Combining Digital Twin and Machine Learning for the Fused Filament Fabrication Process

In this work, the feasibility of applying a digital twin combined with machine learning algorithms (convolutional neural network and random forest classifier) to predict the performance of PLA (polylactic acid or polylactide) parts is being investigated. These parts are printed using a low-cost desktop 3D printer based on the principle of fused filament fabrication. A digital twin of the extruder assembly has been created in this work. This is the component responsible for melting the thermoplastic material and depositing it on the print bed. The extruder assembly digital twin has been separated into three simulations, i.e., conjugate convective heat transfer, multiphase material melting, and non-Newtonian microchannel. The functionality of the physical extruder is controlled by a PID/PWM circuit, which has also been modelled within the digital twin to control the virtual extruder’s operation. The digital twin simulations were validated through experimentation and showed a good agreement. After validation, a variety of parts were printed using PLA at four different extrusion temperatures (180 °C, 190 °C, 200 °C, 210 °C) and ten different extrusion rates (ranging from 70% to 160%). Measurements of the surface roughness, hardness, and tensile strength of the printed parts were recorded. To predict the performance of the printed parts using the digital twin, a correlation was established between the temperature profile of the non-Newtonian microchannel simulation and the experimental results using the machine learning algorithms. To achieve this objective, a reduced order model (ROM) of the extruder assembly digital twin was developed to generate a training database. The database generated by the ROM (simulation results) was used as the input for the machine learning algorithms and experimental data were used as target values (classified into three categories) to establish the correlation between the digital twin output and performance of the physically printed parts. The results show that the random forest classifier has a higher accuracy compared to the convolutional neural network in categorising the printed parts based on the numerical simulations and experimental data.