![]() ![]() The neural network is able to predict the fillet stresses in 0.03 s with reasonable accuracy for spur gears having 25–125 teeth, a 1–5 mm module, a 0.05–0.45 mm fillet radius, and a 15°–25° pressure angle. ![]() TRAINLM function is used for training the network with a learning rate parameter of 0.01 and a momentum constant of 0.8. allow for the higher fillet trochoid produced by the shaperSPUR GEAR CALCULATOR. The 4-5-1 network and a sigmoid activation function are chosen. 0)36 (mm) The root diameter of a spur gear is obtained by the following. Training data are obtained from finite element simulations that are greatly reduced using Taguchi's design of experiments. Because the relationship is nonlinear and complex, an artificial neural network and a backpropagation algorithm are used in the present work to predict the fillet stresses. The gear root fillet equations are derived based on the simulation of cutting tool motion on the gear blank during the manufacturing process. The fillet stress is influenced by the fillet geometry as well as the number of teeth, modules, and the pressure angle of the gear. ![]() The fillet curve may be a trochoid or an arc of suitable size as specified by designer. It is generally defined in terms of normal module and is called. A fillet curve is provided at the root of the spur gear tooth, as stresses are high in this portion. Fillet radius at critical section of gear tooth root, on the other hand, is mainly affected by the tip radius of the cutting tool. ![]()
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