scholarly journals Improving Non-Destructive Test Results Using Artificial Neural NetworksImproving Non-Destructive Test Results Using Artificial Neural Networks

2015 ◽  
Vol 5 (6) ◽  
pp. 480-483 ◽  
Author(s):  
Yi-Fan Shih ◽  
◽  
Yu-Ren Wang ◽  
Shih-Shian Wei ◽  
Chin-Wen Chen
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Test Results ◽  
Destructive Test ◽  
Artificial Neural ◽  
Non Destructive

Asphalt concrete stability estimation from non-destructive test methods with artificial neural networks

2012 ◽  
Vol 23 (3-4) ◽  
pp. 989-997
Author(s):  
Serdal Terzi ◽  
Mustafa Karaşahin ◽  
Süleyman Gökova ◽  
Mustafa Tahta ◽  
Nihat Morova ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Asphalt Concrete ◽  
Test Methods ◽  
Stability Estimation ◽  
Destructive Test ◽  
Artificial Neural ◽  
Non Destructive

scholarly journals Non-Destructive Micromagnetic Determination of Hardness and Case Hardening Depth Using Linear Regression Analysis and Artificial Neural Networks

Metals ◽  
2020 ◽  
Vol 11 (1) ◽  
pp. 18
Author(s):  
Rahel Jedamski ◽  
Jérémy Epp
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Regression Analysis ◽  
Artificial Neural Networks ◽  
Linear Regression ◽  
Linear Regression Analysis ◽  
Case Hardening ◽  
Artificial Neural ◽  
Non Destructive

Non-destructive determination of workpiece properties after heat treatment is of great interest in the context of quality control in production but also for prevention of damage in subsequent grinding process. Micromagnetic methods offer good possibilities, but must first be calibrated with reference analyses on known states. This work compares the accuracy and reliability of different calibration methods for non-destructive evaluation of carburizing depth and surface hardness of carburized steel. Linear regression analysis is used in comparison with new methods based on artificial neural networks. The comparison shows a slight advantage of neural network method and potential for further optimization of both approaches. The quality of the results can be influenced, among others, by the number of teaching steps for the neural network, whereas more teaching steps does not always lead to an improvement of accuracy for conditions not included in the initial calibration.

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.

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.

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.

Sign in / Sign up

Export Citation Format

Share Document