Comparative study of the experimentally observed and GAN-generated 3D microstructures in dual-phase steels

Freestanding three-dimensional (3D) microstructures are widely used in micro-electro-mechanical system (MEMS) applications or can function as microdevices themselves. However, microfabrication methods for freestanding 3D microstructures have limitations in shape, size, cost, and mass production, etc. In this work, integrated photomask micromolding is demonstrated, which uses a portable UV light source and chrome glass micromolding to fabricate 3D microstructures without alignment. Specifically, a chrome layer on one side of the glass micromold shields the excess filling SU-8 photoresist from UV exposure and only the SU-8 photoresist in mold cavities is crosslinked. The 3D microstructures produced using this method have very high dimensional accuracy and the profile error is approximately 1.5%. This method can be used with features of virtually any size and shape and can be integrated into highly-parallel micromolding processes and has potential for MEMS applications.

A scanning tunneling microscope-based multi-axis measuring system is specially developed for the on-machine measurement of three-dimensional (3D) microstructures, to address the quality control difficulty with the traditional off-line measurement process. A typical 3D microstructure of the curved compound eye was diamond-machined by the slow slide servo technique, and then the whole surface was on-machine scanned three-dimensionally based on the tip-tracking strategy by utilizing a spindle, two linear motion stages, and an additional rotary stage. The machined surface profile and its shape deviation were accurately measured on-machine. The distortion of imaged ommatidia on the curved substrate was distinctively evaluated based on the characterized points extracted from the measured surface. Furthermore, the machining errors were investigated in connection with the on-machine measured surface and its characteristic parameters. Through experiments, the proposed measurement system is demonstrated to feature versatile on-machine measurement of 3D microstructures with a curved substrate, which is highly meaningful for quality control in the fabrication field.

Advanced high‐strength steels, like dual phase and TRIP steels, have gained much interest for automotive application. The complex microstructures in dual phase steels, and even more critical, the metastable microstructure in TRIP steels, do not follow the well‐established traditional microstructure‐property relationships for deep drawing steels. The volume fraction of the different phases, the phase distribution, and the stability of metastable phases influence significantly the forming potential.This paper discusses the correlation between different microstructural features and the mechanical properties. The tensile test properties of dual phase steels are governed by the martensite volume fraction, the martensite hardness and to a much smaller extent the martensite island diameter. Both in dual phase and more pronounced in TRIP steels the retained austenite content plays a vital role in determining the formability. The stability of the retained austenite can be described by different methods, it needs to be adjusted according to the forming temperature and the type and amount of strain. In general, multiphase steels require a very strict microstructure control in order to develop predictable forming behaviour.

Reconstruction of three-dimensional heterogeneous media from a single two-dimensional section via co-occurrence correlation function

ABSTRACTThe scalable storage of renewable energy by means of converting water to hydrogen fuels electrochemically hinges on fundamental improvements in catalytic materials. However, many applications exist where an extended lifetime is virtually crucial for their functionality and success, e.g. in case of limited accessibility such as tire pressure sensors or biomedical implants. For these kinds of applications, the ultimate power supply should be a self-renewing energy source. This strategy is pursued by the concept of Micro Energy Harvesting (MEH). Within a MEH system a micro generator converts ambient energy to electrical energy for driving an application. Unfortunately, it is not ensured that the ambient energy level will maintain always high enough to provide sufficient power to the system as harvested energy usually manifests itself in rather irregular, random and low-energy bursts. One appealing form of integrated energy storage is the use of H2/air, a so called fuel cell type (FC) battery. Such devices promise very high volumetric energy densities of more than 2000 Wh/l. Consequently, this type of battery has recently attracted more and more attention and primary as well as secondary cells have been realized. Alkaline polymer electrolyte fuel cells have been recognized as the most promising solution in order to overcome the dependency on noble metal catalysts. Nevertheless, further improvements for these kinds of fuel cells have to be reached with respect to high power. Therefore, one promising approach is to increase the skin surface of porous chromium decorated nickel electrodes for enhancement of exchange current density by forming three-dimensional (3D) microstructures directly into the electrode. Therefore, a novel laser structuring process was applied using ultrashort laser pulses. Ultrashort laser processing of complex multimaterial systems for energy storage allow for precise material removal without changing the material properties. By applying this novel laser-based structuring technique, 3D microstructures could be formed permitting shortened diffusion lengths between the electrolyte and the electrode surface being necessary for increased exchange current densities.

