Thermal Conductivity of Cu-Zn Hybrid Newtonian Nanofluids: Experimental Data and Modeling using Neural Network

Mechiri, Sandeep Kumar; Vasu, V.; Gopal, A. Venu; Babu, Roshina

doi:10.1016/j.proeng.2015.11.345

Cited by 26 publications

(2 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…There have been many books 15 describing the details of ANN and many studies in different fields. 16,17 The next article, which is similar to the subject of this study, is one of the rare articles studied on FGMs and artificial neural networks in the open literature. Trinh and Jun, 18 examined the influence of uncertainties of geometry, boundary condition, and material properties on the vibration of FG beams.…”

Section: Introductionmentioning

confidence: 95%

An approach for predicting longitudinal free vibration of axially functionally graded bar by artificial neural network

Demir

Sayer

Çallıoğlu

2022

Proceedings of the Institution of Mechanical Engineers, Part C:

View full text Add to dashboard Cite

In this study, an Artificial Neural Network (ANN) model is designed for the analysis of the longitudinal free vibration of an axially Functional Graded (FG) bar. The material of the bar is varied from one end to the other end according to power law function. Aluminum and Alumina and the mixture of these two materials are used in the analysis. The radius of the bar is also varied along to the longitudinal direction according to power law function. In addition, the effect of the variation in the slenderness ratio is also investigated. An ANN code is written in Matlab in order to obtain a generalized approach for vibration analysis of the FG bar. In the ANN training, some results of free vibration analysis obtained from the SolidWorks program based on Finite Element Method are used. The predicted values that are not used in the training are also obtained in SolidWorks for comparison. A Matlab program code, which based ANN method is used to find new values to be predicted. The process is also made for comparison with the ANN based Matlab nntool program. When the results obtained from SolidWorks and prepared Matlab code and Matlab nntool are compared, it is seen that the results are in harmony. AMS subject classifications: 74H45, 68T07

show abstract

Section: Introductionmentioning

confidence: 95%

An approach for predicting longitudinal free vibration of axially functionally graded bar by artificial neural network

Demir

Sayer

Çallıoğlu

2022

Proceedings of the Institution of Mechanical Engineers, Part C:

View full text Add to dashboard Cite

show abstract

“…Soybean oil with the inclusion of MoS2 (3-5µm) solid particles with MQL mode was performed better than paraffin oil based nanofluid in grinding in terms of reduction in specific grinding energy, coefficient of friction and grinding ratio (Kalitha et al, 2012).In addition to mono type cutting fluids, the combination of different nano sized material or solid particles in base fluid (it is termed as hybrid type nano fluids) performing better in machining. Very few investigations were in process on hybrid nanofluids in machining (Mechiri et al, 2015;Zareie and Akbari, 2017;Singh et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Optimizing Multi-Response Parameters in Turning of AISI1040 Steel Using Desirability Approach

Gugulothu¹,

Krishna²

2019

Int J Math, Eng, Manag Sci

View full text Add to dashboard Cite

In this study, an attempt is made to examine the machining response parameters in turning of AISI 1040 steel under different lubrication environment. Subsequently, design of experiment technique Response surface methodology (RSM) is used for analyzing machining performance by varying cutting conditions with the use of 2wt% of CNT/MoS2(1:2) HNCF. Regression models are developed for multiple machining responses. Optimization is performed for these models by using desirability function, which converts multi-objective into single objective. Then the optimal setting parameters for single objective is found. Significant reduction in main cutting force (Fz), cutting temperature (T), surface roughness(Ra) and tool flank wear (Vb) are found with the use of 2wt% of CNT/MoS2(1:2) HNCF compared to other lubrication environment. Significant factors that affect the main cutting force (Fz), the temperature in the cutting zone are cutting speed, feed rate and depth of cut. Parameter depth of cut has an insignificant effect on tool flank wear and surface roughness (Ra). The optimal cutting conditions for four multi-objective optimization of main cutting force (Fz), cutting temperature, surface roughness (Ra) and tool flank wear are found to be cutting speed 70.25 m/min, feed 0.13 mm/rev and doc 0.5mm at desirability value of 0.907.

show abstract

Thermal conductivity of nanoresins as a function of particle concentration determined by thermal lens and thermal interferometry techniques

et al. 2021

View full text Add to dashboard Cite

Thermal Conductivity of Cu-Zn Hybrid Newtonian Nanofluids: Experimental Data and Modeling using Neural Network

Cited by 26 publications

References 17 publications

An approach for predicting longitudinal free vibration of axially functionally graded bar by artificial neural network

An approach for predicting longitudinal free vibration of axially functionally graded bar by artificial neural network

Optimizing Multi-Response Parameters in Turning of AISI1040 Steel Using Desirability Approach

Thermal conductivity of nanoresins as a function of particle concentration determined by thermal lens and thermal interferometry techniques

Contact Info

Product

Resources

About