Chaudhari Rakesh, Vora Jay J, Mani Prabu S S, Palani I A, Patel Vivek K, Parikh D M, de Lacalle Luis Norberto López
Department of Mechanical engineering, School of Technology, Pandit Deendayal Petroleum University, Raisan, Gandhinagar 382007, India.
Metallurgy Engineering and Materials Science, Indian Institute of Technology Indore, Indore 453552, India.
Materials (Basel). 2019 Apr 18;12(8):1277. doi: 10.3390/ma12081277.
Nitinol, a shape-memory alloy (SMA), is gaining popularity for use in various applications. Machining of these SMAs poses a challenge during conventional machining. Henceforth, in the current study, the wire-electric discharge process has been attempted to machine nickel-titanium (Ni55.8Ti) super-elastic SMA. Furthermore, to render the process viable for industry, a systematic approach comprising response surface methodology (RSM) and a heat-transfer search (HTS) algorithm has been strategized for optimization of process parameters. Pulse-on time, pulse-off time and current were considered as input process parameters, whereas material removal rate (MRR), surface roughness, and micro-hardness were considered as output responses. Residual plots were generated to check the robustness of analysis of variance (ANOVA) results and generated mathematical models. A multi-objective HTS algorithm was executed for generating 2-D and 3-D Pareto optimal points indicating the non-dominant feasible solutions. The proposed combined approach proved to be highly effective in predicting and optimizing the wire electrical discharge machining (WEDM) process parameters. Validation trials were carried out and the error between measured and predicted values was negligible. To ensure the existence of a shape-memory effect even after machining, a differential scanning calorimetry (DSC) test was carried out. The optimized parameters were found to machine the alloy appropriately with the intact shape memory effect.
镍钛诺，一种形状记忆合金（SMA），在各种应用中越来越受欢迎。在传统加工过程中，这些形状记忆合金的加工存在挑战。因此，在当前的研究中，尝试采用电火花线切割加工镍钛（Ni55.8Ti）超弹性形状记忆合金。此外，为了使该工艺在工业上可行，已制定了一种系统方法，该方法包括响应面方法（RSM）和传热搜索（HTS）算法，用于优化工艺参数。脉冲导通时间、脉冲关断时间和电流被视为输入工艺参数，而材料去除率（MRR）、表面粗糙度和显微硬度被视为输出响应。生成残差图以检查方差分析（ANOVA）结果和生成的数学模型的稳健性。执行多目标HTS算法以生成二维和三维帕累托最优点，这些点表示非主导可行解。所提出的组合方法在预测和优化电火花线切割加工（WEDM）工艺参数方面被证明是非常有效的。进行了验证试验，测量值与预测值之间的误差可以忽略不计。为了确保即使在加工后仍存在形状记忆效应，进行了差示扫描量热法（DSC）测试。发现优化后的参数能够在保留形状记忆效应的情况下对合金进行适当加工。
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