Process Structure Property Relationships Research Articles

Simultaneous enhancement of build rate and microstructure in SS316L additive manufacturing through in-situ laser remelting

The build quality in Laser-Directed Energy Deposition (L-DED) is crucial for industrial production but is often compromised at higher build rates. Higher deposition rates in L-DED typically lead to poor surface finish, high porosity, and solute segregation at grain boundaries. This study investigates in-situ laser remelting with positive defocus to enable a substantially higher deposition rate, i.e., 25 g/min compared to the preferred 7 g/min, in building SS316L components using the powder-based L-DED technique while maintaining the desired build quality. It is shown that laser remelting significantly improves surface finish, reduces porosity by up to 83%, increases microhardness by up to 34%, and eliminates solute segregation compared to the as-deposited case. The effect of specific laser remelting parameters and underlying mechanisms are further investigated via specially designed experiments. The results indicate that employing the highest linear energy density through maximum laser power (2000 W) and minimum scan speed (400 mm/min) yields minimum porosity. On the other hand, employing the lowest linear energy density through minimum laser power (500 W) and maximum scan speed (1000 mm/min) yields the highest microhardness due to grain refinement. An analytical model is utilized to provide insights into the process-structure–property relationship based on experimental measurements of cooling rate, grain size, and microhardness. In conclusion, this research demonstrates the potential of in-situ laser remelting to significantly enhance both build rate and quality in L-DED, offering a promising approach for large-scale part production through periodic laser melting during deposition.

Effect of Laser Powder Bed Fusion Parameters on the Evolution of Melt Pool, Densification, Microstructure, and Hardness in 420 Stainless Steel Parts

Laser powder bed fusion (LPBF) involves depositing, melting, and solidifying metal powder particles layer by layer to create 3D components. In this study, a deep fundamental understanding on how process parameters—laser power, scan speed, and hatch spacing—affect the melt pool, densification, microstructure, hardness, and thermal behavior of 420 stainless steel (420SS) parts produced by such technology is provided. The conducted investigation considers five levels of laser power and hatch spacing, and four scan speeds. Optimal single tracks, based on geometry and profile, are achieved with laser powers between 40 and 80 W and a scan speed of 10 mm s−1. In the multitrack analysis, it is indicated that a dense, smooth surface is obtained with a hatch spacing of 250 μm, corresponding to an overlapping rate of ≈30%. The 420SS samples show high densification (≈99%) and low surface roughness (≈3.62 μm). The microstructure consisted of martensite laths and retained austenite. The hardness and thermal conductivity of the samples are measured at 540 HV and 15.3 W m−1 K−1, respectively. In this study, the understanding of the process–structure–property relationships in LPBF of 420SS is expanded.

A microscale cellular automaton method for solid-state phase transformation of directed energy deposited Ti6Al4V

Directed energy deposition is a promising additive manufacturing technology that fabricates complex geometries by fusing feed material layer-by-layer. However, the formation mechanism of the directed energy deposited Ti6Al4V solid-state phase transformation process, which is crucial for understanding the process-structure–property relationship, has remained unclear. In this study, a microscale cellular automaton method is proposed to simulate the microstructure evolution process for Ti6Al4V, specifically the β→α/α′ phase transformation process within a few β grains. The method is further integrated with the mesoscale cellular automaton method, which predicts the prior β grain structure, the solid-state phase transformation kinetics model for the prediction of the phase volume fractions, and the finite volume method, which is used for the thermal-fluid flow modeling, providing a temperature field to the former. The integrated numerical framework not only links the thermal history with the phase volume fractions but also provides the columnar β grain structures and acicular α/α′ grain structures in satisfying agreement with the available experimental observation. Moreover, the predictions shed some light on the formation mechanism of the hierarchical α/α′ structure and their particular clusters. The influence of the cooling rate on the α/α′ grain formation is illustrated via the three-layer case simulation. The findings on the formation mechanism of α/α′ are beneficial in tailoring the microstructure of Ti6Al4V for excellent mechanical properties in directed energy deposited Ti6Al4V.

