In this paper, we report on a study of the surface effect on nanoindentation and introduce the apparent surface stress that represents the energy dissipated per unit area of a solid surface in a nanoindentation test. The work done by an applied indentation load contains both bulk and surface work. Surface work, which is related to the apparent surface stress and the size and geometry of an indenter tip, is necessary in the deformation of a solid surface. Good agreement is found between theoretical first-order approximations and empirical data on depth-dependent hardness, indicating that the apparent surface stress plays an important role in depth-dependent hardness. In addition, we introduce a critical indentation depth. The surface deformation predominates if the indentation depth is shallower than the critical depth, while the bulk deformation predominates when the indentation depth is deeper than the critical depth.

With the development of the materials genome philosophy and data mining methodologies, machine learning (ML) has been widely applied for discovering new materials in various systems including highend steels with improved performance. Although recently, some attempts have been made to incorporate physical features in the ML process, its effects have not been demonstrated and systematically analysed nor experimentally validated with prototype alloys. To address this issue, a physical metallurgy (PM) -guided ML model was developed, wherein intermediate parameters were generated based on original inputs and PM principles, e.g., equilibrium volume fraction (V f ) and driving force (D f ) for precipitation, and these were added to the original dataset vectors as extra dimensions to participate in and guide the ML process. As a result, the ML process becomes more robust when dealing with small datasets by improving the data quality and enriching data information. Therefore, a new material design method is proposed combining PM-guided ML regression, ML classifier and a genetic algorithm (GA). The model was successfully applied to the design of advanced ultrahigh-strength stainless steels using only a small database extracted from the literature. The proposed prototype alloy with a leaner chemistry but better mechanical properties has been produced experimentally and an excellent agreement was obtained for the predicted optimal parameter settings and the final properties. In addition, the present work also clearly demonstrated that implementation of PM parameters can improve the design accuracy and efficiency by eliminating intermediate solutions not obeying PM principles in the ML process. Furthermore, various important factors influencing the generalizability of the ML model are discussed in detail.

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The present work uses Raman spectra to measure residual stresses in Pb(Zr0.53Ti0.47)O3 thin films. Based on thermodynamic analysis, a linear relationship is found between the stress and the square of the Raman frequency in the A1 [transverse optical3 (TO3)] and E [longitudinal optical3 (LO3)] modes. We calibrate the linear relationship by measuring the Raman spectra of stressed bulk Pb(Zr0.53Ti0.47)O3 samples. Then, we assess residual stresses in the lead zirconate titanate thin films at different thicknesses and different annealing temperatures. The residual stresses extracted from the A1(TO3) mode are consistent with those from the E(LO3) mode, which are more or less the same as those measured by the x-ray diffraction sin2 ψ method.