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Paper Title: Observation of Shear Strength Frication Surfaced Tool Using Feed Forward Neural Network
Authors Name: V. Pitchi Raju
Unique Id: IJSDR1909024
Published In: Volume 4 Issue 9, September-2019
Abstract: An artificial neuron network (ANN) is a computational model is widely used for computation work in the engineering filed . ANN is used by the so many researcher for various worked here in this paper I am used the ANN as Computation model for prediction of Shear strength friction surfaced .ANNs are considered nonlinear statistical data modeling tools where the complex relationships between inputs and outputs are modeled or patterns are found. Friction surface treatment is well-established solid technology and is used for deposition, abrasion and corrosion protection coatings on rigid materials. This novel process has wide range of industrial applications, particularly in the field of reclamation and repair of damaged and worn engineering components. In this paper, we present the prediction of shear strength of friction surface treated tool steel using ANN for simulated results of friction surface treatment. This experiment was carried out to obtain tool steel coatings of low carbon steel parts by changing input process parameters such as friction pressure, rotational speed and welding speed. The simulation is performed by a 33-factor design that takes into account the maximum and minimum limits of the experimental work performed by the 23-factor design. Neural network structures, such as the Feed Forward Neural Network (FFNN), were used to predict shear strength of tool steel sediments caused by friction.
Keywords: Friction surfacing, Artificial Neural Networks (ANN), Process Parameters
Cite Article: "Observation of Shear Strength Frication Surfaced Tool Using Feed Forward Neural Network", International Journal of Science & Engineering Development Research (www.ijsdr.org), ISSN:2455-2631, Vol.4, Issue 9, page no.162 - 168, September-2019, Available :http://www.ijsdr.org/papers/IJSDR1909024.pdf
Downloads: 0001824
Publication Details: Published Paper ID: IJSDR1909024
Registration ID:190994
Published In: Volume 4 Issue 9, September-2019
DOI (Digital Object Identifier):
Page No: 162 - 168
Publisher: IJSDR | www.ijsdr.org
ISSN Number: 2455-2631

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