Contrastive learning based on hierarchical graph of microstructures through directed energy deposition process to establish process-structure–property relationship via autoencoder

n this study, an innovative algorithm is developed to transform microstructures to hierarchical graphs without manual feature engineering. The hierarchical graph comprises two layers. Initially, pixel data, which includes Euler angles, phase, and position, forms the pixel-wise graphs within individual grains. Following this foundation, the grains serve as nodes, constituting the second layer. Importantly, the hierarchical graph preserves essential measurement data and structural details for training machine learning models. After that, a contrastive learning model based on hierarchical graph is designed to capture representations of microstructures obtained by the electron backscatter diffraction (EBSD) technique. This model can be directly extended for other microscopy techniques, such as Scanning Electron Microscopy (SEM), Energy-Dispersive X-ray spectroscopy (EDX), and Transmission Electron Microscopy (TEM). Using the learned microstructure representations, an autoencoder model for the directed energy deposition (DED) is developed to establish the relationship among process parameters, microstructures, and material properties, completing the process-structure–property cycle quantitatively. The performance of the present model is naturally benchmarked against two other models: a contrastive learning model based on pixel-wise graph (without manual feature engineering) and a contrastive learning model based on grain-wise graph (employing manual feature engineering). The results of the present model highlight the potential in decoding the process-structure–property relationships in the DED process.

Evolution of fundamental mechanical properties with aliovalent Co/Cu partial substitution and preparation method for Y-123 system

This study investigates the effect of aliovalent Co/Cu replacement and preparation method on fundamental mechanical performance features of YBa2Cu3−xCoxO7−δ (Y-123) ceramic system depending on the crack propagation mechanism by Vickers hardness measurements (Hv) and mechanical investigation models for the first time. All the findings are verified by the scanning electron microscopy (SEM) examinations. Besides, the electron-dispersive X-ray (EDX) technique verifies the successful substitution mechanism. Besides, the Vickers hardness parameters improve systematically with the increment in the Co/Cu partial substitution (serving as a barrier) level due to formation of operable slip systems, ionic bond formations, and decrement of stress-amplified strain fields. Moreover, the Y-123 ceramic produced by solid-state reaction method and molecular weight of 0.20% presents the densest and smoothest surface morphology with the largest particle distributions and well-linked cobblestone-like grains. On the other hand, the Y-123 ceramic compounds produced by the sol–gel method are more sensitive and responsive to the indentation test loads. All the findings are wholly supported by the mechanical performance properties, including the shear modulus, resilience, and degree of granularity. Furthermore, the mechanical models indicate that every compound prepared exhibits the untypical reverse indentation size effect (RISE). Additionally, the modeling studies display that the induced cracking (IIC) approach is found to be the most appropriate method to examine true Vickers hardness parameters in the plateau limit regions. All in all, this comprehensive study reports efficiently exploiting the process–structure–property relationships in Y-123 ceramic material design for physical science and mechanical application fields using the aliovalent partial substitution and preparation condition.

Advancing neodymium permanent magnets with laser powder bed fusion technology: a comprehensive review of process–structure–property relationship

This comprehensive review delves into the critical relationship between process, structure, and properties in the context of manufacturing neodymium (NdFeB) permanent magnets using laser powder bed fusion (LPBF) technology.

Learning material synthesis–process–structure–property relationship by data fusion: Bayesian co-regionalization N-dimensional piecewise function learning

Autonomous materials research labs require the ability to combine and learn from diverse data streams.

Shear Piezoelectricity of Poly(l-lactide) Films Manufactured by Extrusion–Orientation: An Insight on Process–Structure–Property Relationships

Shear Piezoelectricity of Poly(<scp>l</scp>-lactide) Films Manufactured by Extrusion–Orientation: An Insight on Process–Structure–Property Relationships

Optimizing the synthesis process for Lithium-Ion sieve adsorbents: Effect of calcination temperature and heating rate on reaction efficiency and performance

