A durability analysis of forging tools for different operating conditions with application of a decision support system based on artificial neural networks (ANN)

Hawryluk, Marek; Mrzygłód, B.

doi:10.17531/ein.2017.3.4

Cited by 26 publications

(13 citation statements)

References 23 publications

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“…This chapter presents four machine learning methods that can be used in tomographic image reconstruction processes: Neural Networks with Multilayer Perceptron, Supported Vector Machine, K-Nearest Neighbour and Multivariate Adaptive Regression Splines (MAR Splines) [4,5,7,9].…”

Section: Machine Learning In Tomographic Image Reconstructionmentioning

confidence: 99%

Applying Machine Learning Algorithms to Solve Inverse Problems in Electrical Tomography

Rymarczyk

Kłosowski

2018

MATEC Web Conf.

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The article presents four selected methods of supervised machine learning, which can be successfully used in the tomography of flood embankments, walls, tanks, reactors and pipes. A comparison of the following methods was made: Artificial Neural Networks (ANN), Supported Vector Machine (SVM), K-Nearest Neighbour (KNN) and Multivariate Adaptive Regression Splines (MAR Splines). All analysed methods concerned regression problems. Thanks to performed analysis the differences expressed quantitatively were visualized with the use of indicators such as regression, error of mean square deviation, etc. Moreover, an innovative method of denoising tomographic output images with the use of convolutional auto-encoders was presented. Thanks to the use of a convolutional structure composed of two autoencoders, a significant improvement in the quality of the output image from the ECT tomography was achieved.

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Section: Machine Learning In Tomographic Image Reconstructionmentioning

confidence: 99%

Applying Machine Learning Algorithms to Solve Inverse Problems in Electrical Tomography

Rymarczyk

Kłosowski

2018

MATEC Web Conf.

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“…This makes it possible to construct computer systems which enable a partial replacement of the costly and time-consuming material experiments performed by way of computer simulations. In the literature, one can find many publications describing computer systems developed to support the forging processes [22][23][24][25][26][27][28][29][30][31][32][33][34]. The main goal of all these works is to investigate the financial and ecological aspects of materials production, combined with the scientific and cognitive goals aiming at a continuous improvement of the forging process technology and development of new solutions and technologies [1].In [28], an artificial neural network was developed.…”

Section: Introductionmentioning

confidence: 99%

“…The best matching and the smallest error were, however, achieved with the use of artificial neural networks. The works [24][25][26] present a methodology of system creation, the parameters and architectures of the developed networks as well as an analysis of the results obtained by means of the elaborated system. This study concerns a sensitivity analysis of artificial neural networks developed for a system predicting the durability of forging tools used in hot die forging processes.…”

Section: Introductionmentioning

confidence: 99%

Sensitivity analysis of the artificial neural networks in a system for durability prediction of forging tools to forgings made of C45 steel

Mrzygłód

Hawryluk

Janik

et al. 2020

Int J Adv Manuf Technol

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The article presents the results of a sensitivity analysis of artificial neural networks developed for a system which predicts the durability of forging tools used in the selected hot die forging process. The developed system makes it possible to calculate the geometric loss of the examined tool for the given values of its operating parameters (number of forgings, tool temperature at selected points, type of the applied protective layer, pressure and path of friction) and estimates the intensity of the occurrence of typical mechanisms of tool destruction, i.e. thermo-mechanical fatigue, mechanical wear, abrasive wear and plastic deformation. Nine neural networks operate in the developed system. Five of them determine the geometric loss of the material used for tools operating with protective layers, including a nitrided layer, a pad welded layer and three hybrid layers, i.e. AlCrTiSiN, Cr/CrN and Cr/AlCrTiN. Four networks make calculations determining the intensity of the occurrence of typical destructive mechanisms. The developed sensitivity analysis allows for each neural network to show which input parameters are most important and have the greatest impact on the explained variables. This is determined based on the network error analysis in the case of elimination of individual variables from the input data. The greater the network error calculated after rejecting an input variable relative to the error obtained for the network with all the input variables, the more sensitive the network to the lack of this variable. The best compliance was obtained for the first developed set of networks regarding the geometric loss of material, while the lowest compliance was obtained for the second developed set of networks regarding the applied protective layers, and in particular for plastic deformation and mechanical fatigue, probably due to the smallest size of these sets in the knowledge base. The obtained results of this analysis are important for the system operation, i.e. supporting the technologist's decision in the selection of such process parameter values that will increase the die's lifetime.

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“…Less frequently used methods to obtain the same purpose are: integer linear programming [10], Support Vector Machine [17], non-linear criterion of shear stability [23], and artificial neural networks (ANN) [1,8]. Currently, the main application area of ANN are predictive issues which aim at enhance exploitation processes by identifying [16] or classifying faults [26].…”

Section: Introductionmentioning

confidence: 99%

Application of neural reconstruction of tomographic images in the problem of reliability of flood protection facilities

Rymarczyk¹,

Kłosowski²

2018

Eksploatacja i Niezawodność – Maintenance and Reliability

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Article citation info: (*) Tekst artykułu w polskiej wersji językowej dostępny w elektronicznym wydaniu kwartalnika na stronie www.ein.org.pl IntroductionThe exploitation of flood defences -such as dams and flood banks -is of key value that heavily impacts the security of people, animals and plants that lay within the area of the object. There are two main types of issues that may appear as a result of improper exploitation of such pieces of infrastructure. The first threat is a physical damage of the embankment, which may result in a breach. The other is the leakage of such a structure -in case of reservoirs that contain fluid chemical compound waste it might lead to a vast range of contamination.

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A durability analysis of forging tools for different operating conditions with application of a decision support system based on artificial neural networks (ANN)

Cited by 26 publications

References 23 publications

Applying Machine Learning Algorithms to Solve Inverse Problems in Electrical Tomography

Applying Machine Learning Algorithms to Solve Inverse Problems in Electrical Tomography

Sensitivity analysis of the artificial neural networks in a system for durability prediction of forging tools to forgings made of C45 steel

Application of neural reconstruction of tomographic images in the problem of reliability of flood protection facilities

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