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An efficient deep learning method is presented for distinguishing microstructures of a low carbon steel. There have been numerous endeavors to reproduce the human capability of perceptually classifying different textures using machine learning methods, but this is still very challenging owing to the need for a vast labeled image dataset. In this study, we introduce an unsupervised machine learning technique based on convolutional neural networks and a superpixel algorithm for the segmentation of a low-carbon steel microstructure without the need for labeled images. The effectiveness of the method is demonstrated with optical microscopy images of steel microstructures having different patterns taken at different resolutions. In addition, several evaluation criteria for unsupervised segmentation results are investigated along with the hyperparameter optimization.

Surface wettability is an important parameter that affects nucleate boiling. Irradiation can alter the surface wettability on metal surfaces without altering the surface macrostructure. The wettability characteristics of indium tin oxide and TiO2 film-coated sapphire substrates following gamma-ray and electron beam irradiation were experimentally investigated. A sapphire plate was exposed to gamma rays and electron beams. Within the irradiation dose, no evident change in the sapphire surface color was found. The surface contact angle decreased after irradiation, and surface wettability was enhanced with more irradiation. After irradiation, the contact angle recovered with time. The related mechanism is possible due to the absorption/desorption of hydroxyl groups. Our results indicate that the irradiation method can be used in indium tin oxide film-coated sapphire experiments to study nucleate boiling.

In this study, a numerical approach was proposed to predict the failure of resistance spot welds, into which theoretical and empirical models as well as finite element (FE) model are integrated. In this approach, the hardness of spot welds was first evaluated and the estimated hardness was then used to determine the local mechanical properties. The weld geometry and boundary conditions were faithfully followed when generating the FE model. A Gurson-type damage model and a cohesive zone model were simultaneously considered for the two main failure modes, i.e. pull-out failure and interface failure. The predictions were validated by experimental observations. The successful estimation of critical nugget size, as an exemplary application, demonstrated the utility and reliability of this approach.

In-situ measurement by digital holographic microscopy (DHM) was employed to clarify the difference between the surface relief effects of Widmanstatten ferrite (WF) and bainitic ferrite (BF) associated with their transformations from austenite in a low-carbon steel. It was clarified that the evolution of the surface relief is totally different between WF and BF. Only in BF, the observed ratio of the relief height to the plate width converged instantly to the value predicted by the phenomenological theory of martensite crystallography (PTMC). The ratio gradually increased with increasing plate thickness and never reached the value predicted by PTMC in WF.

In material design, microstructure characterization and reconstruction are indispensable for understanding the role of a structure in a process-structure-property relation. The significant contribution of this paper is to introduce a methodology for the characterization and generation of material microstructures using deep generative networks as the first step in the establishment of a process-structure-property linkage for forward or inverse material design. Our approach can be divided into two parts: (i) characterization of material microstructures by a vector quantized variational auto-encoder, and (ii) determination of the correlation between the extracted microstructure characterizations and the given conditions, such as processing parameters and/or material properties, by a pixel convolutional neural network. As an example, we tested our framework in the generation of low-carbon-steel microstructures from the given material processing. The results were in satisfactory agreement with the experimental observation qualitatively and quantitatively, demonstrating the potential of applying the proposed method to forward or inverse material design. One of the advantages of the proposed methodology lies in the capability to capture the stochastic nature behind the microstructure generation. As a result, this methodology enables us to build a process-structure-property linkage while quantifying uncertainties, which not only makes a prediction more robust but also shows a way toward enhancing our understanding of the stochastic competitive phenomena behind the generation of material microstructures.

The strengthening mechanism of the 2000 series aluminum alloy has been studied using neural networks. We have constructed a neural network for the simultaneous prediction of multiple mechanical properties, including ultimate tensile strength, tensile yield strength, and elongation at break. The replica-exchange Monte Carlo method, an improved Markov chain Monte Carlo (MCMC) method, has been applied for Bayesian learning of the optimal neural network architecture and hyperparameters. The obtained neural network, combined with the thermodynamic analysis using the Thermo-Calc software, enables us to identify a dominant combination of additive elements and heat treatments for strengthening alloys. We have also addressed an inverse problem for optimizing the process parameters. The approach we propose will accelerate the design of high strength alloys for high-temperature applications.