Processing-structure–property relationships play a crucial role in tuning the performance of materials for a given application in order to attain a suitable set of optimal conditions, and it is therefore imperative to evaluate the parameters of either the synthesis, delithiation, adsorption, and desorption process. Through an orthogonal test design of the solid-state synthesis process, it was determined that a heating rate of 1 ℃/min had consistently higher reaction efficiencies of 68.1 %, 68.6 %, and 72.3 % at the calcination temperatures of 650 ℃, 700 ℃, and 750 ℃ respectively compared to heating rates of 4 ℃/min and 7 ℃/min. Moreover, a decrease in delithiation efficiency with increase in calcination temperature (99 % at 650 ℃, 96.1 % at 700 ℃, and 94.2 % at 750 ℃) at the 1 ℃/min heating rate was observed which was attributed to an increase in the average crystallite size. Adsorption performance of the synthesis-optimized zinc-doped lithium metatitanate showed consistently high adsorption efficiency (>90 %) despite the low adsorption capacity (4.9 mg/g) which was attributed to the low initial lithium-ion concentration of the lithium chloride solution and high adsorbent dosage.

Experimental-based statistical models for the tensile characterization of synthetic fiber ropes: a machine learning approach

This study investigated the tensile behavior of some prevalent synthetic fiber ropes made of polyester, polypropylene, and nylon polymeric fibers. The aim was to generate well-documented experimental statistics and develop simplified stress–strain constitutive laws that can describe the ropes' tensile response. The methodology involved analyzing the thermal history of the fibers using the DSC technique, tensile testing of fibers and yarn components of the rope, and conducting 196 rope tensile tests with optimum testing conditions. Based on the test results, an experimental database of the ropes' tensile characteristics was established, containing different parameters of material properties, rope construction, fiber processing, fiber tensile properties, and rope tensile responses. Subsequently, ANN models were developed and optimized using MATLAB based on the generated dataset's inputs and outputs to predict the studied ropes' tri-linear stress–strain profiles. The results showed that the ANN models accurately predicted the stress–strain properties of ropes represented by the tri-linear approximation with an error of about 5% for the failure strength and strain. The study provides insight into the process-structure–property relationship of synthetic fiber ropes and contributes to minimizing the cost and effort in designing and predicting their tensile properties while contributing to the practical industry.

Multiscale Fast Fourier Transform homogenization of additively manufactured fiber reinforced composites from component-wise description of morphology

The process-structure–property relationship in short-fiber reinforced composites made in Fused Filament Fabrication (FFF) remains inadequately understood, much trial-and-error and extensive testing is required to use these materials for load-bearing applications. As a consequence, the largely empirical design process has hindered the adoption of this technology, notably due to the lack of reliable structural analysis capability. In order to surpass the limitations of mechanical property prediction using simplified artificial microstructures, this work demonstrates the decisive advantage of using geometries obtained directly from imaging of printed specimen instead. The analysis of μCT images is performed via a purpose-built extraction tool called OpenFiberSeg, yielding profound insight into the process-structure relation. The use of real microstructures is shown to considerably improve the mechanical behavior prediction capability via dual-scale FFT-based homogenization, bringing relative error margins below 5%, for full anisotropic description. It also becomes possible to investigate the effect of processing parameters such as nozzle diameter and printing pattern on morphological properties and on mechanical behavior, revealing the magnitude of the spatial variation of local properties. The combination of experimental and simulation enables insight that is not accessible to either alone. Original imaging data and source code are made publicly available.

Review of Process–Structure–Property Relationships in Metals Fabricated Using Binder Jet Additive Manufacturing

Review of Process–Structure–Property Relationships in Metals Fabricated Using Binder Jet Additive Manufacturing

Geometrical and crystal plasticity modelling: Towards the establishment of a process-structure-property relationship for additively manufactured 316L struts

This paper presents a methodology to establish a process structure property (PSP) relationship for the additive manufacturing (AM) of thin AISI 316L struts, as might be used in coronary stent applications. The methodology is based on a new geometrically based process-structure method for AM process variables, and crystal plasticity finite element (CPFE) modelling that includes the representation of melt pool microstructure morphology and texture. The effects of AM process variables are characterised with respect to ductility, yield strength and UTS across a range of process variables, including hatch spacing, layer thickness and build orientation (two orientations are considered: horizontal and vertical). The CPFE-predicted effect of build orientation is shown to be consistent with experimental test results. The present methodology has allowed identification of optimal hatch spacing and layer thickness for a given laser spot size, power and scanning speed. CPFE modelling of AM melt pool texture is shown to be key to successful prediction of the effects of process variables on mechanical behaviour.

