Identifying friction stir welding process parameters through coupled numerical and experimental analysis

Zhou, Xingguo and Pan, Wenke and Mackenzie, Donald (2013) Identifying friction stir welding process parameters through coupled numerical and experimental analysis. International Journal of Pressure Vessels and Piping, 108-109 (Aug-Se). 2–6. ISSN 0308-0161

[img] PDF (Mackenzie D et al - Identifying friction stir welding process parameters through coupled numerical and experimental anaysis Aug 2013)
Mackenzie_D_et_al_Identifying_friction_stir_welding_process_parameters_through_coupled_numerical_and_experimental_anaysis_Aug_2013.pdf
Final Published Version
Download (853kB)

    Abstract

    Friction Stir Welding (FSW) is a complex thermal-mechanical process. Numerical models have been used to calculate the thermal field, distortion and residual stress in welded components but some modeling parameters such as film coefficient and thermal radiation of the work pieces may be technically difficult and/or expensive to measure experimentally. Therefore, it is important to establish a systematic procedure to identify FSW process parameters. In this paper, a simplified finite element model for analysis of a FSW thermal progress is proposed in which two parameters, tool heat input rate and heat loss through the backing plate, are identified as parameters for optimization through application of a generic algorithm. A genetic algorithm is used to evaluate the two thermal parameters. By comparing the FEM numerical results with experimental results, the FSW process thermal parameters have been successfully identified. This automatic parameters characterization procedure could be used for the FSW process optimization.

    Author(s):Zhou, Xingguo ORCID logoORCID: https://orcid.org/0000-0001-8542-7510, Pan, Wenke and Mackenzie, Donald ORCID logoORCID: https://orcid.org/0000-0002-1824-1684
    Item type:Article
    ID code:45826
    Keywords:friction stir welding , genetic algorithms, automatic parameter identification, numerical modelling, Python, Mechanical engineering and machinery, Mining engineering. Metallurgy, Mechanical Engineering, Mechanics of Materials, Metals and Alloys
    Subjects:Technology > Mechanical engineering and machinery
    Technology > Mining engineering. Metallurgy
    Department:Faculty of Engineering > Mechanical and Aerospace Engineering
    Depositing user: Pure Administrator
    Date deposited:12 Nov 2013 12:00
    Last modified:14 Feb 2020 00:36
    Related URLs:
    URI:https://strathprints.strath.ac.uk/id/eprint/45826
    Export data:
    CORE (COnnecting REpositories)