NDLI: Variable Neighborhood Strategy Adaptive Search for Optimal Parameters of SSM-ADC 12 Aluminum Friction Stir Welding

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Variable Neighborhood Strategy Adaptive Search for Optimal Parameters of SSM-ADC 12 Aluminum Friction Stir Welding

Content Provider	MDPI
Author	Chainarong, Suppachai Srichok, Thanatkij Pitakaso, Rapeepan Sirirak, Worapot Khonjun, Surajet Akararungruangku, Raknoi
Copyright Year	2021
Description	In this study, we present a new algorithm for finding the optimal friction stir welding parameters to maximize the tensile strength of a butt joint made of the semisolid material (SSM) ADC 12 aluminum. The welding parameters were rotational speed, welding speed, tool tilt, tool pin profile, and rotational direction. The method presented is a variable neighborhood strategy adaptive search (VaNSAS) approach. The process of finding the optimal friction stir welding parameters comprises five steps: (1) identifying the type and range of friction stir parameters using a literature survey; (2) performing experiments according to (1); (3) constructing a regression model using the response surface method optimizer (RSM optimizer); (4) using VaNSAS to find the optimal parameters for the model obtained from (3); and (5) confirming the results from (4) using the parameter levels obtained from (4) to perform real experiments. The computational results revealed that the tensile strength generated from VaNSAS was 3.67% higher than the tensile strength obtained from the RSM optimizer parameters. The optimal parameters obtained from VaNSAS were a rotation speed of 2200 rpm, a welding speed of 108.34 mm/min, a tool tilt of 1.23 Deg, a tool pin profile of a hexagon, and a rotational direction of clockwise.
Starting Page	1805
e-ISSN	22279717
DOI	10.3390/pr9101805
Journal	Processes
Issue Number	10
Volume Number	9
Language	English
Publisher	MDPI
Publisher Date	2021-10-11
Access Restriction	Open
Subject Keyword	Processes Manufacturing Engineering Friction Stir Welding Differential Evolution Algorithm D-optimal Ssm-adc 12
Content Type	Text
Resource Type	Article

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