The cerebellum is essential for motor learning and adaptation since it can change the relationship between sensory input and motor output. Cerebellar activity contributes to the motor execution with the aim of optimizing the resulting performance. This optimization is driven by the error information, that the cerebellum receives through the sensory feedback. In this study, we aim at studying the cerebellar contribution in the saccadic adaptation, fine and rapid control of eye movements. We set up a virtual experiment where an iCub robot, controlled by a cerebellar-inspired spiking neural network, was challenged in a saccadic adaptation task. The virtual robot, at first, made inaccurate saccades and therefore attempted to adjust them, trial after trial, based on sensory error feedback. The spiking neural network model was equipped with plasticity mechanisms, that allowed the robot to change and adapt the amplitude of the saccade movement after a few trials. Interestingly, we observed that for this task a single plasticity mechanism was sufficient to produce a fast adaptation, while in other cerebellar-driven tasks, the addition of more plasticities granted an improvement of the adaptation performances.

Cross-sectional observations of the deformation bands formed during in situ tensile observation at the free surface of both ultralow- and low-carbon lath martensitic steels were conducted. The cross-sectional observations clearly revealed the formation of steps along substructure boundaries of the ultralow-carbon lath martensite during deformation. In contrast, severe undulation indicating strain accumulation was observed in the vicinity of high-angle boundaries for the low-carbon lath martensite, and no clear steps were formed along substructure boundaries, even though small filmlike ferrites were found. The “greasy-film” mechanism proposed by Maresca [Maresca et al., Modelling and Simulation in Materials Science and Engineering 24(2016)025006, Maresca and Curtin, Acta Materialia 134(2017)302] as well as the plastic instability theory proposed by Dao and Asaro [Dao and Asaro: Mech. Mater., 23(1996)71] was examined considering the non-Schmid effects and the anisotropic activation of slip systems in lath martensite.

One of the most powerful excitatory synapses in the brain is formed by cerebellar climbing fibers, originating from neurons in the inferior olive, that wrap around the proximal dendrites of cerebellar Purkinje cells. The activation of a single olivary neuron is capable of generating a large electrical event, called "complex spike," at the level of the postsynaptic Purkinje cell, comprising of an initial large-amplitude spike followed by a long polyphasic tail of small-amplitude spikelets. Several ideas discussing the role of the cerebellum in motor control are centered on these complex spike events. However, these events, only occurring one to two times per second, are extremely rare relative to Purkinje cell "simple spikes" (standard sodium-potassium action potentials). As a result, drawing conclusions about their functional role has been very challenging. In fact, because standard spike sorting approaches cannot fully handle the polyphasic shape of complex spike waveforms, the only safe way to avoid omissions and false detections has been to rely on visual inspection by experts, which is both tedious and, because of attentional fluctuations, error prone. Here we present a deep learning algorithm for rapidly and reliably detecting complex spikes. Our algorithm, utilizing both action potential and local field potential signals, not only detects complex spikes much faster than human experts, but it also reliably provides complex spike duration measures similar to those of the experts. A quantitative comparison of our algorithm's performance to both classic and novel published approaches addressing the same problem reveals that it clearly outperforms these approaches.NEW & NOTEWORTHY Purkinje cell "complex spikes", fired at perplexingly low rates, play a crucial role in cerebellum-based motor learning. Careful interpretations of these spikes require manually detecting them, since conventional online or offline spike sorting algorithms are optimized for classifying much simpler waveform morphologies. We present a novel deep learning approach for identifying complex spikes, which also measures additional relevant neurophysiological features, with an accuracy level matching that of human experts yet with very little time expenditure.

The division of neutron detector panel regions has improved the precision of complex texture evaluation and appropriate sample rotation enhances the texture reliability in a limited neutron beam time. The TAKUMI instrument has achieved satisfactory texture precision for a limestone standard sample. The compressive rolling direction–transverse direction in-plane stress field was quantitatively measured in martensite layers of a martensite–austenite multilayered steel, and this stress field was found to originate from the martensite transformation strain and the linear contraction misfit between austenite layers and newly transformed martensite layers., Neutron diffraction texture measurements provide bulk averaged textures with excellent grain orientation statistics, even for large-grained materials, owing to the probed volume being of the order of 1 cm3. Furthermore, crystallographic parameters and other valuable microstructure information such as phase fraction, coherent crystallite size, root-mean-square microstrain, macroscopic or intergranular strain and stress, etc. can be derived from neutron diffractograms. A procedure for combined high stereographic resolution texture and residual stress evaluation was established on the pulsed-neutron-source-based engineering materials diffractometer TAKUMI at the Materials and Life Science Experimental Facility of the Japan Proton Accelerator Research Center, through division of the neutron detector panel regions. Pole figure evaluation of a limestone standard sample with a well known texture suggested that the precision obtained for texture measurement is comparable to that of the established neutron beamlines utilized for texture measurement, such as the HIPPO diffractometer at the Los Alamos Neutron Science Center (New Mexico, USA) and the D20 angle-dispersive neutron diffractometer at the Institut Laue–Langevin (Grenoble, France). A high-strength martensite–austenite multilayered steel was employed for further verification of the reliability of simultaneous Rietveld analysis of multiphase textures and macro stress tensors. By using a texture-weighted geometric mean micromechanical (BulkPathGEO) model, a macro stress tensor analysis with a plane stress assumption showed a rolling direction–transverse direction (RD–TD) in-plane compressive stress (about −330 MPa) in the martensite layers and an RD–TD in-plane tensile stress (about 320 MPa) in the austenite layers. The phase stress partitioning was ascribed mainly to the additive effect of the volume expansion during martensite transformation and the linear contraction misfit between austenite layers and newly transformed martensite layers during the water quenching process.

