Optimising the Mechanical Properties of Additive-Manufactured Recycled Polylactic Acid (rPLA) using Single and Multi-Response Analyses Methods.

Abstract

Abstract Taguchi’s design of experiment (DoE) and the grey relational analysis are used to optimise fused deposition modelling (FDM) parameters for the tensile strength and Modulus of toughness (MoT) responses of a recycled Polylactic acid (Reform-rPLA) polymer. The influences of the infill geometry, infill density, infill orientation, nozzle temperature and infill speed on the mechanical properties of the material are studied using the \({L}_{18}\) orthogonal array which is based the \({2}^{1}\times {4}^{3}\) factor levels with 3 experimental repetitions. The output responses are first studied individually and combined as a multi-response optimisation using the grey relational analysis method. The analysis of variance (ANOVA) showed that the infill orientation parameter highly influences both the single and multi-response optimisations. In the strength optimisation, the infill orientation and infill density are statistically significant with P-values \(\alpha\) less than the 0.05 criterion. Similarly, the ANOVA for the (MoT) showed that infill orientation and infill geometry parameters are statistically significant. The infill orientation is statistically significant for the multi-response optimisation, followed by the infill density with \({\alpha }=0.08\). On the other hand, the response predictions indicated that the zigzag infill geometry, \(45\%\) infill density, \(90^\circ\) infill geometry and \(205℃\) nozzle temperature led to optimum tensile strength and MoT properties. Our confirmation tests are in good agreement with optimum response predictions. Based on rankings of the mean responses, the first three factors were used to develop linear regression models for the tensile strength and MoT of the material. The predictive models showed average errors of \(7.91\%\) for the tensile strength and \(8.6\%\) for the MoT.