERT SYSTEM FOR DIAGNOSTICS OF LUNG DISEASES BASED ON NEURAL NETWORK MODELING

2021 ◽  
pp. 155-159
Author(s):  
Екатерина Ивановна Новикова ◽  
Екатерина Александровна Андрианова ◽  
Елена Евгеньевна Удодова ◽  
Анастасия Юрьевна Корниенко ◽  
Александр Станиславович Панов
Keyword(s):  
Neural Network ◽  
Lung Diseases ◽  
Network Models ◽  
The Body ◽  
Neural Network Modeling ◽  
Diagnostic Study ◽  
Topological Groups ◽  
Social Significance ◽  
Neural Network Models ◽  
Information Process

В статье рассматриваются вопросы диагностики заболеваний легких, таких как кавернозный, инфильтративный, очаговой, диссеминированный туберкулез, онкология и пневмония. Медико-социальное значение болезней органов дыхания в современных условиях велико и определяется, прежде всего, их крайне высокой частотой среди различных контингентов населения. Учитывая значимость дыхания для организма, необходимо вовремя выявлять различные патологии и применять незамедлительные меры лечения. Одним из средств повышения эффективности диагностики данных патологий является автоматизация обработки диагностических данных с использованием современных технологий, а также создание компьютерной системы поддержки принятия решений, которая принимала бы во внимание большой объем диагностической информации и исключала ошибки субъективного характера. Выделение топологических групп по легочным заболеваниям проводилось с использованием самоорганизующихся карт Кохонена. По результатам классификации было проведено обучения нейронных сетей, используя алгоритм «многослойного персептрона» методом «обратного распространения», и получены математические модели. В медицинской практике постоянно следует учитывать то обстоятельство, что достоверные и адекватные медицинские данные, например, лабораторные анализы, результаты инструментального диагностического исследования, данные опроса больного или физикального исследования, потеряют свою актуальность, если информационный процесс длительно растянут по времени. Разработанные нейросетевые модели, были реализованы в информационно-программном обеспечении, которое позволит повысить эффективность процесса диагностики заболеваний легких The article deals with the diagnosis of lung diseases such as cavernous, infiltrative, focal, disseminated tuberculosis, oncology and pneumonia. The medical and social significance of respiratory diseases in modern conditions is great and is determined, first of all, by their extremely high frequency among various contingents of the population. Given the importance of breathing for the body, it is necessary to timely identify various pathologies and apply immediate treatment measures. One of the means of increasing the efficiency of diagnosing these pathologies is the automation of the processing of diagnostic data using modern technologies, as well as the creation of a computer decision support system that would take into account a large amount of diagnostic information and exclude subjective errors. The selection of topological groups for pulmonary diseases was carried out using self-organizing Kohonen maps. Based on the classification results, neural networks were trained using the "multilayer perceptron" algorithm by the "backpropagation" method and mathematical models were obtained. In medical practice, one should constantly take into account the fact that reliable and adequate medical data, for example, laboratory tests, the results of an instrumental diagnostic study, data from a patient survey or physical examination, will lose their relevance if the information process is prolonged for a long time. The developed neural network models were implemented in information and software that will improve the efficiency of the process of diagnosing lung diseases

Fine Pitch Stencil Printing Process Modeling and Optimization

1996 ◽  
Vol 118 (1) ◽  
pp. 1-6 ◽  
Author(s):  
Y. Li ◽  
R. L. Mahajan ◽  
N. Nikmanesh
Keyword(s):  
Neural Network ◽  
Network Models ◽  
Design Parameters ◽  
Neural Network Modeling ◽  
Solder Paste ◽  
Neural Network Models ◽  
Height Variation ◽  
Stencil Printing ◽  
Fine Pitch ◽  
Gradient Descent Algorithm

In this paper, we present a statistical-neural network modeling approach to process optimization of fine pitch stencil printing for solder paste deposition on pads of printed circuit boards (PCB). The overall objective was to determine the optimum settings of the design parameters that would result in minimum solder paste height variation for the new board designs with 20-mil, 25-mil, and 50-mil pitch pad patterns. As a first step, a Taguchi orthogonal array, L27, was designed to capture the main effects of the six important printing machinery parameters and the PCBs pad conditions. Some of their interactions were also included. Fifty-four experimental runs (two per setting) were conducted. These data were then used to construct neural network models relating the desired quality characteristics to the input design parameters. Our modular approach was used to select the appropriate architecture for these models. These models in conjunction with the gradient descent algorithm enabled us to determine the optimum settings for minimum solder paste height variation. Confirming experiments on the production line validated the optimum settings predicted by the model. In addition to the combination of all the three pad patterns, i.e., 20, 25, and 50 mil pitch pads, we also built neural network models for individual and dual combinations of the three pad patterns. The simulations indicate different optimum settings for different pad pattern combinations.

