Evaluation of Knuckle Joint Performance with Altered Materials by Executing a Smart Prediction Tool

Abstract

The joint usually used to connect load under tensile stress is known as Knuckle joint. This works deals with numerical technique of altering materials like Steel, AL 6061-T6 and Teflon& calculating the parameters of maximum principal stress & maximum shear stress. Structural analysis was carried out on the Knuckle Joint at loads of 100N to 185 N. The best combination of parameters like deformation, shear stress and principal stresses for knuckle joint were calculated. From the results Teflon has more factor of safety and reduce the stress, weight than other material. After getting 54 numerical results 48 data sets are considered as a trainee data to establish mathematical relations between input and output parameters by using generalized regression neural networks and 6 data sets are used to test the trainee data. The established prediction tool is good agreement with trainee data and tested with an accuracy of 99.98%. The main advantage of GRNN is, without conducting experiments estimate the output values for a given inputs.