Knowledge discovery for friction stir welding via data driven approaches: Part 1 – correlation analyses of internal process variables and weld quality

Zhang, Qian and Mahfouf, Mahdi and Panoutsos, George and Beamish, Kathryn and Norris, Ian (2012) Knowledge discovery for friction stir welding via data driven approaches: Part 1 – correlation analyses of internal process variables and weld quality. Science and Technology of Welding and Joining, 17 (8). pp. 672-680. ISSN 1362-1718. (doi:https://doi.org/10.1179/1362171812Y.0000000061) (Full text available)

Abstract

For a comprehensive understanding towards Friction Stir Welding (FSW) which would lead to a unified approach that embodies materials other than aluminium, such as titanium and steel, it is crucial to identify the intricate correlations between the controllable process conditions, the observable internal process variables, and the characterisations of the post-weld materials. In Part I of this paper, multiple correlation analyses techniques have been developed to detect new and previously unknown correlations between the internal process variables and weld quality of aluminium alloy AA5083. Furthermore, a new exploitable weld quality indicator has, for the first time, been successfully extracted, which can provide an accurate and reliable indication of the ‘as-welded’ defects. All results relating to this work have been validated using real data obtained from a series of welding trials that utilised a new revolutionary sensory platform called ARTEMIS developed by TWI Ltd., the original inventors of the FSW process.

Item Type: Article
Uncontrolled keywords: Friction stir welding; Aluminium alloy; Correlation analysis; Frequency analysis; Wavelet-based analysis; Reduced space searching algorithm
Subjects: Q Science > Q Science (General) > Q335 Artificial intelligence
T Technology > TA Engineering (General). Civil engineering (General) > TA168 Systems engineering, cybernetics and intelligent systems
T Technology > TA Engineering (General). Civil engineering (General) > TA 403 Materials Science
Divisions: Faculties > Sciences > School of Engineering and Digital Arts > Instrumentation, Control and Embedded Systems
Depositing User: Qian Zhang
Date Deposited: 18 Sep 2015 01:46 UTC
Last Modified: 22 Sep 2015 10:58 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/50511 (The current URI for this page, for reference purposes)
  • Depositors only (login required):

Downloads

Downloads per month over past year