Towards inverse microstructure-centered materials design using generative phase-field modeling and deep variational autoencoders

The field of Integrated Computational Materials Engineering (ICME) combines a broad range of methods to study materials’ responses over a spectrum of length scales. A relatively unexplored aspect of microstructure-sensitive materials design is uncertainty propagation and quantification (UP/UQ) of materials’ microstructure, as well as establishing process-structure–property (PSP) relationships for inverse material design. In this study, an efficient UP technique built on the idea of changing probability measures and a deep generative unsupervised representative machine learning method for microstructure-based design of thermal conductivity of materials is proposed. Probability measures are used to represent microstructure space, and Wasserstein metrics are used to test the efficiency of the UP method. By using deep Variational AutoEncoder (VAE), we identify the correlations between the material/process parameters and the thermal conductivity of heterogeneous dual-phase microstructures. Through high-throughput screening, UP, and the deep-generative VAE method, PSP relationships that are too complex can be revealed by exploiting the materials’ design space with an emphasis on microstructures. As a last point, we demonstrate generative machine learning serves as a useful tool for inverse microstructure-centered materials design, and we demonstrate this by examining the inverse design of thermal conductivity in nano-structured materials. The results reveal the effects of morphology, volume fraction, characteristic length scale, and the individual thermal diffusivity of phases on the thermal conductivity of dual-phase alloys. Our findings emphasize the advantages of high-throughput phase-field modeling and generative deep learning for linking PSP and inverse microstructure-centered materials design.

FFF-based metal and ceramic additive manufacturing: Production scale-up from a stream of variation analysis perspective

Metal and ceramic fused filament fabrication (FFF) has been increasingly used additive manufacturing (AM) processes in the various applications including product prototyping and rapid tooling. Due to its high flexibility in both material composition and part design fabrication, metal and ceramic FFF has shown significant potential in fabricating various functional structures with specific properties of interest, such as mechanical property and thermal conductivity. Moreover, those FFF processes have also demonstrated phenomenal advantages over the other AM technologies, including its cost and energy efficiency. However, the quality control for these processes is still in its infancy, which hinders their broader adoption in more mission critical applications. Therefore, there is an urgent need in understanding the process-structure–property relationships in the metal and ceramic FFF. Both metal and ceramic FFF processes are generally composed of three major steps, i.e., materials preparation, FFF, and post-treatment. In this paper, the state-of-the-art studies on metal and ceramic FFF has been examined from a stream of variation (SoV) analysis perspective, where the variation sources introduced in each step are investigated and the metrological methods to characterize those variation are summarized. Furthermore, research opportunities and challenges for the quality control for the production scale-up of the FFF-based metal and ceramic AM are discussed.

Copolymers for electronic, optical, and sensing applications with engineered physical properties

Electronic and optoelectronic devices often require multifunctional properties combined with conductivity that are not achieved from a single species of molecules. The capability to tune chain length, shape, and physicochemical characteristics of conductive copolymers provides substantial benefits for a wide range of scientific areas that require unique and engineered optical, electrical, or optoelectronic properties. Although efforts have been made to develop synthetic routes to realize such promising copolymers, an understanding of the process–structure–property relationship of the synthesis methods needs to be further enhanced. In addition, since traditional methods are often limited to achieving pinhole-free, large-area coverage, and conformal coating of copolymer films with thickness controllability, unconventional synthetic strategies to address these issues need to be established. This Perspective article intends to enhance knowledge on the process–structure–property relationship of functional copolymers by providing the definition of copolymers, polymerization mechanisms, and a comparison of traditional and emerging synthetic methods with reaction parameters and tuned physical properties. In parallel, practical applications featuring the desired copolymers in electronic, optical, and sensing devices are showcased. Last, a pathway toward further advancement of unique copolymers for next-generation device applications is discussed.

Accelerating Thermal Simulations in Additive Manufacturing by Training Physics-Informed Neural Networks With Randomly Synthesized Data

Abstract The temperature history of an additively manufactured part plays a critical role in determining process–structure–property relationships in fusion-based additive manufacturing (AM) processes. