Durability analysis of forging tools after different variants of surface treatment using a decision-support system based on artificial neural networks

Mrzygłód, B.; Hawryluk, Marek; Gronostajski, Z.; Opaliński, Andrzej; Kaszuba, M.; Polak, S.; Widomski, Paweł; Ziemba, Jacek; Zwierzchowski, M.

doi:10.1016/j.acme.2018.02.010

Cited by 21 publications

(17 citation statements)

References 26 publications

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“…These coefficients that are calculated according to Eq. (9) give information about the relationship between experimental and estimated results. While correlation coefficient of the training set is calculated as R 2 =0.913, it is determined as R 2 =0.891 for the testing set after analyses of velocity values.…”

Section: Resultsmentioning

confidence: 99%

Estimating Fluid Parameters of Submarine Outfall Using Neural Networks

Gücüyen¹,

Erdem²

2020

DÜMF Mühendislik Dergisi

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Disposal of the urban and industrial liquid waste has become important by paying attention to environmental and human health recently. Submarine outfall diffusers are the major parts of the marine disposal systems. Pipe of the diffuser, risers and ports, internal and external flows which form the discharge system are modelled and fluid-structure interaction (FSI) method is utilized by ABAQUS finite elements program. Coupled CFD & Explicit technique is performed in FSI analysis. Method of bidirectional fluid-structure interaction (FSI) is used in finite elements method (FEM). Internal and external flows constitute fluid domain and diffuser constitutes the structure domain. While internal velocity and pressure values are obtained from the program, predictions of these results are performed by Artificial Neural Network (ANN) analysis. The average discharge velocities provide to avoid water intrusion into the ports. According to results obtained by FEM it can be said that the discharge system works efficiently. Numerical and estimated values are compared and the relationship between these values is investigated. The correlation coefficients are calculated by using numerical and estimated values and it is observed that a strong relationship is obtained between them.

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Section: Resultsmentioning

confidence: 99%

Estimating Fluid Parameters of Submarine Outfall Using Neural Networks

Gücüyen¹,

Erdem²

2020

DÜMF Mühendislik Dergisi

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“…Figure 2 presents a fragment of the developed set, while the details related to the knowledge acquisition are described in ref. [24,25].…”

Section: Data Setmentioning

confidence: 99%

“…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%

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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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“…Oxidation and tempering of the surface layer occurs at sites exposed to long-lasting contact with the hot material of the forging, and in combination with high loads, this may lead to plastic deformations. Moreover, a mesh of cracks typical of thermo-mechanical fatigue is frequently observed throughout the entire surface [7]. The impact of the aforementioned mechanisms on tool lifetime is generally analyzed separately for each mechanism, and no precise, global physical description of the wear process is known that would account for all phenomena simultaneously, just as they occur in reality [8].…”

Section: Introductionmentioning

confidence: 99%

The application of a new, innovative, hybrid technology combining hardfacing and nitriding to increase the durability of forging tools

Kaszuba

2020

Archiv.Civ.Mech.Eng

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The article deals with the wear of forging tools used in hot forging processes. The research presented in the work includes analysis of tool life used in a selected industrial hot die forging process. Multiple treatment variants were used to increase wear resistance, including thermo-chemical treatment (nitriding), welding methods (surfacing) and an innovative new hybrid technology combining surfacing and nitriding. First of all, the research focused on determining the impact of the phase structure of the nitrided layers used and the surfacing layer on resistance to destructive factors occurring in the analyzed process. Next, hybrid treated tools combining surfacing and nitriding were also subjected to operational tests. Each of the tools analyzed in this work was operated until it was withdrawn due to excessive wear, and then subjected to comprehensive analysis. The tests of tools after operation included: surface scanning to determine the amount of wear of the analyzed tools after work, microhardness measurement, and microscopic tests. A detailed analysis of changes in the surface layer of tools in selected areas was made using a scanning microscope. The aim of the study was to assess the effectiveness of the hybrid surface treatment process used to increase the wear resistance of the surface layer of tools and thereby improve the durability of the forged tools analyzed. The obtained research results indicate a beneficial effect of using the new technology resulting in 300% increase in the durability of the analyzed tools. The effect of improving durability confirmed by obtained results arises from the use of hybrid layers, which are more resistant to abrasive wear and to cracking due to thermo-mechanical fatigue. Moreover, the study shows that nitriding may have a beneficial influence on improving the lifetime of forging tools, under the condition that the nitrided layer has an α diffusive layer structure, without a larger amount of γ’ and ε nitride precipitates.

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Durability analysis of forging tools after different variants of surface treatment using a decision-support system based on artificial neural networks

Cited by 21 publications

References 26 publications

Estimating Fluid Parameters of Submarine Outfall Using Neural Networks

Estimating Fluid Parameters of Submarine Outfall Using Neural Networks

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

The application of a new, innovative, hybrid technology combining hardfacing and nitriding to increase the durability of forging tools

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