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Optimization of process parameters for compressive strength-to-weight ratio of additively manufactured polyethylene terephthalate glycol

Obaeed Nareen Hafidh, Abdulridha HInd H., Shwaish Raed R., Bedan Aqeel S.

Advances in Science and Technology. Research Journal

2025

Vol. 19, no. 6

301--315

Additive manufacturing (AM), particularly the Fused Deposition Modeling (FDM) has become a cornerstone manufacturing technology in the nascent field of 3D printing. The mechanical properties and effective use of material in 3D printed parts are essential for enhancing the potential of AM in industrial and functional applications. This paper explores how core FDM printing process parameters: print temperature, extrusion width, and printing speed affect the compressive strength-to-weight ratio of Polyethylene Terephthalate Glycol PETG parts produced via FDM. Based on the Box-Behnken design of Response Surface Methodology (RSM) the influence of these conditions concerning the mechanics and material properties were studied. The results show that a printing temperature of 250 °C provides improved compressive strength as well as decreased weight through strong bonding between layers. Small, extruded widths (0.5 mm) have been found to offer the ideal strength-to-weight ratio while large extruded widths (0.6 mm) greatly enhanced strength by adding weight. A slower printing speed of 30mm/s promoted greater compressive strength but yielded more dense parts. In the multi-objective desirability optimization, optimal parameters were found in which the printing temperature was 250°C, the extruded width was 0.5879mm and the printing speed was 30mm/s. The results of this study are beneficial for realizing lightweight yet mechanically abundant 3D printing parts while enhancing the field of AM in different industries.

Predicting mechanical strength and optimized parameters in FDM-printed polylactic acid parts via artificial neural networks and desirability analysis

Abdulridha HInd H., Abbas Tahseen F., Bedan Aqeel S.

Management Systems in Production Engineering

2024

nr 3 (32)

428--437

Fused deposition modeling (FDM) is a commonly used additive manufacturing (AM) technique in both domestic and industrial end-product fabrications. It produces prototypes and parts with complex geometric designs, which has the major benefits of eliminating the need for expensive tooling and flexibility. However, the produced parts often face poor part strength due to anisotropic fabrication strategies. The printing procedure, the kind of material utilized, and the printing parameters all have a significant impact on the mechanical characteristics of the printed item. In order to predict the mechanical properties related to printed components made with the use of FDM and Polylactic Acid (PLA) material, this study concentrates on developing a prediction model utilizing Artificial Neural Networks (ANNs). This study used the Taguchi design of experiments technique, utilizing (L25) orthogonal array as well as a Neural Network (NN) method with two layers and 15 neurons. The effect of FDM parameters (layer thickness (mm), percentage of infill density, number of top/bottom layers, shell thickness (mm), and infill overlap percentage) on ultimate tensile and compressive strength (UTS and UCS) was examined through analysis of variance (ANOVA). With an ANOVA result of 67.183% and 40.198%, respectively, infill density percentage was found to be the most significant factor influencing UCS and UTS dependent on other parameters. The predicted results demonstrated valuable agreement with experimental values, with mean squared errors of (0.098) and (0.326) for UTS and UCS, respectively. The predictive model produces flexibility in selecting the optimal setting based on applications.