Calcite twinning is a dominant deformation mechanism at low temperatures. It is often used to reconstruct paleostresses: orientations of the principal stress axes, stress ratios and differential stress. Despite numerous studies, on single crystals and aggregates, questions remain about the initiation and evolution of the twinning. In particular, the existence of a critical value for the activation of twin planes is debated. In this study, Carrara marble samples were uniaxially deformed at low temperature. The experiments were monitored in situ in an SEM (Scanning Electron Microscope) and a deformation analysis was performed at regular intervals using image correlation. Image correlation analysis shows the link between the overconcentration of strains and the appearance of the first twinned planes. This is followed by a densification and a gradual thickening of the twin lamellae. Fracturing only appears in a third stage as a precursor to the collapse of the sample. The inversion, using the CSIT-2 technique, showed that the twinned planes are globally related to the applied macroscopic stress. The inversion allows one to retrieve the macroscopic stress tensor. Schmid factors were extracted from this analysis and correlated to the loading curves. For crystals of about 200 µm diameter, the threshold value is in between 6.75 and 8.25 MPa.

PeakForce quantitative nanomechanical AFM mode (PF-QNM) is a popular AFM technique designed to measure multiple mechanical features (e.g., adhesion, apparent modulus, etc.) simultaneously at the exact same spatial coordinates with a robust scanning frequency. This paper proposes compressing the initial high-dimensional dataset obtained from the PeakForce AFM mode into a subset of much lower dimensionality by a sequence of proper orthogonal decomposition (POD) reduction and subsequent machine learning on the low-dimensionality data. A substantial reduction in user dependency and subjectivity of the extracted results is obtained. The underlying parameters, or “state variables”, governing the mechanical response can be easily extracted from the latter using various machine learning techniques. Two samples are investigated to illustrate the proposed procedure (i) a polystyrene film with low-density polyethylene nano-pods and (ii) a PDMS film with carbon–iron particles. The heterogeneity of material, as well as the sharp variation in topography, make the segmentation challenging. Nonetheless, the underlying parameters describing the mechanical response naturally offer a compact representation allowing for a more straightforward interpretation of the high-dimensional force–indentation data in terms of the nature (and proportion) of phases, interfaces, or topography. Finally, those techniques come with a low processing time cost and do not require a prior mechanical model.

Ultrafine-grained Al matrix nanocomposites, reinforced with Al₂O₃ nanoparticles, were produced from milled powders, either by equal channel angular pressing (ECAP), at room or high temperature, with or without back pressure, or by spark plasma sintering (SPS). Their microstructures, mechanical properties (compression, hardness, and sliding wear), and thermal stabilities (thermally induced softening and cracking) were compared, and the advantages and limitations of each process discussed on a scientific but also practical point of view. For the most successful set of process parameters, the yield stress in compression reached 380 MPa, the hardness, HV = 139, remained stable up to 500 °C, and the resistance to sliding wear was comparable to that of Al 5083, and better than that of Al 7075-T6. While the samples consolidated at high temperatures (by ECAP or SPS) showed a good thermal stability, those consolidated by ECAP at room temperature were prone to thermally induced softening and cracking, which was related to trapped and pressurized gases.