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2 2024

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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.

Investigation of Electrochemical Discharge Machining for Tungsten Carbide: Effects of Electrolyte Composition on Material Removal Rate and Surface Quality

Najm Vian N., Abbas Tahseen F., Aghdeab Shukry H.

Management Systems in Production Engineering

2024

nr 4 (32)

563--570

Tungsten carbide (WC-Co) with a cobalt binder has been widely used in industrial application. Through their high wear resistance and hardness, which make it a challenge to machine. Electrochemical discharge machining (ECDM) is a newly developed hybrid technique used to machine conductive and nonconductive materials. Tungsten carbide machining is an area that needs more investigation. In this study, different types of electrolytes have been tested in the electrochemical machining of tungsten carbide. It has been concluded that tungsten carbide was successfully machined with electrolytes that were either neutral salts or a combination of neutral salts and hydroxides, the highest material removal rate achieved was (0.09250 g/min), and the average surface roughness achieved in this work was measured at (Ra 0.9275 µm). However, deposition took place on the surface of machined tungsten carbide when the samples were treated with sodium hydroxide and potassium hydroxide. EDX analysis of successfully machined tungsten carbide samples reveal the presence of carbon (C) due to diffusion from the base material and oxygen (O), most likely due to oxidation brought on by the high temperatures utilized. Scanning electron microscopy confirmed that the machined surfaces had craters, pores, restricted microcracks, and re-deposited melt particles, among other things.