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Tensile strength prediction by regression analysis for pulverized glass waste-reinforced aluminium alloy 6061-T6 friction stir weldments

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Abstract

AA6061-T6 is becoming a material of choice in the automobile, marine and aerospace industries because of its combination of relatively favourable and superior properties including high toughness, strength and excellent corrosion resistance. The major issue of concern about this material is the deterioration of these properties in the welded joint which has been established to improve through the additions of synthetic reinforcements such as SiC, WC, Al2O3, B4C and SiO2. This study seeks to investigate the quality of pulverised glass waste-reinforced friction stir welded joints of AA6061-T6 within a process window (rotational speed: 900-1400 rpm; traverse speed: 25-63 mm/min; tilt angle: 1° - 2.5°) as well as developing a regression model predicting the tensile strength of the pulverised waste glass-reinforced AA6061-T6 friction stir welded joints at varying process parameters. The tensile strength of the weldment was determined using Instron universal testing machine while the model was developed using a new statistical method (analysis of variance and hierarchy rule). The effects of the interaction of the parameters on the joint quality were also determined. Optimum tensile strength of ~185 MPa was achieved at rotational speed of 1120 rpm, traverse speed of 40 mm/min and tilt angle of 1.5°. There is an improvement of about 37% over the unreinforced joint with tensile strength of ~135 MPa. A model with a prediction accuracy of 92% was developed. The analysis of variance revealed that tool rotational speed, traverse speed and tilt angle had significant effects on the tensile strength of the weldments while the factors’ interactions do not show any significant contribution to the tensile strength. The model finds technical applications where timely selection of optimum process parameters is required for producing particulate-reinforced AA6061-T6 friction stir welded joints.

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Acknowledgements

The authors appreciate the technical assistance provided by Mr C.O. Abulola of the Department of Industrial and Production Engineering, Federal University of Technology Akure, Ondo State, Nigeria. Also, the statistical assistance rendered by Miss O.O. Kusoro of Industrial Mathematics Department, Federal University of Technology Akure is highly appreciated.

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Authors and Affiliations

  1. Landmark University SDG-9 (Industry, Innovation and Infrastructure Research Group), Omu-Aran, Kwara State, Nigeria

    B T Ogunsemi

  2. Mechanical Engineering Department, College of Engineering, Landmark University, P.M.B 1001, Omu-Aran, Kwara State, Nigeria

    B T Ogunsemi

  3. Mechanical Engineering Department, Federal University of Technology Akure, PMB 704, Akure, Ondo State, Nigeria

    B T Ogunsemi, O M Eta, E Olanipekun & T I Ogedengbe

  4. Industrial and Production Engineering Department, Federal University of Technology Akure, PMB 704, Akure, Ondo State, Nigeria

    T E Abioye

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  1. B T Ogunsemi
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Correspondence to T E Abioye.

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Ogunsemi, B.T., Eta, O.M., Olanipekun, E. et al. Tensile strength prediction by regression analysis for pulverized glass waste-reinforced aluminium alloy 6061-T6 friction stir weldments. Sādhanā 47, 53 (2022). https://doi.org/10.1007/s12046-022-01830-5

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  • DOI: https://doi.org/10.1007/s12046-022-01830-5

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