Processing-structure Relationships Research Articles

Understanding Process-Structure Relationships during Lamination of Halide Perovskite Interfaces.

Fabrication of halide perovskite (HP) solar cells typically involves the sequential deposition of multiple layers to create a device stack, which is limited by the thermal and chemical incompatibility of top contact layers with the underlying HP semiconductor. One emerging strategy to overcome these restrictions on material selection and processing conditions is lamination, where two half-stacks are independently processed and then diffusion bonded to complete the device. Lamination reduces the processing constraints on the top side of the solar cell to allow new device designs, expanded use of deposition methods, and self-encapsulation of devices. While laminated perovskite solar cells with high efficiencies and novel interlayer combinations have been demonstrated, there is a limited understanding of how the lamination process parameters affect the diffusion-bond quality and material properties of the resulting HP layer. In this study, we systematically vary temperature, pressure, and time during lamination and quantify the resulting impacts on bonded area, grain domain size, and photoluminescence. A design of experiments is performed, and statistical analysis of the experimental results is used to quantitatively evaluate the resulting process-structure-property relationships. The lamination temperature is found to be the key parameter controlling these properties. A temperature of 150 °C enables successful bonding over 95% of the substrate area and also results in increases in apparent grain domain size and photoluminescence intensity. Based on these insights, the lamination temperature of functional perovskite solar cell devices is varied, demonstrating the importance of the resulting bond quality on device performance metrics.

In-plane properties of an in-situ consolidated automated fiber placement thermoplastic composite

Automated fiber placement (AFP) has been employed to manufacture aerospace structures for decades, recently focusing on thermoplastic composites (TPC). The in-situ consolidation AFP of TPCs is pursued as an energy-efficient additive manufacturing (AM) approach for fabricating composite structures. This work compares the in-plane mechanical properties of in-situ consolidated coupons with those of compression molded counterparts to provide new insights into their failure mechanics and processing-structure relationships. Tensile and compressive properties along the fiber and transverse directions, in-plane shear properties, and short beam strength were measured for all samples. Failure modes and mechanics in tested coupons were related to AFP defects and processing, i.e., resultant crystallinities, fiber misalignment, matrix mechanical properties, porosity, and fiber–matrix interfacial strength. The findings of this study can be used to guide the manufacturing of future TPC structures and potentially open new avenues for applications where post-processing is not feasible or reduced mechanical performance is acceptable.

From fabrication to mechanical properties: exploring high-entropy oxide thin films and coatings for high-temperature applications

High-entropy oxides (HEOs) containing five or more cations have garnered significant attention recently due to their vastly tunable compositional space, along with their remarkable physical and mechanical properties, exceptional thermal stability, and phase reversibility at elevated temperatures. These characteristics position HEOs as promising candidates for structural components and coatings in high-temperature applications. While much of the ongoing research on HEOs centers around understanding processing-structure relationships, there remains a dearth of knowledge concerning their mechanical properties, crucial for their prospective high-temperature applications. Whether in bulk form or as coatings, the efficacy of HEOs hinges on robust mechanical properties across a spectrum of temperatures, to ensure structural integrity, fracture resistance, and resilience to thermal stress. This review offers a succinct synthesis of recent advancements in HEO research, spanning from processing techniques to mechanical behaviors under extreme conditions. Emphasis is placed on three key aspects: (1) Investigating the influence of processing parameters on HEO crystal structures. (2) Analyzing the interplay between crystal structure and mechanical properties, elucidating deformation mechanisms. (3) Examining the mechanical behavior of HEOs under extreme temperatures and pressures. Through this review, we aim to illuminate the effective control of HEOs’ unique structures and mechanical properties, paving the way for their future applications in extreme environments.

