Prediction Of Tensile And Shear Strength Of Friction Surfaced Tool Steel Deposit By Using Artificial Neural Networks

International Conference on Recent Advances in Materials & Manufacturing Technologies

Manzoor Hussain, M.^1 ; Pitchi Raju, V.^2 ; Kandasamy, J.^3 ; Govardhan, D.^4

Department of Mechanical Engineering, JNT University, Hyderabad, India^1

Mechanical Engineering Dept, ACE College of Engg. and Tech, Hyderabad, India^2

Dept. of Mech Engg, MVSR Engg. College, Nadergul, Hyderabad, India^3

Mechanical Engineering Dept, IARE, Dundigal (V), Hyderabad, India^4

关键词: Engineering components;    Friction pressure;    Friction surfaces;    Friction surfacing;    Neural network structures;    Process parameters;    Rotational speed;    Simulated results;   

Others  :  https://iopscience.iop.org/article/10.1088/1757-899X/346/1/012086/pdf
DOI  :  10.1088/1757-899X/346/1/012086

来源: IOP

【 摘 要 】

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.