Application of support vector regression in predicting thickness strains in hydro-mechanical deep drawing and comparison with ANN and FEM

Singh, Swadesh Kumar; Gupta, Amit Kumar

doi:10.1016/j.cirpj.2010.07.005

Cited by 19 publications

(5 citation statements)

References 14 publications

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“…KVD Rajesh et al finds the microstructural and corrosion resistance study on plasma arc welded joints of AISI 304 and AISI 316. Subbiah, Ram, et al worked-on wear behaviour of Al-2014 Alloy Coated by varying processes [27]. Reddy, D. Mahesh, has worked on formability of Al 8011 alloy sheets under uniaxial test condition [28].…”

Section: Formability Of 6xxx Series Aluminium Alloymentioning

confidence: 99%

Formability Studies of Automotive Aluminium Alloy Sheet series: A Review

Lakshmi¹,

Buddi²,

Subbiah³

et al. 2020

E3S Web Conf.

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The job of aluminium alloys in car and aircraft industries has been extending fundamentally over the most recent 20 years. Because of their low thickness to weight proportion and high explicit quality, aluminium turned into a solid trade for steel especially for car producing. However, to stamp a convoluted board parts from aluminium sheet is very troublesome explicitly at cold working temperatures where as far as possible are very less. To enhance formability breaking points of aluminium alloy sheet a few procedures are joined by numerous specialists like warm shaping, hot forming, superplastic forming, cold die quench (HFQ) forms and so on. This paper displays a basic study of various procedures utilized to enhance aluminium alloy sheet formability and distinguishing advantages and downsides for each procedure

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Section: Formability Of 6xxx Series Aluminium Alloymentioning

confidence: 99%

Formability Studies of Automotive Aluminium Alloy Sheet series: A Review

Lakshmi¹,

Buddi²,

Subbiah³

et al. 2020

E3S Web Conf.

View full text Add to dashboard Cite

show abstract

“…A better choice of neural network structure is a very important factor to ensure accurate results, the choice of function and learning algorithm, the choice of number of hidden layers and the neurons in each hidden layer are all factors that directly affect network performance [22].…”

Section: Artificial Neural Network Model Of Gtn Damage (Anngtn)mentioning

confidence: 99%

Parameter identification of a mechanical ductile damage using Artificial Neural Networks in sheet metal forming

Abbassi¹,

Belhadj²,

Mistou

et al. 2013

Materials & Design

117

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“…4). Increasing the insensitivity zone (error level ) can reduce the accuracy of the prediction, however, this increase can lead to smooth effects on modelling of the data sets that include the high level of noise (Na et al, 2008;Singh & Gupta, 2010;Vapnik et al, 1997).…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Expectation of Tourism Demand in Iraq by Using Artificial Neural Network

Anasari

2019

IJSSRR

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This study survey and proves this effectiveness connected with artificial neural networks (ANNs) as an alternative approach in the tourism research. The learning utilizes the travel industry in the Japan being a method for estimating need to exhibit the solicitation. The outcome reveals the use of ANNs in tourism research might perhaps result in better quotations when it comes to prediction bias and accuracy. Even more applications of ANNs in the context of tourism demand examination is needed to establish and validate the effects. An ANN is based on a collection of connected units or nodes called artificial neurons, which loosely model the neurons in a biological brain. Each connection, like the synapses in a biological brain, can transmit a signal to other neurons. An artificial neuron that receives a signal then processes it and can signal neurons connected to it.

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Application of support vector regression in predicting thickness strains in hydro-mechanical deep drawing and comparison with ANN and FEM

Cited by 19 publications

References 14 publications

Formability Studies of Automotive Aluminium Alloy Sheet series: A Review

Formability Studies of Automotive Aluminium Alloy Sheet series: A Review

Parameter identification of a mechanical ductile damage using Artificial Neural Networks in sheet metal forming

Expectation of Tourism Demand in Iraq by Using Artificial Neural Network

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