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Optimierung der Prozessparameter zur Effizienzsteigerung der Verbindungsfestigkeit beim Rührreibschweißen von Polycarbonatplatten
Author(s) -
Sahu S.K.,
Mishra D.,
Pal K.
Publication year - 2021
Publication title -
materialwissenschaft und werkstofftechnik
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.285
H-Index - 38
eISSN - 1521-4052
pISSN - 0933-5137
DOI - 10.1002/mawe.202000205
Subject(s) - response surface methodology , welding , materials science , particle swarm optimization , fractional factorial design , friction stir welding , rotational speed , design of experiments , parametric statistics , ultimate tensile strength , structural engineering , joint (building) , central composite design , composite material , polycarbonate , factorial experiment , mechanical engineering , computer science , engineering , mathematics , algorithm , statistics , machine learning
Abstract The present work addresses optimization of these friction stir welding process variables to maximize joint strength efficiency of welded polycarbonate sheets by using particle swarm optimization algorithm over response surface method based regression model. Initially, parametric influence on weld quality characteristics namely weld bead profile, bead geometry with associated microstructure along with micro‐hardness deviation through the weld centerline and stress‐strain behavior of the weld have been studied in detail as per full factorial design of experiments by using three different tool pin profiles such as cylindrical, square and triangular. The center point experiment i. e. tool rotational speed of 1800 min −1 and welding speed of 20 mm/min, and square tool pin profile were found to be the optimum combination with a maximum joint strength efficiency of 60.06 %. The regression model of weld ultimate tensile strength was developed by using response surface methodology which was found to be significant. Therefore, this model was further used for parametric optimization by using both response surface methodology and intelligent particle swarm optimization approaches. A slight improvement in joint strength efficiency with better optimization capability was found by using particle swarm optimization technique as compared to response surface methodology.