High-Throughput Microstructural Characterization and Process Correlation Using Automated Electron Backscatter Diffraction

The need to optimize the processing conditions of additively manufactured (AM) metals and alloys has driven advances in throughput capabilities for material property measurements such as tensile strength or hardness. High-throughput (HT) characterization of AM metal microstructure has fallen significantly behind the pace of property measurements due to intrinsic bottlenecks associated with the artisan and labor-intensive preparation methods required to produce highly polished surfaces. This inequality in data throughput has led to a reliance on heuristics to connect process to structure or structure to properties for AM structural materials. In this study, we show a transformative approach to achieve laser powder bed fusion (LPBF) printing, HT preparation using dry electropolishing and HT electron backscatter diffraction (EBSD). This approach was used to construct a library of > 600 experimental EBSD sample sets spanning a diverse range of LPBF process conditions for AM Kovar. This vast library is far more expansive in parameter space than most state-of-the-art studies, yet it required only approximately 10 labor hours to acquire. Build geometries, surface preparation methods, and microscopy details, as well as the entire library of >600 EBSD data sets over the two sample design versions, have been shared with intent for the materials community to leverage the data and further advance the approach. Using this library, we investigated process–structure relationships and uncovered an unexpected, strong dependence of microstructure on location within the build, when varied, using otherwise identical laser parameters.

Determining the Effects of Inter-Layer Time Interval in Powder-Fed Laser-Directed Energy Deposition on the Microstructure of Inconel 718 via In Situ Thermal Monitoring.

Cylindrical Inconel 718 specimens were fabricated via a blown-powder, laser-directed energy deposition (DED-L) additive manufacturing (AM) process equipped with a dual thermal monitoring system to learn key process-structure relationships. Thermographic inspection of the heat affected zone (HAZ) and melt pool was performed with different layer-to-layer time intervals of ~0 s, 5 s, and 10 s, using an infrared camera and dual-wavelength pyrometer, respectively. Maximum melt pool temperatures were found to increase with layer number within a substrate affected zone (SAZ), and then asymptotically decrease. As the layer-to-layer time interval increased the HAZ temperature responses became more repetitive, indicating a desirable approach for achieving a more homogeneous microstructure along the height of a part. Microstructural variations in grain size and the coexistence of specific precipitate phases and Laves phases persisted among the investigated samples despite the employed standard heat treatment. This indicates that the effectiveness of any post DED-L heat treatment depends significantly on the initial, as-printed microstructure. Overall, this study demonstrates the importance of part size, part number per build, and time intervals on DED-L process parameter selection and post-process heat treatments for achieving better quality control.

Full laser irradiation processed Pb-graphene nanocomposite electrodes toward the manufacturing of high-performance supercapacitors

Facile and scalable manufacturing of metal decorated carbon electrodes is highly expected for next-generation energy-storage devices. Inspired by unique processing characteristics of laser-induced graphene (LIG) as well as electrochemical advantages of Pb, a full-laser protocol is developed for assembling Pb doped LIG (Pb-LIG) electrodes through two separate laser-irradiation processes to selectively irradiate polyimide (PI) for forming graphene framework and to selectively melt-and-recrystallize Pb powders. Along with high-efficient production and customizable sizes and shapes, Pb dosage and laser power are systematically investigated as two critical parameters to understand the process-structure relationship. By simultaneously considering the two factors, an interesting two-regime effect has been discovered, which is guiding for achieving optimized Pb content of 77.77 wt% as long as laser fluence per Pb dosage is around 100 J mg−1. Owing to clear redox faraday reaction, the integrated Pb content is indeed fundamental for promoting capacitive performance, by realizing a six-fold enhancement of areal capacitance from 25 to 128 mF cm−2. Finally, multiple Pb-LIG supercapacitors are facilely combined either in series or in parallel to expand its performance for energy-storage applications.

Experimental and Numerical Investigation of Hot Extruded Inconel 718

Inconel 718 is a widely used superalloy, due to its unique corrosion resistance and mechanical strength properties at very high temperatures. Hot metal extrusion is the most widely used forming technique, if the manufacturing of slender components is required. As the current scientific literature does not comprehensively cover the fundamental aspects related to the process–structure relationships, in the present work, a combined numerical and experimental approach is employed. A finite element (FE) model was established to answer three key questions: (1) predicting the required extrusion force at different extrusion speeds; (2) evaluating the influence of the main processing parameters on the formation of surface cracks using the normalized Cockcroft Latham’s (nCL) damage criterion; and (3) quantitatively assessing the amount of recrystallized microstructure through Avrami’s equation. For the sake of modeling validation, several experimental investigations were carried out under different processing conditions. Particularly, it was found that the higher the initial temperature of the billet, the lower the extrusion force, although a trade-off must be sought to avoid the formation of surface cracks occurring at excessive temperatures, while limiting the required extrusion payload. The extrusion speed also plays a relevant role. Similarly to the role of the temperature, an optimal extrusion speed value must be identified to minimize the possibility of surface crack formation (high speeds) and to minimize the melting of intergranular niobium carbides (low speeds).

