Prediction of Tensile and Shear Strength of Friction Surfaced Tool Steel Deposit by Using Artificial Neural Networks

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 tensile and 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 contribution process parameters essentially friction pressure, rotational speed and welding speed. The simulation is performed by a 3³-factor design that takes into account the maximum and least limits of the experimental work performed with the 2³-factor design. Neural network structures, such as the Feed Forward Neural Network (FFNN), were used to predict tensile and shear strength of tool steel sediments caused by friction. (paper)