EXPERIMENTAL & NUMERICAL MICRO-EDM SURFACE TREATMENTS OF NEW GENERATION TITANIUM BIOMEDICAL ALLOYS
| dc.contributor.author | Ali, Shahid | |
| dc.date.accessioned | 2024-05-19T10:56:56Z | |
| dc.date.available | 2024-05-19T10:56:56Z | |
| dc.date.issued | 2024-04-23 | |
| dc.description.abstract | Despite advancements in material science, biomedicine, and orthopedic implant applications continue to encounter numerous challenges. The prevalence of implant failures in long-term applications serves as a significant motivation for ongoing research aimed at reducing the likelihood of such failures. Titanium and its new-generation alloys are widely employed in high-end applications due to their special properties, such as corrosion resistance, biocompatibility, Young’s modulus, and strength-to-weight ratio. This unique blend of qualities positions titanium alloys as highly sought-after materials in sectors, such as biomedical, aerospace, automotive, sports equipment, etc. Modern industries frequently use micro–Electro micro-electro-discharge machining (μ-EDM), a non-conventional machining technique that is especially useful for processing hard materials like titanium alloys. μ-EDM can produce intricate shapes with excellent dimensional precision. The main emphasis of this work is an experimental analysis of the μ-EDM of TNZT alloys employing a tungsten carbide (WC) electrode. The results for micro-EDM of Ti-35Nb-7Zr-5Ta showed that with the increase in process parameters capacitance (C) from 10nF to 100nF at Voltage (V) 80V, the machining performance parameters such as material removal rate (MRR), overcut (OC), crater size, surface microhardness, surface roughness, and crater size increase by 6.38, 1.523, 4.496 1.069, and 1.803 times respectively. However, the circularity of the µ-hole decreases by 1.099 times. This study intends to show how the two key machining factors (C & V) influence significant machining performance parameters. The influence of capacitance on machining performance variables is more dominant. Additionally, this study introduces an analytical model rooted in electro-thermal theory to estimate the size of micro-craters in micro-electrical discharge machining (μ-EDM). Addressing the challenge of the stochastic nature of the μ-EDM process, the model incorporates voltage and capacitance to predict crater size on the workpiece caused by individual discharges in micro-EDM. The simulation involved using COMSOL Multiphysics 5.6 software to simulate a single discharge in the μ-EDM process. This simulation accounted for the phase change material properties and the expansion of the plasma channel radius over time. The study further developed a numerical analysis to assess how input parameters (capacitance and voltage) influence the efficiency of the plasma channel in removing molten material. A regression model for plasma flushing efficiency (PFE%) based on experimental and numerical results was introduced to predict the actual crater size. The findings indicate contrasting effects of capacitance and voltage on plasma flushing efficiency. Specifically, an increase in capacitance contributes to an increase in plasma flushing efficiency. | en_US |
| dc.identifier.citation | Ali, Shahid (2024). Experimental & Numerical micro-EDM Surface Treatments of New Generation Titanium Biomedical Alloys. School of Engineering and Digital Sciences | en_US |
| dc.identifier.uri | http://nur.nu.edu.kz/handle/123456789/7676 | |
| dc.language.iso | en | en_US |
| dc.publisher | School of Engineering and Digital Sciences | en_US |
| dc.rights | Attribution-NoDerivs 3.0 United States | * |
| dc.rights.uri | http://creativecommons.org/licenses/by-nd/3.0/us/ | * |
| dc.subject | Type of access: Embargo | en_US |
| dc.subject | TNTZ alloy | en_US |
| dc.subject | micro EDM | en_US |
| dc.subject | COIMSOL | en_US |
| dc.subject | surface roughness | en_US |
| dc.subject | microhardness | en_US |
| dc.title | EXPERIMENTAL & NUMERICAL MICRO-EDM SURFACE TREATMENTS OF NEW GENERATION TITANIUM BIOMEDICAL ALLOYS | en_US |
| dc.type | Master's thesis | en_US |
| workflow.import.source | science |