Impact Strength Investigation of Carbon-Black-Reinforced Polylactic Acid Composite in Extrusion-Based Additive Manufacturing: Experimental, Statistical, and Machine Learning Analysis

This research looks into the effects of nozzle temperature (NT), bed temperature (BT), and fan speed (FS) on the impact strength (IS) of carbon black-reinforced conductive polylactic acid (CPLA) composites manufactured by the fused filament fabrication (FFF) process. We experimented with thirteen combinations of these parameters using the Box-Behnken design, which is part of response surface methodology (RSM). The impact specimens prepared under ASTM D6110 directives were then tested. Through analysis of variance (ANOVA), two factors (NT and BT) were found to be of utmost significance in determining IS, while the FS played an intermediate role. A maximum IS of 21 kJ/m² was predicted by the optimization using a regression model at NT = 216 °C, BT = 46 °C, and FS = 100%. The consistency between the experimental results and the predicted results highlights the model's strength. Additionally, machine learning models like Gaussian process regression (GPR) and linear regression were employed to determine the predictability, with GPR having the highest accuracy. Such results not only provide theoretical understanding but also point towards practical applications in optimizing the thermal process parameters to get the desired IS performance in conductive PLA composites, especially where the durability under impact loading is required.