scholarly journals Evaluating Representations for Gene Ontology Terms

2019 ◽  
Author(s):  
Dat Duong ◽  
Ankith Uppunda ◽  
Lisa Gai ◽  
Chelsea Ju ◽  
James Zhang ◽  
...  
Keyword(s):  
Neural Network ◽  
Gene Ontology ◽  
Network Models ◽  
Language Models ◽  
Future Research ◽  
Model Parameters ◽  
Neural Network Models ◽  
Go Annotation ◽  
Go Terms ◽  
Similarity Scores

AbstractProtein functions can be described by the Gene Ontology (GO) terms, allowing us to compare the functions of two proteins by measuring the similarity of the terms assigned to them. Recent works have applied neural network models to derive the vector representations for GO terms and compute similarity scores for these terms by comparing their vector embeddings. There are two typical ways to embed GO terms into vectors; a model can either embed the definitions of the terms or the topology of the terms in the ontology. In this paper, we design three tasks to critically evaluate the GO embeddings of two recent neural network models, and further introduce additional models for embedding GO terms, adapted from three popular neural network frameworks: Graph Convolution Network (GCN), Embeddings from Language Models (ELMo), and Bidirectional Encoder Representations from Transformers (BERT), which have not yet been explored in previous works. Task 1 studies edge cases where the GO embeddings may not provide meaningful similarity scores for GO terms. We find that all neural network based methods fail to produce high similarity scores for related terms when these terms have low Information Content values. Task 2 is a canonical task which estimates how well GO embeddings can compare functions of two orthologous genes or two interacting proteins. The best neural network methods for this task are those that embed GO terms using their definitions, and the differences among such methods are small. Task 3 evaluates how GO embeddings affect the performance of GO annotation methods, which predict whether a protein should be labeled by certain GO terms. When the annotation datasets contain many samples for each GO label, GO embeddings do not improve the classification accuracy. Machine learning GO annotation methods often remove rare GO labels from the training datasets so that the model parameters can be efficiently trained. We evaluate whether GO embeddings can improve prediction of rare labels unseen in the training datasets, and find that GO embeddings based on the BERT framework achieve the best results in this setting. We present our embedding methods and three evaluation tasks as the basis for future research on this topic.

PREDICTING SOFTWARE QUALITY, DURING TESTING, USING NEURAL NETWORK MODELS: A COMPARATIVE STUDY

1994 ◽  
Vol 01 (03) ◽  
pp. 303-319 ◽  
Author(s):  
TAGHI M. KHOSHGOFTAAR ◽  
ROBERT M. SZABO
Keyword(s):  
Neural Network ◽  
Software Quality ◽  
Principal Components ◽  
Network Modeling ◽  
Network Models ◽  
Neural Nets ◽  
Neural Network Modeling ◽  
Neural Network Models ◽  
Predictive Quality

The application of statistical modeling techniques has been an intensely pursued area of research in the field of software engineering. The goal has been to model software quality and use that information to better understand the software development process. Neural network modeling methods have recently been applied to this field. The results reported indicate that neural network models have better predictive quality than some statistical models when predicting reliability and the number of faults. In this paper, we will investigate the application of principal components analysis to neural network modeling as a way of improving the predictive quality of neural network quality models. Using data we collected from a large commercial software system, we developed a multiple regression model using the principal components. Then, we trained two neural nets, one with raw data, and one with principal components. Then, we compare the predictive quality of the three competing models for a variety of quality measures.

SYSTEM ORIENTED NEURAL NETWORKS — PROBLEM FORMULATION, METHODOLOGY AND APPLICATION

2006 ◽  
Vol 20 (02) ◽  
pp. 143-158 ◽  
Author(s):  
KANG LI ◽  
JIAN-XUN PENG
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Network Model ◽  
Neural Network Model ◽  
Fitness Function ◽  
A Priori ◽  
Network Models ◽  
Problem Formulation ◽  
Neural Network Modeling ◽  
Neural Network Models

A novel methodology is proposed for the development of neural network models for complex engineering systems exhibiting nonlinearity. This method performs neural network modeling by first establishing some fundamental nonlinear functions from a priori engineering knowledge, which are then constructed and coded into appropriate chromosome representations. Given a suitable fitness function, using evolutionary approaches such as genetic algorithms, a population of chromosomes evolves for a certain number of generations to finally produce a neural network model best fitting the system data. The objective is to improve the transparency of the neural networks, i.e. to produce physically meaningful "white box" neural network model with better generalization performance. In this paper, the problem formulation, the neural network configuration, and the associated optimization software are discussed in detail. This methodology is then applied to a practical real-world system to illustrate its effectiveness.

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