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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.

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.

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Fused deposition modeling (FDM) technology is one of the rapidly growing techniques used for producing various complicated configurations without the need for any tools or continuous human intervention. However, a low quality of surfaces results for the layered production used in FDM. It is essential to investigate a suitable method for enhancing the accuracy and quality associated with FDM parts. This study aims to investigate the impact of different parameters such as the percentage of infill density, the shell thickness, layer thickness, and the number of top/bottom layers, as well as the percentage of infill overlap on part quality and the improvement of surface finish for printed specimens achieved through post-processing. Polylactic acid (PLA) material is used in building test specimens through the FDM approach. The experiments are carried out based on the Taguchi design of experiment method using (L25) orthogonal array. Using an analysis-of-variance approach (ANOVA), it is possible to understand the significance of the FDM parameters in order to find optimal parameter combinations. The results indicate that the application of the vapour smoothing procedure (VSP) treatment enhances the surface quality of FDM components to a microstage with minimal dimensional variation. The dichloromethane chemical has been found to exhibit excellent surface finish at an infill density of 50%, a layer thickness of 0.1 mm, a shell thickness of 2.8 mm, five top/bottom layer numbers, and 0.25 infill overlap. [ABSTRACT FROM AUTHOR]

Fused deposition modeling (FDM) is a commonly used additive manufacturing (AM) technique that creates prototypes and parts with intricate geometrical designs. It is gaining popularity since it enhances products by removing the need for expensive equipment. The printed item's mechanical properties are affected by the type of materials used, the printing process, and the printing parameters. The 3-D model of the polylactic acid (PLA) filament generated specimens was created using the Fused Deposition Modeling procedure and developed using Solid Works. This study investigates the effect of printing parameters on the mechanical and physical properties of samples printed using a Fused Deposition Modeling machine (Creality Ender-5 Pro). Six parameters are used: infill pattern, density, overlap percentage, layer thickness, shell thickness, and top/bottom layer number. Five levels were chosen for each FDM parameter. The results illustrated how printing parameters affected the mechanical and physical properties of samples, which were proven by ultimate tensile stress, surface roughness, and percentage of tensile average deviation. A comparison between the predicted results and the measured results was presented, and the maximum percentage error of the model, which fit the data well, was 0.54%, 0.3%, and 1.36% for ultimate tensile strength (UTS), surface roughness (Ra), and Tensile average deviation percentage respectively. [ABSTRACT FROM AUTHOR]