Applying of Neural Networks for Testing of Tracers with using of Empirical Data

2019 ◽  
Vol 218 (3) ◽  
pp. 5-23
Author(s):  
Dariusz Ampuła
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Test Results ◽  
Test Methodology ◽  
Laboratory Test Results ◽  
Depth Analysis ◽  
Evaluation Module ◽  
Artificial Neural ◽  
Marine Climate ◽  
Over 40 Years

Abstract An attempt of designing artificial neural networks for empirical laboratory test results tracers No. 5, No. 7 and No. 8 was introduced in the article. These tracers are applied in cartridges with calibres from 37 mm to 122 mm which are still used and stored both in the marine climate and land. The results of laboratory tests of tracers in the field of over 40 years of tests have been analysed. They have been properly prepared in accordance with the requirements that are necessary to design of neural networks. Only the evaluation module of these tracers was evaluated, because this element of tests, fulfilled the necessary assumptions needed to build artificial neural networks. Several hundred artificial neural networks have been built for each type of analysed tracers. After an in-depth analysis of received results, it was chosen one the best neural network, the main parameters of which were described and discussed in the article. Received results of working built of neural networks were compared with previously functioning manual evaluation module of these tracers. On the basis conducted analyses, proposed the modification of functioning test methodology by replacing the previous manual evaluation modules through elaborated automatic models of artificial neural networks. Artificial neural networks have a very important feature, namely they are used in the prediction of specific output data. This feature successfully used in diagnostic tests of other elements of ammunition.

Modeling of cutting parameters in turning of PEEK composite using artificial neural networks and adaptive-neural fuzzy inference systems

2021 ◽  
pp. 089270572110130
Author(s):  
Gökçe Özden ◽  
Mustafa Özgür Öteyaka ◽  
Francisco Mata Cabrera
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Cutting Forces ◽  
Fuzzy Inference ◽  
Test Results ◽  
Inference System ◽  
Good Prediction Accuracy ◽  
Peek Composite ◽  
Artificial Neural ◽  
Neural Fuzzy

Polyetheretherketone (PEEK) and its composites are commonly used in the industry. Materials with PEEK are widely used in aeronautical, automotive, mechanical, medical, robotic and biomechanical applications due to superior properties, such as high-temperature work, better chemical resistance, lightweight, good absorbance of energy and high strength. To enhance the tribological and mechanical properties of unreinforced PEEK, short fibers are added to the matrix. In this study, Artificial Neural Networks (ANNs) and the Adaptive-Neural Fuzzy Inference System (ANFIS) are employed to predict the cutting forces during the machining operation of unreinforced and reinforced PEEK with30 v/v% carbon fiber and 30 v/v% glass fiber machining. The cutting speed, feed rate, material type, and cutting tools are defined as input parameters, and the cutting force is defined as the system output. The experimental results and test results that are predicted using the ANN and ANFIS models are compared in terms of the coefficient of determination ( R2) and mean absolute percentage error. The test results reveal that the ANFIS and ANN models provide good prediction accuracy and are convenient for predicting the cutting forces in the turning operation of PEEK.

scholarly journals Application of Neural Networks in the Tests of Hand Grenade Fuses

2019 ◽  
Vol 68 (1) ◽  
pp. 197-212
Author(s):  
Dariusz Ampuła
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Diagnostic Tests ◽  
Evaluation Method ◽  
Empirical Test ◽  
Evaluation Process ◽  
Test Results ◽  
Linear Character ◽  
Wide Range ◽  
Artificial Neural

The neural networks, which find currently use in the unusually wide range of problems, in such fields as: finance, medicine, geology or physics, were characterized in the article. It was accent, that neural networks are very sophisticated technique of modelling, able to map extremely complex functions. It was noticed particularly, that neural networks had a non-linear character, what very essentially improve the possibilities of their applications. Some previous applications of neural networks were introduced, both in the area of domestic and foreign, including also military applications. The fuse of UZRGM type (Universal Modernized Fuse of Hand Grenades) was characterized, describing his building and way of action, special attention-getting on the tested features during laboratory diagnostic tests. Necessary technical parameters for the first and the second laboratory diagnostic tests, whose purpose was to build two independent neural networks, on the basis of existing test results and undertaken post-diagnostic decisions were designed. A few artificial neural networks were made and finally the best two independent neural networks were chosen. The main parameters of the chosen active neural networks were introduced in the pictures. Concise information, relating to the built artificial neural networks, for the first and the second laboratory diagnostic tests of the fuses of UZRGM type, was presented in the end of the article. In the summary, clearly distinguished are advantages of the applications of the proposed evaluation method, which significantly shortens an evaluation process of new empirical test results and causes complex automatization of an evaluation process of the tested fuses. Keywords: artificial intelligence, neural networks, activation function, hidden neurons, fuse.

scholarly journals Artificial Neural Networks Investigation of Indentation Force Effects on Nano- and Microhardness of Dual Phase Steels

