Artificial neural network prediction of aging effects on the wear behavior of IN706 superalloy

Palavar, Osman; Özyürek, Dursun; Kalyon, Ali

doi:10.1016/j.matdes.2015.05.055

Cited by 39 publications

(17 citation statements)

References 25 publications

(28 reference statements)

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“…BP NN uses the fastest descent method to learn the input and output data, and continuously adjusts the weights and thresholds of the network through the reverse propagation of errors, so that the error squared and minimum of the network. [45][46][47] In principle, the BP NN is able to approximate any function and map highly non-linear relationships. The method of BP NN modeling is a statistical modeling method based on more data, and the result is a statistical law.…”

Section: Modeling Methodsmentioning

confidence: 99%

A property-oriented design strategy for high performance copper alloys via machine learning

et al. 2019

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Traditional strategies for designing new materials with targeted property including methods such as trial and error, and experiences of domain experts, are time and cost consuming. In the present study, we propose a machine learning design system involving three features of machine learning modeling, compositional design and property prediction, which can accelerate the discovery of new materials. We demonstrate better efficiency of on a rapid compositional design of high-performance copper alloys with a targeted ultimate tensile strength of 600-950 MPa and an electrical conductivity of 50.0% international annealed copper standard. There exists a good consistency between the predicted and measured values for three alloys from literatures and two newly made alloys with designed compositions. Our results provide a new recipe to realize the property-oriented compositional design for highperformance complex alloys via machine learning.

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Section: Modeling Methodsmentioning

confidence: 99%

A property-oriented design strategy for high performance copper alloys via machine learning

et al. 2019

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“…Absolutely, Das et al [8], justified the use of artificial neural network to develop relationship between cutting process parameters and surface roughness when machining of Al-4.5Cu-1.5TiC Metal Matrix Composites, by its capability to detect non-linear relationships. Moreover, Palavar et al [9], concluded that the prediction of aging effects on the wear behavior of Inconel 706 superalloy using ANN, can provide effective results and that the method can be effectively used to determine weight loss values in the determined parameters with a high coefficient of determination value. In addition, the ANN approach can save time in experimental processes and reduce costs as it provides quicker results.…”

Section: Introductionmentioning

confidence: 99%

On the Modeling of Surface Roughness and Cutting Force when Turning of Inconel 718 Using Artificial Neural Network and Response Surface Methodology: Accuracy and Benefit

Tebassi¹,

Yallese²,

Meddour³

et al. 2017

Period. Polytech. Mech. Eng.

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IntroductionThe Inconel 718 is one of the most important materials used in modern industries. In addition of the best properties in terms of high strength, corrosion resistance, heat resistance and fatigue resistance, the Inconel 718 has, also a low thermal conductivity as it is mentioned by Lynch [1]. Certainly, this type of alloy is difficult to machine for the following reasons as it is presented by Alauddin [2]: High work hardening rates at machining, strain rates leading to high cutting forces; abrasiveness; toughness, gummy and strong tendency to weld to the tool with forming the built-up edge; low thermal properties leading to high cutting temperatures. However, it has a wide variety of applications such as aircraft gas turbines stack gas reheaters, reciprocating engines and others.In order to respond to the requirements of those applications, it is very important to forecasting surface roughness and cutting force. Consequently, it is necessary to search the best modeling approach of these output parameters. To obtain this objective, several approaches can be used as well as response surface methodology (RSM) and artificial neural network (ANN). Response surface methodology (RSM) is considered as a quick and useful procedure for the investigation and optimization of complex processes as well as modeling machining output parameters. Certainly, Davoodi and Eskandari [3] found that response surface methodology represents a better approach to predict tool life and productivity when turning of N-155 iron-nickel-base superalloy. Shihab et al. [4], investigated cutting temperature during hard turning of AISI 52100 alloy steel using multilayer coated carbide insert; they concluded that the developed RSM model is able to predict cutting temperature for different combination of input parameters very close to experimental values. Arokiadass et al. [5], used response surface methodology to modeling tool flank wear when end milling of LM 25 Al/SiCp with carbide tool. They concluded that the developed relationship can be effectively used to predict flank wear of carbide tool at a confidence level of 95%. Sahoo et al. [6], confirmed this conclusion when studying the development of flank wear model in turning hardened EN 24 steel with PVD TiN coated mixed ceramic insert under dry environment.

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“…Where t= T-T' and p = P-P' in which the T and P are target and predicted output, T' and P' are its mean with n being the number of samples. [25].…”

Section: ……………………………… (5)mentioning

confidence: 99%

“…Computational models for weight loss predictions were prepared with aging time, load and sliding distance as inputs. Developed model expressed higher reliability and predictability [25]. ANN have been used by researchers and found to be effective for studying the varying properties of different aluminum alloys in a range of applications [26][27][28].…”

Section: Introductionmentioning

confidence: 99%

Artificial neural network to predict the degraded mechanical properties of metallic materials due to the presence of hydrogen

Thankachan

Prakash

Pleass

et al. 2017

International Journal of Hydrogen Energy

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Artificial neural network prediction of aging effects on the wear behavior of IN706 superalloy

Cited by 39 publications

References 25 publications

A property-oriented design strategy for high performance copper alloys via machine learning

A property-oriented design strategy for high performance copper alloys via machine learning

On the Modeling of Surface Roughness and Cutting Force when Turning of Inconel 718 Using Artificial Neural Network and Response Surface Methodology: Accuracy and Benefit

Artificial neural network to predict the degraded mechanical properties of metallic materials due to the presence of hydrogen

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