Investigation and optimization of machining parameters in Micro-WEDM of SMA to enhance performance

Q1 Engineering
Rakesh R. Kolhapure , Duradundi S. Badkar
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引用次数: 0

Abstract

Ti–Ni Shape Memory Alloys (SMAs) are extensively used in biomedical applications due to their superior biocompatibility and mechanical properties over traditional biomaterial SS316L and Ti alloys. However, achieving high precision and surface integrity during machining remains a significant challenge. This study focuses on optimizing the Micro-Wire Electric Discharge Machining (μ-WEDM) parameters to enhance the machining efficiency and surface quality of Ti–Ni SMAs. An L27 orthogonal array (OA) and Grey Relational Analysis (GRA) were applied to optimize multiple machining responses, including Material Removal Rate (MRR), Surface Roughness (SR), Dimensional Deviation (DD), and Kerf Width (KW) by using Voltage (V), Capacitance (C), and Wire feed (WF) as process parameters. Analysis of Variance (ANOVA) was conducted to evaluate the contribution of each parameter. The results indicate that ‘C’ significantly influences MRR (78.40 %), DD (50.98 %), and KW (36.64 %), while ‘V’ has the highest impact on SR (57.62 %). The optimal parameter combination (105 V, 6 nF, 1 mm/min) improved machining efficiency by 2.79 % (GRG) increased from 0.6898 to 0.7091, minimized surface defects, and enhanced dimensional accuracy. Scanning Electron Microscope (SEM) analysis confirmed that optimized μ-WEDM parameters minimized surface defects, refined textures, and reduced micro-cracks, enhancing surface integrity also minimizing recast layer results in dimensional accuracy. Energy Dispersive Spectroscopy (EDS) analysis verified minimal contamination, ensuring biocompatibility, making μ-WEDM ideal for high-precision biomedical applications. Furthermore, the study emphasizes the environmental sustainability of μ-WEDM, highlighting its reduced material waste and lower energy consumption compared to traditional machining methods. By integrating robust statistical analysis and process control, the study offers new insights into achieving good surface quality and performance in medical field.
SMA微细线切割加工参数的研究与优化,提高加工性能
Ti - ni形状记忆合金(sma)由于其优于传统生物材料SS316L和钛合金的生物相容性和机械性能而广泛应用于生物医学领域。然而,在加工过程中实现高精度和表面完整性仍然是一个重大挑战。为了提高Ti-Ni sma的加工效率和表面质量,对微线切割加工参数进行了优化研究。以电压(V)、电容(C)和进给丝(WF)为工艺参数,采用L27正交阵列(OA)和灰色关联分析(GRA)对材料去除率(MRR)、表面粗糙度(SR)、尺寸偏差(DD)和切口宽度(KW)等多个加工响应进行优化。进行方差分析(ANOVA)来评估每个参数的贡献。结果表明,“C”显著影响MRR(78.40%)、DD(50.98%)和KW(36.64%),而“V”对SR的影响最大(57.62%)。最佳参数组合(105 V, 6 nF, 1 mm/min)使加工效率提高2.79% (GRG),从0.6898提高到0.7091,表面缺陷最小化,尺寸精度提高。扫描电镜(SEM)分析证实,优化后的μ-WEDM参数最大限度地减少了表面缺陷,改善了织构,减少了微裂纹,提高了表面完整性,最大限度地减少了重铸层,提高了尺寸精度。能量色散光谱(EDS)分析验证了最小的污染,确保了生物相容性,使μ-WEDM成为高精度生物医学应用的理想选择。此外,该研究强调μ-电火花线切割的环境可持续性,强调与传统加工方法相比,μ-电火花线切割减少了材料浪费和降低了能耗。通过整合稳健的统计分析和过程控制,该研究为医疗领域实现良好的表面质量和性能提供了新的见解。
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来源期刊
International Journal of Lightweight Materials and Manufacture
International Journal of Lightweight Materials and Manufacture Engineering-Industrial and Manufacturing Engineering
CiteScore
9.90
自引率
0.00%
发文量
52
审稿时长
48 days
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