Enhancing 3D Printing Producibility in Polylactic Acid Using Fused Filament Fabrication Fused Deposition Modelling and Machine Learning

Abstract

Polylactic Polylactic acid (PLA) is one of the high applicable material which is used in the 3D printers due to some significant features like its deformation property and affordable costacid (PLA) is brittle in nature with extensive deformation property. For improvement of the end-use quality, it is of significant importance to enhance the quality of Fused Filament Fabrication (FFF)fused deposition modeling (FDM)-printed objects in PLA. The purpose of this investigation is to boost toughness and to reduce the production cost of the FDMFFF-printed tensile test samples with the desired part thickness. Due to prevent from many numerous and idle printing samples the response Surface Method (RSM) is used.To attain the research purpose number of experiments are designed and analyzed by the Response Surface Method (RSM). The statistical analysis is performed to deal with this concern considering extruder temperature (ET), infill percentage (IP), and layer thickness (LT) as controlled factors. The tensile test specimens are printed based on the designed experiments, and the tensile strength tests are conducted by SANTAM 150 universal testing machine based on ASTM D638. The pattern for filling is designed based on honeycomb which is applied to produce lightweight and high-strength specimens. The area under Force- Extension curve up to fracture is acquired as the toughness of the printed specimens. This study also developed a modeling process using artificial neural network (ANN) and artificial neural network- genetic algorithm (ANN-GA) techniques to develop an accurate estimation for 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 could successfully improveenhances the accuracy of modeling 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 side, 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. The research is accomplished under the constraints of PLA compatibility with existing Fused Filament Fabrication fused deposition modeling installation, in the absence of the functional assistant of the machine in the absence of the functional assistant of the machine. Although the mechanical properties and dimensional accuracy of PLA have already been studied, there is little literature on the toughness of the printed PLA with honeycomb internal fill pattern.

Improvement of tensile strength of fused deposition modelling (FDM) part using artificial neural network and genetic algorithm techniques

“…These issues can be overtaken if the I-optimality design approach combined with the machine learning ANN technique for optimisation of the conflicting process parameters as well as to address the shortcomings of the commonly used DOE schemes. Machine learning models are successfully applied for optimisation of the FDM process (Mahmood et al ., 2021; Giri et al ., 2021; Moradi et al ., 2020).…”

“…Artificial neural network (ANN) is the most widely used technique in the AM field, owing to its ability to handle large datasets and superior computational capability (Mahmood et al ., 2021). Literature also witnessed development of predictive models by ANN approach for accurate estimation of part toughness, part thickness and production cost (Moradi et al ., 2020; Mohamad et al ., 2017). Further, Giri et al .…”

“…Artificial neural network (ANN) is the most widely used technique in the AM field, owing to its ability to handle large datasets and superior computational capability (Mahmood et al, 2021). Literature also witnessed development of predictive models by ANN approach for accurate estimation of part toughness, part thickness and production cost (Moradi et al, 2020;Mohamad et al, 2017). Further, Giri et al (2021) performed optimisation of FDM process parameters for a dual extruder 3D printer by means of ANN modelling and proved that the task of training the large datasets and optimising them can be effectively accomplished by using the function approximation of ANN.…”