Visualization of small-angle X-ray scattering datasets and processing-structure mapping of isotactic polypropylene films by machine learning

With the rapid development of the synchrotron radiation X-ray characterization techniques, the preprocessing of large small-angle X-ray scattering (SAXS) datasets and the data mining become urgent requirements for researchers. In this work, we apply the variational autoencoder (VAE) and the conditional variational autoencoder (cVAE) to visualize a large SAXS dataset of hard-elastic isotactic polypropylene (iPP) films in 2- and 1-dimensional latent spaces. The low-dimensional representations enable us to capture key features of the dataset rapidly, such as the similarity among SAXS patterns and the structural evolution trends. The preprocessing of the dataset points out the further direction of data analysis so that researchers can focus on the most valued regions in the dataset. Then, we develop a hybrid VAE-multilayer perceptron (MLP) neural network to realize the processing-structure mapping of iPP films. The robustness of the hybrid VAE-MLP network is verified. Finally, SAXS patterns in the temperature-strain space are generated, which allows us to explore the processing parameter space not involved by previous experiments. These capabilities indicate that the developed machine-learning methods are valuable artificial intelligence toolset to assist in the preprocessing of large-scale SAXS datasets and the establishment of comprehensive processing-structure relationship of hard-elastic iPP films.

Crystalline morphology formation in phase-field simulations of binary mixtures

With the aim of identifying process–structure relationships for solution-processed photovoltaics, a multiphysics modelling framework is employed to systematically investigate morphology formation in complex material mixtures.

XANES Studies of Zinc Tin Oxide Films Deposited by Atomic Layer Deposition: Revealing Process-Structure Relationships for Amorphous Oxide Semiconductors

Amorphous oxide semiconductors have grown in technological relevance in displays, information technology, energy, and catalysis. Recent research efforts have converged on the need to develop design principles for the electronic structure of these materials. Here, we systematically vary the cation composition and annealing temperatures of zinc tin oxide films using atomic layer deposition (ALD) and study their process-structure relationships with synchrotron X-ray absorption near-edge spectroscopy (XANES). Measurements of the O K-edge, Sn M-edge, and Zn L-edge are analyzed with ab initio and nonlinear statistical modeling to understand the changes in geometric and electronic structure of the films. A key finding from this approach is the ability to measure the changes in the relative contribution of the Zn and Sn s orbitals to the density of states near the conduction band minimum. Furthermore, we identify and delineate critical process-structure design principles when working with amorphous oxide semiconductor (AOS) material systems: (1) use of complementary diffraction and absorption spectroscopy techniques to characterize the as-deposited coordination environment; (2) accounting for gradual and abrupt changes in coordination environment as a function of processing parameters (stoichiometry, annealing, etc.); (3) revealing and exploiting the relevant knowledge and interdependence of coordination environments, orbital hybridizations, the density of states, and band structures. In the future, this multimodal X-ray analysis and modeling framework can be applied to understand the process-structure relationships needed to optimize AOS performance in devices.

Formation of Crystalline Bulk Heterojunctions in Organic Solar Cells: Insights from Phase-Field Simulations.

