Deep Neural Network Predicts Ti‐6Al‐4V Dissolution State Using Near‐Field Impedance Spectra

Abstract

AbstractRetrieval studies document Ti‐6Al‐4V selective dissolution within crevices of total hip replacement devices. A gap persists in the fundamental understanding of Ti‐6Al‐4V crevice corrosion in vivo and its impact on local impedance. Previous studies use nearfield electrochemical impedance spectroscopy (nEIS) for characterization of retrieved CoCrMo surfaces and phase angle symmetry‐based EIS (sbEIS) for rapid data acquisition. In this study, these methods are combined with a deep neural network to characterize the local impedance changes after selective dissolution. It is hypothesized that structural changes occurring during dissolution will manifest as property changes to the oxide film capacitance. First, after sustained cathodic activation, the Ti‐6Al‐4V β phase selectively dissolves from the surface. Next, nEIS acquires n = 100 control and n = 105 dissolved spectra. Over dissolved regions, oxide capacitance significantly increases (Log10Q = ‐4.17 versus ‐4.78 (Scm−2(s)α), p = 0.000). Using single frequency EIS (5000 Hz), a capacitance‐based scanning impedance microscopy method identifies dissolved regions within seconds. Finally, Bode phase plots of the 205 control and dissolved nEIS spectra are input into a deep neural network. After training with n = 180 spectra, the model predicts the surface state for n = 25 previously unseen nEIS spectra with 96% accuracy.