Chaudhari Rakesh, Prajapati Parth, Khanna Sakshum, Vora Jay, Patel Vivek K, Pimenov Danil Yurievich, Giasin Khaled
Department of Mechanical Engineering, School of Technology, Pandit Deendayal Energy University, Raisan, Gandhinagar 382007, India.
Department of Solar Engineering, School of Technology, Pandit Deendayal Energy University, Raisan, Gandhinagar 382007, India.
Materials (Basel). 2022 Mar 9;15(6):2018. doi: 10.3390/ma15062018.
Shape memory alloy (SMA), particularly those having a nickel-titanium combination, can memorize and regain original shape after heating. The superior properties of these alloys, such as better corrosion resistance, inherent shape memory effect, better wear resistance, and adequate superelasticity, as well as biocompatibility, make them a preferable alloy to be used in automotive, aerospace, actuators, robotics, medical, and many other engineering fields. Precise machining of such materials requires inputs of intellectual machining approaches, such as wire electrical discharge machining (WEDM). Machining capabilities of the process can further be enhanced by the addition of AlO nanopowder in the dielectric fluid. Selected input machining process parameters include the following: pulse-on time (T), pulse-off time (T), and AlO nanopowder concentration. Surface roughness (SR), material removal rate (MRR), and recast layer thickness (RLT) were identified as the response variables. In this study, Taguchi's three levels L approach was used to conduct experimental trials. The analysis of variance (ANOVA) technique was implemented to reaffirm the significance and adequacy of the regression model. AlO nanopowder was found to have the highest contributing effect of 76.13% contribution, T was found to be the highest contributing factor for SR and RLT having 91.88% and 88.3% contribution, respectively. Single-objective optimization analysis generated the lowest MRR value of 0.3228 g/min (at T of 90 µs, T of 5 µs, and powder concentration of 2 g/L), the lowest SR value of 3.13 µm, and the lowest RLT value of 10.24 (both responses at T of 30 µs, T of 25 µs, and powder concentration of 2 g/L). A specific multi-objective Teaching-Learning-Based Optimization (TLBO) algorithm was implemented to generate optimal points which highlight the non-dominant feasible solutions. The least error between predicted and actual values suggests the effectiveness of both the regression model and the TLBO algorithms. Confirmatory trials have shown an extremely close relation which shows the suitability of both the regression model and the TLBO algorithm for the machining of the nanopowder-mixed WEDM process for Nitinol SMA. A considerable reduction in surface defects owing to the addition of AlO powder was observed in surface morphology analysis.
形状记忆合金（SMA），尤其是那些具有镍钛组合的合金，在加热后能够记忆并恢复其原始形状。这些合金具有优异的性能，如更好的耐腐蚀性、固有的形状记忆效应、更好的耐磨性、足够的超弹性以及生物相容性，这使得它们成为汽车、航空航天、 actuator、机器人技术、医疗和许多其他工程领域中首选的合金。对这类材料进行精密加工需要采用智能加工方法，如电火花线切割加工（WEDM）。通过在工作液中添加AlO纳米粉末，可以进一步提高该加工工艺的加工能力。选定的输入加工工艺参数包括：脉冲导通时间（T）、脉冲关断时间（T）和AlO纳米粉末浓度。表面粗糙度（SR）、材料去除率（MRR）和重铸层厚度（RLT）被确定为响应变量。在本研究中，采用田口的三水平L方法进行实验试验。运用方差分析（ANOVA）技术来再次确认回归模型的显著性和充分性。发现AlO纳米粉末的贡献最大，贡献率为76.13%，T被发现是对SR和RLT贡献最大的因素，分别为91.88%和88.3%。单目标优化分析得出最低MRR值为0.3228 g/min（在T为90 µs、T为5 µs和粉末浓度为2 g/L时），最低SR值为3.13 µm，最低RLT值为10.24（在T为30 µs、T为25 µs和粉末浓度为2 g/L时的两个响应）。实施了一种特定的基于教学学习的多目标优化（TLBO）算法来生成突出非主导可行解的最优点。预测值与实际值之间的最小误差表明回归模型和TLBO算法都是有效的。验证性试验显示出极其密切的关系，这表明回归模型和TLBO算法都适用于镍钛形状记忆合金的纳米粉末混合电火花线切割加工工艺。在表面形貌分析中观察到，由于添加了AlO粉末，表面缺陷显著减少。
