Open AccessUse of Orthogonal Arrays in Design of a Fuzzy Logic Controller to Predict the Proof Stress for the TIG Welded Al-65032
Author(s) -
K. Ankamma*,
D. R. Reddy
Publication year - 2020
Publication title -
international journal of innovative technology and exploring engineering
Language(s) - English
Resource type - Journals
ISSN - 2278-3075
DOI - 10.35940/ijitee.g5857.059720
Subject(s) - gas tungsten arc welding , welding , inert gas , fuzzy logic , groove (engineering) , controller (irrigation) , base (topology) , mechanical engineering , stress (linguistics) , arc welding , materials science , control theory (sociology) , computer science , mathematics , engineering , composite material , mathematical analysis , agronomy , linguistics , philosophy , artificial intelligence , biology , control (management)
Fuzzy logic controller (FLC) is well suited where there is a considerable amount of uncertainty in the process. The material properties of a weldment in TIG welding depend on welding parameters like shielding gas pressure, current, torch angle, Electrode size, electrode projection, arc length etc. It is also influenced by the joint parameters like groove angle, land, root gap, preheating temperature. But a lot of noise parameters like variation of base material properties, variation in quality of inert gas used, variation in ambient conditions, variation in workman ship etc introduce uncertainties in the into the process. To deal with such uncertainties an FLC is designed and validated. In the current work, four parameters namely inert gas pressure, current, groove angle of the joint and preheating temperature of base metal are considered as input variables and the influence of these variables on the 0.2% proof stress is studied. Three linguistic terms are used for each parameter. To minimise the number of experiments in designing data base an L-9 orthogonal array is chosen for experimentation. TIG welding is carried and data base with 9 rules are formulated. For the input and out variables Triangular membership function is selected and FLC is designed. The FLC is validated with 5 more experiments. Mamdani approach is used to develop the Fuzzy controller.