{"title":"Investigation and optimization of machining parameters in Micro-WEDM of SMA to enhance performance","authors":"Rakesh R. Kolhapure , Duradundi S. Badkar","doi":"10.1016/j.ijlmm.2025.03.002","DOIUrl":null,"url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":52306,"journal":{"name":"International Journal of Lightweight Materials and Manufacture","volume":"8 4","pages":"Pages 537-550"},"PeriodicalIF":0.0000,"publicationDate":"2025-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Lightweight Materials and Manufacture","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2588840425000253","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"Engineering","Score":null,"Total":0}
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.