The effect of compound fields of ultrasonic vibration and applied pressure (UV+AP) on three-dimensional (3D) microstructure and tensile properties of recycled Al-Cu-Mn-Fe-Si alloys was systematically studied using conventional two-dimensional (2D) microscopy, synchrotron X-ray tomography, and tensile test. The properties of UV+AP treated alloys with the pouring temperature of 740, 710 and 680 °C were compared when those alloys achieved after gravity casting. After UV+AP treatment, the alloy with pouring temperature of 710 °C show the smallest grain size. Also, the sizes of Fe-rich phases and Al2Cu are greatly reduced and their 3D morphologies are compacted. The mechanical properties of UV+AP treated alloys are relatively higher than those measured for gravity cast equivalents. This improvement can be explained by the synergistic effect of acoustic cavitation, acoustic streaming, and force-feeding, which resulted in the dendrite fragmentation, uniform solute distribution, and microstructural refinement. The Orowan strengthening and solution strengthening were identified as the main strengthening mechanisms.

Three-dimensional (3D) microstructures are fabricated in negative photoresist SU-8 through two-photon polymerization by direct laser writing using a femtosecond pulse laser. The influence of beam power and baking conditions on the obtained structures is investigated. X-ray photoelectron spectroscopy is used to characterize the crosslinking of SU-8 photoresist after two-photon polymerization. The baking time for prebaking at 65 °C and that for soft baking at 95 °C before laser irradiation are investigated. These conditions are significantly related to the migration of photoacid produced upon laser irradiation. 3D woodpiles, helixes, cylinders, jungle gyms, and double cubic structures are fabricated by controlling the micron scale movement in the X, Y, and Z-axis direction. With a small elevation angle, the overlap of helical lines induces the cylindrical wall structure. Double cubic structures are fabricated: small cubic structure inside the outer large cubic structure.

The generation of three-dimensional (3D) microstructures with multiple constituents is an important part of multiscale computational simulation and design for a wide range of materials including heterogeneous polycrystalline metals, ceramics, composites, and energetics. Realistic 3D microstructures for multiphase materials are difficult to obtain experimentally or computationally. Challenges include generation and representation of complex constituent morphologies, topological arrangement and distribution, defect description, and statistical conformity. Here, we present a novel technique for systematically composing complex 3D statistically equivalent microstructure sample sets (SEMSS) with prescribed statistical constituents and morphological attributes. Based on large libraries of varying representations of individual constituents, the technique can be used with experimental micro computerized tomography (CT) scans to establish SEMSS that track the attributes of existing materials as well as to design SEMSS for new materials not yet in existence for computational exploration. Heterogeneous systems involving different combinations of molecular crystallites, metallic particles, oxidizer granules, and a polymeric matrix are designed and generated to track the properties of an existing material. The corresponding SEMSS are used in multiphysics simulations accounting for coupled thermal–mechanical processes or thermal–mechanical-chemically reactive processes. The results are used to quantify microstructure-induced response variations and point out the limitations of two-dimensional (2D) microstructures that are direct sections of the full 3D microstructures. The use of the SEMSS has also enabled uncertainty quantification (UQ) and the development of probabilistic characterizations for variations in macroscopic responses due to intrinsic material microstructural heterogeneities.

Three-dimensional (3D) microstructures have various applications in many fields due to their unique physical properties. Manufacturing 3D microstructures with precise micron-scale features is difficult. Although the assembly of two-dimensional (2D) structures is a smart way to construct complex 3D microstructures, the way to assemble those 2D structures precisely is still immature. One key issue is that alignment errors often occur during the assembly process, affecting the architecture accuracy of the assembled 3D structures. In this paper, we propose a method to eliminate the alignment error during the self-assembly process only by lateral fluid force. Theoretical analysis has been conducted to demonstrate how alignment errors in the assembly channel are automatically corrected, during which a force perpendicular to the flow direction is generated by the channel’s local spatial asymmetry to automatically correct those alignment errors. Besides, the movement of microstructures in the channel has been numerically simulated, whose results were consistent with the theoretical analysis, and there was indeed a lateral force that causes the self-aligning of the microstructure in the channel. The effect of the microstructure’s dimensions and the channel’s size for self-alignment procedure has also been analyzed. It shows that the self-alignment of the microstructure can complete when the ratio of the diameter of microstructures to the width of the channel is greater than 85%. Besides, experiments of the self-alignment between adjacent layers of microstructures were successful, which show the presented idea using lateral fluid force is a promising way to build 3D structures with less assembly errors.

Maximization of strength–ductility balance of dual-phase steels using generative adversarial networks and Bayesian optimization

The goal of this paper is to predict how the properties of the constituent phases and microstructure of dual phase steels (consisting of ferrite and martensite) influence their fracture resistance. We focus on two commercial low-carbon dual-phase (DP) steels with different ferrite/martensite phase volume fractions and properties. These steels exhibit similar flow behavior and tensile strength but different ductility. Our experimental observations show that the mechanism of ductile fracture in these two DP steels involves nucleation, growth and coalescence of micron scale voids. We thus employ microstructure-based finite element simulations to analyze the ductile fracture of these dual-phase steels. In the microstructure-based simulations, the individual phases of the DP steels are discretely modeled using elastic-viscoplastic constitutive relations for progressively cavitating solids. The flow behavior of the individual phases in both the steels are determined by homogenizing the microscale calibrated crystal plasticity constitutive relations from a previous study (Chen et al. in Acta Mater 65:133–149, 2014) while the damage parameters are determined by void cell model calculations. We then determine microstructural effects on ductile fracture of these steels by analyzing a series of representative volume elements with varying volume fractions, flow and damage behaviors of the constituent phases. Our simulations predict qualitative features of the ductile fracture process in good agreement with experimental observations for both DP steels. A ‘virtual’ DP microstructure, constructed by varying the microstructural parameters in the commercial steels, is predicted to have strength and ductile fracture resistance that is superior to the two commercial DP steels. Our simulations provide guidelines for improving the ductile fracture resistance of DP steels.

