Predicting Force in Single Point Incremental Forming by Using Artificial Neural Network

Oraon, Manish; Sharma, Vivek

doi:10.5829/ije.2018.31.01a.13

Cited by 12 publications

(7 citation statements)

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“…In multilayer neural networks, the back-propagation LM algorithm is used for network training. This algorithm has high accuracy and convergence speed in regression problems [23].…”

Section: Artificial Neural Network Approachmentioning

confidence: 99%

A neural network approach to estimate non-Newtonian behavior of nanofluid phase change material containing mesoporous silica particles

Motahar

2021

IJE

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Neural networks are powerful tools for evaluating the thermophysical characteristics of nanofluids to reduce the cost and time of experiments. Dynamic viscosity is an important property in nanofluids that usually needs to be accurately computed in heat transfer and nanofluid flow problems. In this paper, the rheological properties of nanofluid phase change material containing mesoporous silica nanoparticles are predicted by the artificial neural networks (ANNs) method based on the experimental database reported in literature. Experimental inputs include nanoparticle mass fractions (0-5 wt.%), temperatures (35-55℃), shear rates (10-200 s -1 ), targets include dynamic viscosities and shear stresses. A multilayer perceptron feedforward neural network with Levenberg-Marquardt back-propagation training algorithm is utilized to predict rheological properties. The optimal network architecture consists of 22 neurons in the hidden layer based on the minimum mean square error (MSE). The results showed that the developed ANN has an MSE of 6.67×10 -4 and 6.55×10 -3 for the training and test dataset, respectively. The predicted dynamic viscosity and shear stress also have the maximum relative error of 6.26 and 0.418%, respectively.

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“…In multilayer neural networks, the back-propagation LM algorithm is used for network training. This algorithm has high accuracy and convergence speed in regression problems [23].…”

Section: Artificial Neural Network Approachmentioning

confidence: 99%

A neural network approach to estimate non-Newtonian behavior of nanofluid phase change material containing mesoporous silica particles

Motahar

2021

IJE

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“…Although the deformation of high-strength and lightweight alloys are difficult, their applications are widely expanded in various industries, including the aerospace and automotive industries [1][2][3][4][5]. Hot metal gas forming (HMGF) is a high temperature forming processes, which serves for forming metal sheets and tubes using gas with low pressure.…”

Section: Introductionmentioning

confidence: 99%

Multi-objective Optimization of HMGF Process Parameters for Manufacturing AA6063 Stepped Tubes using FEM-RSM

Rajaee

Hosseinipour

Aval

2021

IJE

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“…The applications of lightweight materials, such as aluminum alloys are being widely expanded in various industries, including the aerospace and transportation industries [13][14][15][16][17]. Aluminum alloys can be suitable as bipolar plates because of their desirable mechanical properties, corrosion resistance, thermal conductivity, and low electrical resistance.…”

Section: Introductionmentioning

confidence: 99%

Numerical analyses of Manufacturing of an AA8111 Pin-shaped Bipolar Plate by Hot Metal Gas Forming

2021

IJE

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In this paper, fabrication of a pin-shaped bipolar plate for a proton exchange membrane fuel cell, the deformation behavior of a thin 8111 aluminum alloy sheet by the hot metal gas forming (HMGF) process was investigated. The effect of gas pressure, forming time, pin diameter, pin height, and die fillet radius on the specimen profiles, as well as the thickness distribution were analyzed by finite element method using ABAQUS 6.10 software. In addition, experimental tests were performed to validate numerical outputs. The results indicated that the sheet-thinning rate sharply grew by the gas pressure exceeding 4 MPa. The specimens were cracked following decreasing the die fillet radius to 0.1 mm. Furthermore, the die filling rate was high at the beginning of forming time and then diminished gradually after 1000 s. Moreover, the thickness reduced sharply by augmenting the ratio of pin height to diameter to over 0.4. Finally, perfect specimens were produced in experiments, which verified the feasibility of fabricating a pin-shaped bipolar plate out of a thin AA8111 sheet by the HMGF process.

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Predicting Force in Single Point Incremental Forming by Using Artificial Neural Network

Cited by 12 publications

References 0 publications

A neural network approach to estimate non-Newtonian behavior of nanofluid phase change material containing mesoporous silica particles

A neural network approach to estimate non-Newtonian behavior of nanofluid phase change material containing mesoporous silica particles

Multi-objective Optimization of HMGF Process Parameters for Manufacturing AA6063 Stepped Tubes using FEM-RSM

Numerical analyses of Manufacturing of an AA8111 Pin-shaped Bipolar Plate by Hot Metal Gas Forming

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