The performance of organic solar cells strongly depends on the bulk-heterojunction (BHJ) morphology of the photoactive layer. This BHJ forms during the drying of the wet-deposited solution, because of physical processes such as crystallization and/or liquid-liquid phase separation (LLPS). However, the process-structure relationship remains insufficiently understood. In this work, a recently developed, coupled phase-field-fluid mechanics framework is used to simulate the BHJ formation upon drying. For the first time, this allows to investigate the interplay between all the relevant physical processes (evaporation, crystal nucleation and growth, liquid demixing, composition-dependent kinetic properties), within a single coherent theoretical framework. Simulations for the model system P3HT-PCBM are presented. The comparison with previously reported in situ characterization of the drying structure is very convincing: The morphology formation pathways, crystallization kinetics, and final morphology are in line with experimental results. The final BHJ morphology is a subtle mixture of pure crystalline donor and acceptor phases, pure and mixed amorphous domains, which depends on the process parameters and material properties. The expected benefit of such an approach is to identify physical design rules for ink formulation and processing conditions to optimize the cell's performance. It could be applied to recent organic material systems in the future.

Kinetics and evolution of solid-state metal dealloying in thin films with multimodal analysis

Thin-film solid-state metal dealloying (thin-film SSMD) is an emerging technique that uses self-organization to design nanostructured thin films. The resulting 3D bicontinuous nanostructures are promising for a wide range of applications, such as catalysis and energy storage. In this work, we prepared thin films by SSMD using Ti-Cu as the parent alloy and Mg as the solvent. Using a multimodal approach, we combined synchrotron X-ray spectroscopy, diffraction, and high-resolution electron-based spectroscopy and imaging to study their morphological, structural, and chemical evolution. The processing-structure relationship was analyzed as a function of parent alloy composition and dealloying temperature and time. Morphological transitions from globular, to lamellar, to bicontinuous structures, in conjunction with a ligament size evolution, were identified as functions of the parent alloy composition. The dealloying rate increased with increasing concentration of interdiffusing elements (dissolving component) in the parent alloy. The parting limit, a dealloying compositional threshold, was systematically analyzed and determined to be 30%–40%. The order of crystalline phase formation is CuMg2, Cu2Mg, and Ti; the Ti phase first shows self-reorganization during dealloying, separate from the crystallization process. The coarsening in thin-film SSMD was identified and not entirely self-similar; in addition to the increase of ligament size over time, the formation of larger globular ligaments were also observed. This work furthers our fundamental understanding of thin-film SSMD and nanostructured thin-film design, where the thermodynamic and kinetic effects differ from the bulk counterparts. The fact that dealloying and diffusion outpaces the crystallization and new phase formation also offers opportunities to utilize thin-film SSMD in certain alloy systems in which deleterious intermetallic phases need to be suppressed, that may not be possible in the bulk geometry.

Metal Additive Manufacturing Process Design based on Physics Constrained Neural Networks and Multi-Objective Bayesian Optimization

The high-dimensionality of the process-structure-property (P-S-P) relationships for metal additive manufacturing (AM) is the major research challenge for process optimization to produce defect-free, structurally sound, and reliable AM parts. The goal of this work is to investigate the feasibility of a hybrid physics-based data-driven process design framework to establish reliable surrogates of process-structure relationships for metal AM process optimization. The process design framework includes a mesoscale multiphysics simulation model to predict microstructure evolution, a physics-constrained neural network to construct the surrogate of the process-structure relationship, and Bayesian optimization for process design. The proposed framework is demonstrated by optimizing the initial temperature and cooling rate for the single dendritic growth of Ti-6Al-4V alloy during rapid solidification in metal AM so that the desired dendritic area and microsegregation level can be achieved. The multi-objective optimization problem is solved with single-objective Bayesian optimization by aggregation. The effects of the aggregation weights on the optimization results are investigated with sensitivity analysis. The results show that the weights of individual objective functions determined from design preferences can help the designer to produce desired microstructure. The Pareto front and processing map for dendritic growth are constructed with the multi-objective Bayesian optimization method. The proposed process design framework is generic and can be potentially applied in materials design and digital twins of metal AM.

