Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

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

Version 1 : Received: 17 December 2020 / Approved: 21 December 2020 / Online: 21 December 2020 (10:10:14 CET)

How to cite: Moradi, M.; Meiabadi, M.S.; Karami Moghadam, M.; Ardabili, S.; Band, S.S.; Mosavi, A. Enhancing 3D Printing Producibility in Polylactic Acid Using Fused Deposition Modelling and Machine Learning. Preprints 2020, 2020120487 (doi: 10.20944/preprints202012.0487.v1). Moradi, M.; Meiabadi, M.S.; Karami Moghadam, M.; Ardabili, S.; Band, S.S.; Mosavi, A. Enhancing 3D Printing Producibility in Polylactic Acid Using Fused Deposition Modelling and Machine Learning. Preprints 2020, 2020120487 (doi: 10.20944/preprints202012.0487.v1).

Abstract

Abstract: Polylactic acid (PLA) is brittle in nature with extensive deformation property. For improvement of the end-use quality, it is of significant importance to enhance the producibility of 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 FDM-printed tensile test samples with the desired part thickness. 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 layer thickness, infill percentage, and extruder temperature 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 honeycomb internal fill pattern is applied for the production of light-weight 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 ANN and ANN-GA techniques for developing an accurate estimation for toughness, part thickness, and production cost as dependant variables. Results were evaluated by correlation coefficient and RMSE values. According to the modeling results, ANN-GA as a hybrid ML technique could successfully improve 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. In the other side, the optimization results confirm that the optimized specimen is cost-effective and able to comparatively undergo deformation, which develops the usability of printed PLA objects. The research is accomplished under the constraints of PLA compatibility with existing fused deposition modeling setup without changing the functional hardware/software 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.

Subject Areas

Fused deposition modeling; toughness; part thickness; machine learning; artificial neural network; response surface method

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