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Optimizing the impact strength and hardness of the liquid crystal display printed parts using artificial neural network

Abdulrazaq Mustafa Mohammed, Najm Vian N., Ahmed Athraa Mohammed S.

Advances in Science and Technology. Research Journal

2025

Vol. 19, no. 8

373--383

Vat photopolymerization (VPP) is an effective additive manufacturing (AM) process known for its high dimensional accuracy and excellent surface finish. The combination of visible light with the use of LCD screens for 3D printing, allows for a faster, more efficient and economical manufacturing process. Despite these benefits, fabricating the end-use products still has some limitations related to the strength of the fabricated parts. For this purpose, the present paper provides a methodology to predict and optimize three critical process variables in AM, namely: layer height, build orientation, post-curing time. A neural-network model was developed for predicting the impact strength and hardness and optimizing the printing variables for highest responses. From the experiments using full-factorial design, it was revealed that improved parts strength and hardness are obtained at lower layer height, flat orientation, and moderate post-curing time. Based on the ANOVA analysis of, the most effective variable on the impact strength was post-curing time with (41.8%), while the orientation was higher contribution than the rest on the parts hardness with (47.5%). Comparisons between the experimental and the predicted values were illustrated. The mean error between experimental and neural network model was (1.13%) for impact strength and (0.82%) for hardness strength with correlation coefficient equal to 0.988 and 0.982 for the two responses respectively. The current proposed study demonstrates good agreement between the predicted model values and the experiments outcomes of impact strength and parts hardness.

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.