Mechanical properties of materials can be derived from the force-displacement relationship through instrumented indentation tests. Complications arise when establishing the full elastic-plastic stress-strain relationship as the accuracy depends on how the material’s and indenter’s parameters are incorporated. For instance, the effect of the material work-hardening phenomenon such as the pile-up and sink-in effect cannot be accounted for with simplified analytical indentation solutions. Due to this limitation, this paper proposes a new inverse analysis approach based on dimensional functions analysis and artificial neural networks (ANNs). A database of the dimensional functions relating stress and strain parameters of materials has been developed. The database covers a wide range of engineering materials that have the yield strength-to-modulus ratio (σ_y/E) between 0.001 to 0.5, the work-hardening power (n) between 0–0.5, Poisson’s ratio (v) between 0.15–0.45, and the indentation angle (θ) between 65–80 degrees. The proposed algorithm enables determining the nanomechanical stress-strain parameters using the indentation force-displacement relationship, and is applicable to any materials that the properties are within the database range. The obtained results are validated with the conventional test results of steel and aluminum samples. To further demonstrate the application of the proposed algorithm, the nanomechanical stress-strain parameters of ordinary Portland cement phases were determined.

The determination of elastic modulus (E) and hardness (H) relies on the accuracy of the contact area under the indenter tip, but this parameter cannot be explicitly measured during the nanoindentation process. This work presents a new approach that can derive the elastic modulus (E) and contact depth (h_c) based on measured experiment stiffness using the continuous-stiffness-measurement (CSM) method. To achieve this, an inverse algorithm is proposed by incorporating a set of stiffness-based relationship functions that are derived from combining the dimensional analysis approach and computational simulation. This proposed solution considers both the sink-in and pile-up contact profiles; therefore, it provides a more accurate solution when compared to a conventional method that only considers the sink-in contact profile. While the proposed solution is sensitive to Poisson’s ratio (ν) and the equivalent indentation conical angle (θ), it is not affected by material plasticity, including yield strength (σ_y) and work hardening (n) for the investigated range of 0.001 < σ_y/E < 0.5. The proposed stiffness-based approach can be used to consistently derive elastic modulus and hardness by using stiffness and the load-and-unload curve measured by the continuous-stiffness-measurement (CSM) method.

The transversely isotropic behaviour of thermal sprayed aluminium and zinc coating has been investigated based on a combination of nanoindentation experimental data and microporomechanics theory. A recently developed strength homogenisation approach comprises of the solid and porous medium is adopted to investigate the morphology properties of thermal sprayed aluminum and zinc coating. The finding of this paper demonstrates that the individual aluminum and zinc phases in the coating have a characteristic packing density close to the theoretical highest spherical packing ratio for face-centred cubic and hexagonal close packed. Also, the plasticity properties of solid particles in both aluminum and zinc are found to have a significant transversely isotropic condition, while the elasticity properties are close to isotropic. These findings led to the conclusion that the anisotropic condition of the coating is dominantly affected by the plasticity properties, in terms of cohesion and friction coefficient.