On the microstructure and texture evolution in 17-4 PH stainless steel during laser powder bed fusion: Towards textural design

Additive Manufacturing (AM) of metals allows the production of parts with complex designs, offering advanced properties if the evolution of the texture can be controlled. 17-4 precipitation hardening (PH) stainless steel is a high strength, high corrosion resistance alloy used in a range of industries suitable for AM, such as aerospace and marine. Despite 17-4 PH being one of the most common steels for AM, there are still gaps in the understanding of its AM processing–structure relationships. These include the nature of the matrix phase, as well as the development of texture through AM builds under different processing conditions. We have investigated how changing the laser power and scanning strategy affects the microstructure of 17-4 PH during laser powder bed fusion. It is revealed that the matrix phase is δ-ferrite with a limited austenite presence, mainly in regions of the microstructure immediately below melt pools. Austenite fraction is independent of the printing pattern and laser power. However, reducing the time between adjacent laser passes during printing results in an increase in the austenite volume fraction. Another effect of the higher laser power, as well as additional remelting within the printing strategy, is an increase in the average grain size by epitaxial ferrite grain growth across multiple build layers and the development of a mosaic type microstructure. Changes to the scanning strategy have significant impacts on the textures observed along the build direction, while 〈100〉 texture along the scanning direction is observed consistently. Mechanisms for texture formation and the mosaic structure are proposed that presents a pathway to the design of texture via AM process control.

Scan strategies in EBM-printed IN718 and the physics of bulk 3D microstructure development

Three-dimensional (3D) characterization provides opportunities for understanding processing-structure relationships in additively manufactured (AM) materials. Bulk samples of Inconel 718 were fabricated via electron beam melting (EBM) in order to study microstructural development as a function of energy input and beam scan strategy. TriBeam tomography of bulk Inconel 718 microstructures built under steady-state growth conditions reveals the sensitivity of microstructure formation and evolution to machine process parameters. In this study, samples manufactured using a narrow range of energy input per unit build area result in varied grain morphologies and crystallographic textures. Using TRUCHAS, a thermal simulation software, the thermal history of bulk scan strategies was predicted, and combined with a calibrated microstructure-processing map to accurately predict bulk grain morphologies. The solidification parameters and the 3D measured nucleation density are used to predict the transition between columnar and equiaxed grain morphologies, providing a process map to guide AM parameter choices to locally control as-printed microstructure. A two-dimensional metric for characterizing bulk grain morphology was also found to agree well with predictions from the process map calibrated by 3D data. Combined with 3D tomography and thermal modelling, the physics of structure development were understood at a new level of detail with respect to the competing processes of grain nucleation and epitaxial growth.

Phase‐Field Simulations of the Morphology Formation in Evaporating Crystalline Multicomponent Films

AbstractIn numerous solution‐processed thin films, a complex morphology resulting from liquid–liquid phase separation (LLPS) or from polycrystallization arises during the drying or subsequent processing steps. The morphology has a strong influence on the performance of the final device but unfortunately, the process–structure relationship is often poorly and only qualitatively understood. This is because many different physical mechanisms (miscibility, evaporation, crystallization, diffusion, and advection) are active at potentially different time scales and because the kinetics plays a crucial role: the morphology develops until it is kinetically quenched far from equilibrium. In order to unravel the various possible structure formation pathways, a unified theoretical framework that takes into account all these physical phenomena is proposed. This phase‐field simulation tool is based on the Cahn–Hilliard equations for diffusion and the Allen–Cahn equation for crystallization and evaporation, which are coupled to the equations for the dynamics of the fluid. The behavior of the coupled model based on simple test cases is discussed and verified. Furthermore, how this framework allows to investigate the morphology formation in a drying film undergoing evaporation‐induced LLPS and crystallization, which is typically a situation encountered, is illustrated, for example, in organic photovoltaics applications.

