Paper URL: https://www.ijtsrd.com/engineering/mechanical-engineering/29169/ann-model-based-calculation-of-tensile-of-friction-surfaced-tool-steel/v-pitchi-raju
ugc approved science journal, languages journal, research papers
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.
international journal of science, call for paper pharmacy, ugc approved journals with low publication fees
In the current trends of optimizing machining process parameters, various evolutionary techniques such as Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Simulated Annealing (SA), Ant Colony Optimization (ACO) and Artificial Bee Colony algorithm (ABC) have been used. This paper gives an overview of PSO techniques to optimize machining process parameter of both traditional and modern machining. Machining process parameters such as cutting speed, depth of cut and radial rake angle are mostly considered by researchers in order to minimize or maximize machining performances. From the review, the most machining process considered in PSO was multi-pass turning while the most considered machining performance was production costs
