Open AccessMathematical Model for Friction Stir Lap Welded AA5052 and SS304 Joints and Process Parameters Optimization for High Joint Strength
Author(s) -
Veerendra Chitturi,
Srinivasa Rao Pedapati,
Mokhtar Awang
Publication year - 2022
Publication title -
advances in materials science/advances in materials sciences
Language(s) - English
Resource type - Journals
eISSN - 2083-4799
pISSN - 1730-2439
DOI - 10.2478/adms-2022-0001
Subject(s) - taguchi methods , materials science , welding , rotational speed , orthogonal array , regression analysis , shear strength (soil) , joint (building) , linear regression , context (archaeology) , penetration depth , aluminium , structural engineering , shear (geology) , composite material , mechanical engineering , mathematics , statistics , engineering , paleontology , environmental science , physics , optics , biology , soil science , soil water
Due to the numerous challenges faced during the dissimilar welding, choosing the right process parameters and their optimization yields better results. In this context, the current investigation is focused on the optimization of process parameters. Taguchi’s L 9 orthogonal array was selected to carry out the experimental investigations. The welded samples were tested for shear strength, and the results were analysed using Taguchi’s S/N ratio analysis with “larger the better” criteria. Log-linear regression analysis was applied to formulate an empirical correlation between the process parameters and shear strength. According to S/N ratio analysis, the tool rotational speed of 800 rpm, welding speed of 20 mm/min and a penetration depth of 4.1 mm are the optimized parameters that achieve high joint strength. The achieved joint strength was 3.46 kN that is 70% of the base aluminium metal. It was noticed from the Analysis of variance of the regression model that penetration depth and tool rotational speed are the significant contributors with p-values less than 0.5. Confirmation tests show that the error between the predicted and calculated shear strength is 2.06% which is considered acceptable. R 2 and adjusted R 2 values of the model with a standard error of 0.076 show that the developed model is statistically significant.