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基于电火花加工的铁基形状记忆合金分析，采用具有多输出优化和微观结构验证的铜钨电极。

EDM-based analysis of Fe-based shape memory alloys using Cu-W electrodes with multiple output optimization and microstructural validation.

作者信息

Singh Ranjit, Satpathy Sambit, Shukla Dhirendra Kumar, Singh Ravi Pratap, Trehan Rajeev, Panda Jibitesh Kumar, Bhattacharjee Biplab

机构信息

Department of Industrial and Production Engineering, NIT Jalandhar, Jalandhar, 144027, Panjab, India.

CSE Department, Galgotias College of Engineering and Technology, Greater Noida, Uttar Pradesh, 201306, New Delhi, India.

出版信息

Sci Rep. 2025 Aug 2;15(1):28287. doi: 10.1038/s41598-025-14013-z.

DOI:10.1038/s41598-025-14013-z
PMID:40753344
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12318125/
Abstract

Shape Memory Alloys (SMAs) are pivotal in diverse industrial applications due to their exceptional properties, including actuation, biocompatibility, and adaptability in aerospace, biomedical, and military domains. However, their complex machinability often leads to high costs and suboptimal surface quality when processed using traditional methods. Using Response Surface Methodology (RSM) with a Central Composite Design (CCD), this study evaluated the effects of input parameters, including pulse on time (T), pulse off time (T), peak current (Ip), and gap voltage (GV), on material wear responses during Electrical Discharge Machining (EDM). Fe-based Shape Memory Alloys (SMAs) were machined using a Cu-tungsten electrode to investigate the wear characteristics of both workpieces and tool electrodes. Results revealed that Workpiece Material Removal Rate (WOW) ranged from 11.30 to 65.17 mm³/min, and Tool Wear Rate (WOTE) varied from 0.0062 to 0.01127 g/min. Scanning Electron Microscopy (SEM) of machined surfaces showcased craters, micro-cracks, and recast layers, elucidating the correlation between process parameters and surface integrity. Multi-objective optimization using the desirability approach identified optimal conditions for balancing machining efficiency and surface quality. This research provides a comprehensive understanding of the EDM process for Fe-based SMAs, paving the way for improved machinability and expanded industrial applications.

摘要

形状记忆合金（SMA）因其卓越的性能，在包括航空航天、生物医学和军事领域的驱动、生物相容性及适应性等多种工业应用中起着关键作用。然而，当使用传统方法加工时，其复杂的可加工性常常导致高成本和不理想的表面质量。本研究采用响应面法（RSM）和中心复合设计（CCD），评估了脉冲导通时间（T）、脉冲关断时间（T）、峰值电流（Ip）和间隙电压（GV）等输入参数对放电加工（EDM）过程中材料磨损响应的影响。使用铜钨电极对铁基形状记忆合金（SMA）进行加工，以研究工件和工具电极的磨损特性。结果表明，工件材料去除率（WOW）在11.30至65.17立方毫米/分钟之间，工具磨损率（WOTE）在0.0062至0.01127克/分钟之间变化。加工表面的扫描电子显微镜（SEM）显示出凹坑、微裂纹和重铸层，阐明了工艺参数与表面完整性之间的相关性。采用期望度方法进行多目标优化，确定了平衡加工效率和表面质量的最佳条件。本研究全面了解了铁基形状记忆合金的电火花加工过程，为提高可加工性和扩大工业应用铺平了道路。

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