A novel and efficient data mining method for the segmentation of microstructural images of low-carbon steel is presented. Microstructural characterization has been the focus of many works in the field of materials science because microstructure is the fundamental element in understanding the link between process and property. Recently, deep-learning-based methods have been actively employed for microstructural classification since it has shown outstanding performance for solving image classification problems. However, previous applications of deep learning models to microstructural classification revealed limitations in that not only do they require the time-consuming labeling process but it is also still difficult to obtain a satisfactory result, especially for steel microstructures containing substances developed by displacive transformation. In this study, we propose a rule-based segmentation method that not only works without labeled images but also requires no prior knowledge of the number of microstructural constituents in each image. This unsupervised inference algorithm captures the morphological features of each microstructure and automatically finds the optimal number of microstructures having similar characteristics using a Bayesian Gaussian mixture model. The viability of our method is demonstrated by qualitative and quantitative evaluations with optical microscopy images of steel composed of different microstructures taken under different imaging conditions.

A simple technique for measurement of delamination toughness in environmental barrier coatings (EBCs) under shear loading has been developed. The technique overcomes micro damage accumulation properties of CMC substrate and a delamination toughness with a phase angle of ~ 80° could be obtained using a simple fixture. Demonstration of the present procedure is carried out using a BSAS/mullite + BSAS/Si standard EBC layer on a three-dimensional woven fabric SiC/SiC composite substrate. The delamination path suggests that it is possible to apply the method to wavy EBC coatings and also to thermal barrier coatings.

In the present study, different combinations of multilayer sheets were prepared from 1 and 2 mm Mg AZ31 along with 0.25, 0.5, and 1 mm 304 L stainless steel. The texture and microstructure of the elongated samples (20 and 30 pct strain) were studied. It was found that the transversal stress plays an important role in both texture evolution and twinning in these composites. The obtained pole figures revealed an axial texture tilt with increasing steel layer volume fraction (V f). It was found that this is a direct effect of transverse stress, which becomes more significant upon reducing Mg V f. This extra stress component tilts the basal planes away from the original normal direction in monolithic samples. Moreover, our results indicate that with decreasing Mg V f, twinning activity was increased in the 20 pct deformed samples but reduced in the samples with 30 pct elongation. It is known that at high strains where sufficient transverse stress is generated, the activity of prismatic slip is significantly enhanced, which promotes the motion of dislocations and reduces the necessity of twinning. With decreasing Mg V f, stronger transversal stress is generated and Mg reaches the critical threshold of prismatic activity at lower strains.

The deformation modes of Mg in a steel-Mg-steel multilayer composite were studied by step-by-step deformation and EBSD for the same region at 0, 20 and 30% deformation. Texture evolution was characterized using pole figures. The results indicate that the basal texture intensity significantly increases up to 20% deformation. However, from 20% to 30% deformation, activity of prismatic slip increases and the intensity of the overall basal texture is reduced. Shear stress and Schmid factor analysis of the multilayer system showed the probability of prismatic slip increases up to 35% by increasing the stress ratio up to 0.4 and decreases afterwards. Keywords: Light alloys, Magnesium, Laminated metal composite, Texture evolution

We propose a Bayesian inference methodology to evaluate unobservable parameters involved in multi-phase-field models to accurately predict the observed grain growth, such as in metals and alloys. This approach integrates models and a set of observational image data of grain structures. Because the image data set is not a time series, directly applying conventional inference techniques that require time series as the input data is difficult. The key idea in our methodology to overcome this difficulty is to construct a time series with an appropriate statistic that characterizes static image data of grain structures. Our methodology implements the empirical Bayes method. It can estimate not only a probability density function of the parameters but also an initial phase field, which is generally unobservable in real experiments. After validating the proposed method through numerical tests using synthetic data, we apply it to real experimental images of grain structures in a steel alloy. The proposed method properly estimates unobservable parameters along with their uncertainties and successfully selects the initial phase field that best explains the experimental data from among candidate initial phase fields.

