Modeling the Producibility of 3D Printing in Polylactic Acid Using Artificial Neural Networks and Fused Filament Fabrication

Abstract

Polylactic acid (PLA) is a highly applicable material that is used in 3D printers due to some significant features such as its deformation property and affordable cost. For improvement of the end-use quality, it is of significant importance to enhance the quality of fused filament fabrication (FFF)-printed objects in PLA. The purpose of this investigation was to boost toughness and to reduce the production cost of the FFF-printed tensile test samples with the desired part thickness. To remove the need for numerous and idle printing samples, the response surface method (RSM) was used. Statistical analysis was performed to deal with this concern by considering extruder temperature (ET), infill percentage (IP), and layer thickness (LT) as controlled factors. The artificial intelligence method of artificial neural network (ANN) and ANN-genetic algorithm (ANN-GA) were further developed to estimate the toughness, part thickness, and production-cost-dependent variables. Results were evaluated by correlation coefficient and RMSE values. According to the modeling results, ANN-GA as a hybrid machine learning (ML) technique could enhance the accuracy of modeling by about 7.5, 11.5, and 4.5% for toughness, part thickness, and production cost, respectively, in comparison with those for the single ANN method. On the other hand, the optimization results confirm that the optimized specimen is cost-effective and able to comparatively undergo deformation, which enables the usability of printed PLA objects.

Forecasting Compressive Strength of RHA Based Concrete Using Multi-Expression Programming

“…Historically, AIA were mostly viewed as black boxes that served as a significant barrier between research and practice [ 73 , 74 , 75 ]. Because of AIA’s lack of explainability and credibility, practitioners avoid it [ 75 ].…”

Three-Dimensional Modeling and 3D Printing of Biocompatible Orthodontic Power-Arm Design with Clinical Application

“…For example, the modeling of possible combined axial-torsional non-proportional loadings that were used to simulate shapememory alloys (SMAs) [40]. Though in our use case, where we are well aware of the force from the elastic band to the power-arm and also with the well-defined bonding area, more sophisticated methods are not clinically indispensable [41]. Moreover, topology optimization analysis in combination with shape and sizing optimization can be employed.…”

The Design of 4D‐Printed Hygromorphs: State‐of‐the‐Art and Future Challenges

“…optimization of local properties can be setup before the production of the hygromorph. For instance, Meiabadi et al [171] have used an artificial neural network in combination with a genetic algorithm to control the fracture toughness of the PLA with infill percentage, layer thickness and extruder temperature as inputs. The printing parameters presented in this paragraph can be evaluated for their direct impact of actuation.…”