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, present the prediction of tensile 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 tensile tool steel sediments caused by friction.
Friction surfacing, Artificial Neural Networks (ANN), Process Parameters
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