Influence of indentation size and spacing on statistical phase analysis via high-speed nanoindentation mapping of metal alloys

Abstract

The development of high-speed nanoindentation has enabled the acquisition of mechanical property maps over square millimeters of area with micron-scale resolution in reasonable amounts of time. This provides rich datasets which contain morphological and statistical data on the variation of mechanical properties in a microstructure. However, the influences of the indentation size and the deconvolution method employed on the extracted phase properties remain unclear. In this work, a range of depth/spacing increments was explored on two different materials systems, an Al-Cu eutectic alloy and a duplex stainless steel, representing an ‘easy’ and a ‘hard’ case for statistical deconvolution, respectively. A total of ~ 500,000 indentations were performed. A variety of statistical analyses were then employed and compared: the 1D analysis of Ulm et al. using 2 and 3 phases, a 2D rotated Gaussian fit, K-means clustering, and a visual comparison to 2D histograms. This revealed several different sensitivities of the deconvolution methods to various types of error in phase identification. Graphic abstract

Effect of heat treatment on nano-mechanical behaviour of matrix and carbide phases of Fe–13Cr–1C hardfaced alloy coating

“…Material phase properties generally vary depending on composition, heat treatment and texture, particularly when dealing at nano-scale. [7][8][9][10] The weld deposit containing the hard carbide phases were found to be sensitive to cooling procedures. The microstructural modification, morphology variation (refer Figure 1 and 4) and changes in local elemental composition due to heat treatments were evident.…”

“…It was noted that some of the studies have endeavoured to measure directly the strength of matrix phase with or without the influence of grain boundaries through nano-scale tests. 9,12,[22][23][24][25] For example, the hardness of martensite matrix phase alone was measured by Ohmura et al 24 and ferrite matrix alone in micro-alloyed steels by Lee et al 25 Rodriguez et al 12 analysed the influence of indentation size on the mean hardness and elastic modulus of martensite, pearlite and ferrite phases of steels and the results were reported as a function of nanoindentation depth. Besharatloo et al 9 reported that the 'α' ferrite phase showed higher elastic modulus compared to the 'γ' austenite phase in duplex stainless steel.…”

“…In recent years, special attention on the accelerated property mapping (XPM) technique that was used to perform a number of indents in short periods of time for the creation of contour maps of reduced modulus and hardness with high spatial resolution. [6][7][8][9] The insights on the determination of spatial properties and reports on local mechanical property assessments were given by Hintsala et al 6 and Magazzeni et al 8 The property evaluation of phases [10][11][12][13][14]16,17 was elucidated through microstructural characterization 13,[16][17][18][19][20][21] and insights on plasticity 10,15 along with strengthening mechanisms [22][23][24][25] was explored in a diverse manner. For instance, Kumar et al 11 investigated the deformation response of ferrite and martensite phases through nanoindentation experiments and described inhomogeneity in the hardness values of the ferrite phase.…”

Current trends in nanomechanical testing research

“…A significant trend pervading this focus issue is the strongly increasing use of nanoindentation as a high-throughput method for producing large amounts of data [26][27][28][29][30][31], which are then analyzed with emerging data science algorithms. The former is made possible through recent technical developments by hardware manufacturers which aim at performing a single indentation within no more than 1 s (KLA: Express Test and NanoBlitz, Bruker: XPM, Alemnis: UHSR, and others).…”

“…This allows for the measurement of large mappings of up to 100,000 indents within 1-2 days. Such large datasets are then statistically analyzed, e.g., by machine learning algorithms [26]. The aim of such studies Introduction ADVANCED NANOMECHANICAL TESTING Introduction is generally to access the intrinsic mechanical properties of the different components of modern alloys and composite materials.…”