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<p>Titanium alloys are widely used in medical applications, particularly in dentistry and orthopedics, due to their biocompatibility, corrosion resistance, and low density. Electrical Discharge Machining (EDM) offers an effective method for producing microrough and porous surfaces, which are valued in the medical sector for supporting stable cell integration. However, EDM also generates surfaces with pronounced wear-relevant features, such as craters, microstructural changes, and common defects, including voids and cracks, which can significantly reduce wear performance. Research on the wear characteristics of EDM-processed surfaces and their relationship to surface integrity remains limited, highlighting the need for further investigation to optimize operational conditions for medical industry applications. In this study, medical-grade Ti6Al4V alloy was processed using EDM in deionized water with controlled pulse-on durations and currents. The resulting surfaces were evaluated under dry sliding conditions using a pin-on-disk tribometer to compare process parameters and wear responses. Comprehensive analyses of both worn and unworn surfaces, as well as subsurface features, were conducted using a surface profilometer, Scanning Electron Microscopy with Energy-Dispersive Spectroscopy (SEM/EDS), X-Ray Diffraction (XRD) and micro-hardness testing. To enhance the analytical framework and emphasize a principal contribution of this research, multiple Machine Learning (ML) techniques were employed to model and predict wear rates and layer thicknesses based on experimental parameters and data. Among the evaluated methods, CatBoost and KNN achieved the highest coefficients of determination for predicting the wear characteristics of the Ti6Al4V alloy, demonstrating the potential of ML to provide robust and generalizable analyses in biomedical surface engineering.</p>

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Electrical discharge machining of Ti6Al4V in deionized water: surface integrity and wear response for medical applications

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年5月25日

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  • Hamidullah Yaşar,
  • Nihal Ekmekci

摘要

Titanium alloys are widely used in medical applications, particularly in dentistry and orthopedics, due to their biocompatibility, corrosion resistance, and low density. Electrical Discharge Machining (EDM) offers an effective method for producing microrough and porous surfaces, which are valued in the medical sector for supporting stable cell integration. However, EDM also generates surfaces with pronounced wear-relevant features, such as craters, microstructural changes, and common defects, including voids and cracks, which can significantly reduce wear performance. Research on the wear characteristics of EDM-processed surfaces and their relationship to surface integrity remains limited, highlighting the need for further investigation to optimize operational conditions for medical industry applications. In this study, medical-grade Ti6Al4V alloy was processed using EDM in deionized water with controlled pulse-on durations and currents. The resulting surfaces were evaluated under dry sliding conditions using a pin-on-disk tribometer to compare process parameters and wear responses. Comprehensive analyses of both worn and unworn surfaces, as well as subsurface features, were conducted using a surface profilometer, Scanning Electron Microscopy with Energy-Dispersive Spectroscopy (SEM/EDS), X-Ray Diffraction (XRD) and micro-hardness testing. To enhance the analytical framework and emphasize a principal contribution of this research, multiple Machine Learning (ML) techniques were employed to model and predict wear rates and layer thicknesses based on experimental parameters and data. Among the evaluated methods, CatBoost and KNN achieved the highest coefficients of determination for predicting the wear characteristics of the Ti6Al4V alloy, demonstrating the potential of ML to provide robust and generalizable analyses in biomedical surface engineering.