Process parameters optimization of 5083 aluminum alloy FSW joint based on principal component analysis and grey correlation analysis

CHEN Xinghui; ZHANG Hongshen

doi:10.12073/j.hjxb.20220623001

TRANSACTIONS OF THE CHINA WELDING INSTITUTION > 2023 > 44(5): 62-69. > DOI: 10.12073/j.hjxb.20220623001

CHEN Xinghui, ZHANG Hongshen. Process parameters optimization of 5083 aluminum alloy FSW joint based on principal component analysis and grey correlation analysis[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2023, 44(5): 62-69. DOI: 10.12073/j.hjxb.20220623001

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Process parameters optimization of 5083 aluminum alloy FSW joint based on principal component analysis and grey correlation analysis

CHEN Xinghui,
ZHANG Hongshen^,

School of Electrical and Mechanical Engineering, Kunming University of Science and Technology, Kunming, 650500, China

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Received Date: June 22, 2022
Available Online: April 05, 2023

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Abstract

Abstract

Aim at optimizing the friction stir welding (FSW) process parameters of aluminum alloy, 5083 aluminum alloy plate was selected as the research object in this study. The mechanical properties of 5083 aluminum alloy plate under different FSW process parameters were explored. Principal component analysis (PCA) and grey relational degree (GRG) analysis were employed to analyze the test results. The optimal process parameters were obtained, and a second-order prediction model of process parameters based on GRG was established. The results show that the optimal combination of process parameters is a rotation speed of 1400 r/min, welding speed of 1 mm/s and plunge depth of 0.3 mm. The maximum tensile strength of the joint reaches 225.5 MPa, 95.4% of the base metal, and the failure displacement is 10.6 mm. Among the tested process parameters, the main factors influencing the tensile strength of the joint are plunge depth of shoulder, welding speed and rotation speed in sequence. Besides, there is no significant difference between the predicted value of the model and the calculated value. The good agreement between the regression model and the experimental data indicates that the prediction model has high reliability. The regression model can be used as the prediction model.
- 5083 aluminum alloy,
- process parameters,
- grey relational degree,
- tensile strength

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References (23)

References

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Process parameters optimization of 5083 aluminum alloy FSW joint based on principal component analysis and grey correlation analysis