Farooq Muhammad Umar, Anwar Saqib, Bhatti Haider Ali, Kumar M Saravana, Ali Muhammad Asad, Ammarullah Muhammad Imam
School of Mechanical Engineering, University of Leeds, Leeds LS2 9JT, UK.
Industrial Engineering Department, College of Engineering, King Saud University, P.O. Box 800, Riyadh 11421, Saudi Arabia.
Materials (Basel). 2023 Jun 19;16(12):4458. doi: 10.3390/ma16124458.
The superior engineering properties and excellent biocompatibility of titanium alloy (Ti6Al4V) stimulate applications in biomedical industries. Electric discharge machining, a widely used process in advanced applications, is an attractive option that simultaneously offers machining and surface modification. In this study, a comprehensive list of roughening levels of process variables such as pulse current, pulse ON time, pulse OFF time, and polarity, along with four tool electrodes of graphite, copper, brass, and aluminum are evaluated (against two experimentation phases) using a SiC powder-mixed dielectric. The process is modeled using the adaptive neural fuzzy inference system (ANFIS) to produce surfaces with relatively low roughness. A thorough parametric, microscopical, and tribological analysis campaign is established to explore the physical science of the process. For the case of the surface generated through aluminum, a minimum friction force of ~25 N is observed compared with the other surfaces. The analysis of variance shows that the electrode material (32.65%) is found to be significant for the material removal rate, and the pulse ON time (32.15%) is found to be significant for arithmetic roughness. The increase in pulse current to 14 A shows that the roughness increased to ~4.6 µm with a 33% rise using the aluminum electrode. The increase in pulse ON time from 50 µs to 125 µs using the graphite tool resulted in a rise in roughness from ~4.5 µm to ~5.3 µm, showing a 17% rise.
钛合金（Ti6Al4V）优异的工程性能和出色的生物相容性推动了其在生物医学行业的应用。电火花加工是先进应用中广泛使用的工艺，是一种有吸引力的选择，它同时提供加工和表面改性。在本研究中，使用碳化硅粉末混合电介质，对诸如脉冲电流、脉冲导通时间、脉冲关断时间和极性等工艺变量的一系列粗化水平，以及石墨、铜、黄铜和铝四种工具电极（针对两个实验阶段）进行了评估。该工艺使用自适应神经模糊推理系统（ANFIS）进行建模，以生产粗糙度相对较低的表面。建立了全面的参数、微观和摩擦学分析活动，以探索该工艺的物理原理。对于通过铝产生的表面，与其他表面相比，观察到的最小摩擦力约为25N。方差分析表明，电极材料（32.65%）对材料去除率有显著影响，脉冲导通时间（32.15%）对算术粗糙度有显著影响。将脉冲电流增加到14A表明，使用铝电极时粗糙度增加到约4.6µm，增幅为33%。使用石墨工具将脉冲导通时间从50µs增加到125µs，导致粗糙度从约4.5µm增加到约5.3µm，增幅为17%。