Different multilayer Mg AZ31 and SS304L steel sheet combinations were prepared with different volume fractions of Mg. Isolated stress–strain curves of the Mg layers showed significant improvements in the strength and elongation of multilayer samples. Results indicated that in the most extreme situation with the lowest Mg volume fraction (V f = 0.39), the ultimate strength was increased by 25 pct to 370 MPa and the elongation was improved by 70 pct to 0.34. Investigation of the fracture surface showed that failure occurs by the coalescence of cracks close to the interface region. The improved strength of the multilayer samples was due to the combined effect of surface crack prevention by the steel layer and the higher work-hardening rate caused by the possible increased activity of non-basal systems. It is suggested that the stronger work-hardening behavior and the enhanced activity of non-basal systems in the multilayer samples were due to the formation of new stress components in the transverse direction. The larger the volume fraction of steel in the multilayer, the longer the distance remaining unstrained before the UTS.

Multilayered composite steels consisting of alternating layers of martensitic phase and austenitic phase exhibit an excellent combination of strength and elongation compared with conventional advanced high-strength steels. The deformation processes underlying these properties are of considerable interest. In this article, microstructure, grain size, and phase are characterized by scanning electron microscopy (SEM) and electron backscattering diffraction. The hardness of each layer is analyzed by a microindentation hardness testing system. Finally, the deformation and failure processes in multilayered steel are investigated by in-situ SEM. The hardness results indicate that various hardening modes occur in the soft austenitic layer and the hard martensitic layer. In-situ SEM results combined with microstructure analysis and hardness results reveal that annealing temperature and annealing time have a significant impact on final microstructure, fracture behavior, strength, hardness, and ductility.

A Bayesian approach is presented for clarifying the best kinetic model explaining the transformation kinetics of a low-carbon steel under different continuous cooling conditions only from dilatometric curves. To estimate kinetic parameters as well as the model plausibility of candidate kinetic models, the exchange Markov chain Monte Carlo method was used. The effectiveness of the proposed method was demonstrated by metallographic investigations of the ferrite formation in a Fe-0.15C-1.5Mn alloy. It is shown that the method is successfully applied for clarifying ferrite transformation kinetics, such as transformation start temperatures, formation mechanisms, and fractions of microstructures. In comparison with a previous experimental study, it is also presented that the important parameter determining the ferrite nucleation rate can be estimated only from dilatometric curves without the help of intensive metallographic observations.

Herein, the factors influencing the deformation behavior of lath martensite along with the crystallographic features, we used multilayered steels (MLS) containing martensitic steel and transformation-induced plasticity (TRIP) steel. Since martensite has an extremely strong and low ductility, ductile TRIP steels are coupled with the Martensitic steels to increase overall the plasticity of MLS. The influence factors were divided into three types such as the size of prior austenite grains (PAGs), the alignment of blocks with a tensile axis, and position of block (e.g., vicinity or center of PAG) around the blocks, and analyzed by matching microstructure and mechanical properties. The martensitic features up to an engineering strain of 20% were observed by means of an ex-situ electron backscatter diffraction (EBSD), compared with the operative slip systems of each influencing factor. The operative slip system was derived from an activation priority of the slip systems during deformation with the classification in two types such as in-lath-plane and out-of-lath plane systems. A fine block located near the PAG boundary with an initial shape parallel to the tensile axis is significantly deformed because of an activated multiple slip system, matching with the result by means of the crystal plasticity simulations.

In this work we present a novel in-situ approach to study the recrystallization behavior of age hardening alloys. We use confocal laser scanning microscopy (CLSM) at 400 °C to investigate the static recrystallization of an AlMg4Sc0.4Zr0.12 alloy in-situ. The results are combined with electron backscatter diffraction (EBSD) and transmission electron microscopy (TEM) analyses. It was found that CLSM is a powerful tool to visualize both the local initiation and temporal sequence of recrystallization. After fast nucleation and initial growth, the grain growth rate decreases and the grain boundary migration stops after some minutes due to Zener pinning from Al 3 (Sc,Zr) precipitates produced during the heat treatment. EBSD and TEM analyses confirm both the boundary movements and the particle-boundary interactions.

Significant crystallographic refinement was observed in the as-quenched lath martensitic steel during tensile deformation up to a strain of 20 pct. Crystallographic investigation by electron backscattering diffraction analysis coupled with the use of crystal plasticity model confirmed that the activation behavior of slip systems is inhomogeneous even within each martensite block. In particular, the activation behavior of slip systems is initially affected by the initial crystal orientation; however, the effect becomes negligible with increasing deformation. In addition, slip systems parallel to the lath plane are preferentially activated during elongation. Our results indicate that the nonuniform crystal rotation within individual blocks is induced not only by grain interaction phenomena but also by the anisotropic activity of slip systems. These are key factor that determines the grain refinement behavior in the as-quenched lath martensitic steel during deformation.