2014 ◽  
Vol 2014 ◽  
pp. 1-11
Author(s):  
A. Fotovati ◽  
J. Kadkhodapour ◽  
S. Schmauder
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Volume Fraction ◽  
Nanoindentation Test ◽  
Dual Phase ◽  
Test Results ◽  
Indentation Force ◽  
Input Parameters ◽  
Artificial Neural ◽  
Selection Of

Nanoindentation test results on different grain sizes of dual phase (DP) steels are used to train artificial neural networks (ANNs). With selection of ferrite and martensite grain size, martensite volume fraction (MVF), and indentation force as input and microhardness, ferrite, and martensite nanohardness as outputs, six different ANNs are trained according to normalized datasets to predict hardness and their tolerances. A graphical user interface (GUI) is developed for a better investigation of the trained ANN prediction. The response of the ANN is analyzed in five case studies. In each case the variation of two input parameters on the output is analyzed when the other input parameters are kept constant. Reliable and reasonable results of ANN predictions are achieved in each case.

scholarly journals Neural Networks for Tested MG Artillery Fuses

2019 ◽  
Vol 49 (4) ◽  
pp. 157-186
Author(s):  
Dariusz Ampuła
Keyword(s):  
Neural Networks ◽  
Computer Simulation ◽  
Artificial Neural Networks ◽  
Test Results ◽  
Evaluation Data ◽  
Technical Parameters ◽  
Artificial Neural ◽  
Learning Test ◽  
The Way

Abstract The article presents the information about the usage of artificial neural networks. The automation process of neural networks of the analysed evaluation data results is highlighted. The kinds of MG type artillery fuses are described and the kinds of cartridges’ calibres, in which they are used, are also specified. The way of preparation of databases of test results to computer simulation is described. Building of neural networks determining the main technical parameters and sizes of learning, test and validation sets is characterized. The summary for chosen active neural networks for individual kinds of the analysed MG type artillery fuses is presented. Graphs of learning, values of sensibility indicators and fragments of prediction sheets for the chosen neural networks were shown.

scholarly journals Application of Artificial Neural Networks in Analysis of Time-Variable Optical Reflectance Spectra in Digital Light Projection Spectroscopy

Coatings ◽  
2021 ◽  
Vol 12 (1) ◽  
pp. 37
Author(s):  
Marek Gąsiorowski ◽  
Piotr Szymak ◽  
Leszek Bychto ◽  
Aleksy Patryn
Keyword(s):  
Neural Networks ◽  
Optical Properties ◽  
Artificial Neural Networks ◽  
Mean Squared Error ◽  
Reflectance Spectra ◽  
Training Methods ◽  
Optical Reflectance ◽  
Research Cycle ◽  
Evaluation Module ◽  
Artificial Neural

This article undertakes the subject matter of applying artificial neural networks to analyze optical reflectance spectra of objects exhibiting a change of optical properties in the domain of time. A compact Digital Light Projection NIRscan Nano Evaluation Module spectrometer was used to record spectra. Due to the miniature spectrometer’s size and its simplicity of measurement, it can be used to conduct tests outside of a laboratory. A series of plant-derived objects were used as test subjects with rapidly changing optical properties in the presented research cycle. The application of artificial neural networks made it possible to determine the aging time of plants with a relatively low mean squared error, reaching 0.56 h for the Levenberg–Marquardt backpropagation training method. The results of the other ten training methods for artificial neural networks have been included in the paper.

scholarly journals IMPLEMENTASI JARINGAN SARAF TIRUAN DENGAN METODE BACKPROPAGATION TERHADAP BIBIT TANAMAN KARET

2018 ◽  
Vol 1 (1) ◽  
pp. 1
Author(s):  
Rahmad Fauzi S.Pd., M.Kom.
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Quality Standards ◽  
Seed Color ◽  
Test Results ◽  
Accuracy Rate ◽  
Rubber Seed ◽  
Rubber Plantations ◽  
Artificial Neural

Availability of seeds is one of the critical success factors of increasing the productivity of rubber plantations, the empirical use of seeds as one component technology has made a great contribution in increasing the productivity of rubber plantations. To obtain plant materials of good quality, it is worth noting the procurement process as well as the quality standards of seeds produced, if all quality standards at every election seedlings to be planted, it is certain that the results will be planted in accordance with what had been planned as long as it is balanced with proper maintenance based technical. Artificial Neural Networks can be used to obtain information about the quality of rubber seedlings by using Backpropagation, observations and measurements of rubber seed 51 seeds were used as a sample, of 50 rubber seed of the 35 samples used as training data and 16 samples as test data, observations done by looking at the characteristics of rubber seed color, reflectivity, results marinade, long beans, broad beans and thick seeds. From the results of the training conducted by Artificial Neural Networks as many as 35 sample data by using architecture patterns 6 15 1 obtained accuracy rate of 94.29%, which means that the artificial neural network has been able to identify the quality of the rubber plant seeds, to prove the results of the training testing using a sample of 16 pieces of new data that has not been trained before, the test results showed the accuracy rate of 100%, of the test results can be concluded that the application of Artificial Neural Networks to identify quality rubber seedlings with architectural 6 15 1 more accurate compared to other architectures

Sign in / Sign up

Export Citation Format

Share Document