Learning time-dependent deposition protocols to design thin films via genetic algorithms

Designing next generation thin films tailor-made for specific applications relies on the availability of robust process-structure-property relationships. Traditional structure zone diagrams that relate one or two deposition conditions to microstructures are limited to simple mappings, with machine-learning methods only recently attempting to relate multiple processing parameters to the final microstructure. Despite this progress, process-structure relationships are unknown for deposition conditions that vary during thin-film deposition, limiting the range of achievable microstructures and properties. We combine phase-field simulations with a genetic algorithm to identify and design time-dependent deposition protocols that achieve tailor-made microstructures. We simulate the physical vapor deposition of a binary-alloy thin film by employing a phase-field model, where deposition rates and diffusivities of the deposited species vary in time and are controlled via the genetic algorithm. Our genetic-algorithm-guided protocols achieve targeted microstructures with lateral and vertical concentration modulations, as well as more complex, hierarchical microstructures previously not described in classical structure zone diagrams. By elucidating the process-structure mechanisms during physical vapor deposition and using this knowledge to achieve precise thin-film microstructures, our algorithm provides insights to the thin film, physical vapor deposition, and film functionality communities looking for additional avenues to design novel thin-film microstructures.

Data-driven concurrent nanostructure optimization based on conditional generative adversarial networks

Abstract Iterative numerical optimization is a ubiquitous tool to design optical nanostructures. However, there can be a significant performance gap between the numerically simulated results, with pristine shapes, and the experimentally measured values, with deformed profiles. We introduce conditional generative adversarial networks (CGAN) into the standard iterative optimization loop to learn process-structure relationships and produce realistic simulation designs based on the fabrication conditions. This ensures that the process-structure mapping is accurate for the specific available equipment and moves the optimization space from the structural parameters (e.g. width, height, and period) to process parameters (e.g. deposition rate and annealing time). We demonstrate this model agnostic optimization platform on the design of a red, green, and blue color filter based on metallic gratings. The generative network can learn complex M-to-N nonlinear process-structure relations, thereby generating simulation profiles similar to the training data over a wide range of fabrication conditions. The CGAN-based optimization resulted in fabrication parameters leading to a realistic design with a higher figure of merit than a standard optimization using pristine structures. This data-driven approach can expedite the design process both by limiting the design search space to a fabrication-accurate subspace and by returning the optimal process parameters automatically upon obtaining the optimal structure design.

Electroless Dealloying of Thin-Film Nanocrystalline Au-Ag Alloys: Mechanisms of Ligament Nucleation and Sources of Its Synthesis Variability.

Control of ligament size in nanoporous gold through process inputs in chemical dealloying holds the potential to exploit its size dependent properties in applications in energy and biomedicine. While its morphology evolution is regulated by the kinetics of coarsening, recent studies are focused on the early stage of dealloying (e.g., ∼ 5-42 at. % in residual alloy content) to understand mechanisms of ligament nucleation and its role in altering process-structure relationships. This paper examines this stage in chemical dealloying of nanocrystalline Au49Ag51 thin films and finds that ligaments are nucleated uniformly through its thickness due to the dealloying front rapidly propagating through the thickness of the film. Further, through the establishment of process-structure relationships with large data sets (i.e., 80 samples), this paper quantifies sources of variability that alter the kinetics of ligament growth such as aging of the precursor (e.g., grain growth) and solution evaporation. It is found that ligament diameter is better predicted by the residual silver content rather than by the dealloying time even amidst both effects and independent control of ligament diameter and solid area fraction is demonstrated within a limited window.

Printability of Zr41.2Ti13.8Cu12.5Ni10.0Be22.5 metallic glass on steel by laser additive manufacturing: A single-track study

The amorphous structure of metallic glasses (MGs) endows them with extraordinary properties. Formation of MGs requires sufficiently high cooling rates to bypass crystallization, which results in their limited sizes by conventional processing routes such as casting. Laser additive manufacturing (LAM) technique is featured by high solidification rate that provides the potential for scalable fabrication of MGs. In this work, through high throughput laser single-track melting experiments, we studied the LAM processing window of a Zr41.2Ti13.8Cu12.5Ni10.0Be22.5 (at.%) MG to achieve materials with near-full density and minimal crystallization. We observed different types of single-track features including cracking, lack-of-fusion, and partial crystallization under different combinations of laser processing parameters. To rationalize the processing-structure relationships during LAM of MGs, finite-element thermal modeling was performed to monitor the transient temperature evolution and site-specific cooling rate in the melt pool. Our work provides significant insight into the LAM protocol optimization towards fully amorphous, well-bonded, and dense MG coatings on dissimilar crystalline materials.