Optimising the mechanical properties of additive-manufactured recycled polylactic acid (rPLA) using single and multi-response analyses methods

Abstract

Taguchi’s design of experiment (DoE) and the grey relational analysis are used to optimise fused filament fabrication (FFF) parameters for the tensile strength and modulus of toughness (MoT) responses of a recycled polylactic acid (Reform-rPLA). The paper investigates the influences of the infill geometry, infill density, infill orientation, nozzle temperature and infill speed on the mechanical properties using the L18\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${L}_{18}$$\\end{document} orthogonal array that is based on the 21×43\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${2}^{1}\ imes {4}^{3}$$\\end{document} factor levels and 3 experimental repetitions. The output responses are first studied individually and combined as a multi-response optimisation using the grey relational analysis method. In the strength optimisation, the infill orientation and infill density are statistically significant with P-values α\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\alpha$$\\end{document} less than the 0.05 criterion. Similarly, the analysis of variance (ANOVA) for the MoT showed that infill orientation and infill geometry are statistically significant. For the multi-response optimisation, only the infill orientation is statistically significant. The mean response analyses identified factor levels that led to optimum strength and MoT responses. The confirmation tests are in good agreement with the response predictions. Using the first three influential factors, multiple variable linear regression models were developed. The predictive models showed average errors of 7.91%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$7.91\\%$$\\end{document} for the tensile strength and 8.6%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$8.6\\%$$\\end{document} for the MoT.