Using a large-scale, experimentally captured 3D microstructure data set, we implement the generative adversarial network (GAN) framework to learn and generate 3D microstructures of solid oxide fuel cell electrodes. The generated microstructures are visually, statistically, and topologically realistic, with distributions of microstructural parameters, including volume fraction, particle size, surface area, tortuosity, and triple-phase boundary density, being highly similar to those of the original microstructure. These results are compared and contrasted with those from an established, grain-based generation algorithm (DREAM.3D). Importantly, simulations of electrochemical performance, using a locally resolved finite element model, demonstrate that the GAN-generated microstructures closely match the performance distribution of the original, while DREAM.3D leads to significant differences. The ability of the generative machine learning model to recreate microstructures with high fidelity suggests that the essence of complex microstructures may be captured and represented in a compact and manipulatable form.

The article presents an analysis of neural networks that can be used to generating web page program code from input image. As part of the research of the possibilities of modification of algorithms for generating code, the method of generating code simultaneous using the generative adversarial and recurrent neural networks based on the input image is proposed. Methods for creating source code using generative adversarial network (GAN) and recurrent neural networks (RNN) are analyzed. Using domain-specific language (DSL) for tree generation code method, it was possible to use GAN and RNN together, achieving on the average 10% productivity increase. Using GRID’s mesh, the generated code is adaptable. To implement the proposed method, software with a graphical user interface is created. Classes of GAN and RNN were considered, among which the feasibility of using a neural network with long-term short-term memory (LSTM) for analysis and forecasting of tags, blocks and HTML elements of the language in the input image was selected and substantiated. The stages of the research are presented. For the experiment datasets made on the basis of existing sites from open sources for 2018–2019 are used. The estimation of the effectiveness of the proposed method is described.

Understanding the effects of martensite volume fractions (V m) in dual phase (DP) steel resulting from heat treatment is crucial for designing structures for mechanical impact resistance and optimizing manufacturing processes. DP steel’s material behaviour depends heavily on its microstructure properties. While stress-strain curves for individual phases in DP steels are often determined using empirical models, extensive experimental data is required to establish empirical model constants. This research aims to achieve two main objectives: firstly, to calibrate stress-strain curves for pure ferrite and pure martensite using limited experimental data using micromechanical adaptive iteration algorithm (MAIA). This calibration involves using stress-strain data from DP steels with varying V m during the calibration stage and additional data for verification. Secondly, to conduct a comprehensive sensitivity analysis of MAIA to assess its capabilities and limitations. Microstructure-based finite element (FE) models, simulated with ABAQUS/Standard, are employed to predict stress-strain curves under uniaxial tensile test conditions. The MAIA approach successfully calculated ferrite and martensite stress-strain curves that could predict plastic behaviour of DP steel with different V m, which agreed with experimental work. Key advantages of this approach include avoiding complex 3D microstructure geometries and requiring only two experimentally obtained stress-strain curves with different Vm for material constant calibration, along with another curve for validation. However, the experimental data selected for calibration must have a V m difference between 20%–50% and one of the DP steels must have a low martensite volume fraction. The FE micromechanical model could capture the effect of softening of martensite phase and strengthening of ferrite phase as compared to its bulk properties for DP steel. The effect of V m on strain hardening rate was also successfully captured. This technique comes with obvious shortcomings, such as excluding crystal plasticity behaviour, and change in chemical composition within the individual phase with varying martensite volume fraction.

Magnesium alloys have the attractive combination of lightweight and strength. An understanding of solidification microstructures in these materials is important. An accurate means of quantifying microstructure in 3D is extremely important. In this study, we have used serial polishing and synchrotron-based x-ray tomography technique as a means of 3D characterization of the solidified microstructures of magnesium-based alloys. These models were also used to conduct quantitative analysis in 3D. The phase fraction and morphologies of intermetallics and α-Mg matrix phase were obtained. The phase fractions of β-Mg17Al12 and Al–Mn intermetallics are consistent with measurements in the literature and calculations based on the Scheil–Gulliver solidification model. Our 3D reconstructions also show that the dendrite morphology has sixfold symmetry. The results of 3D microstructural characterization and analysis will enable a comprehensive understanding of solidification variables, microstructure, and properties.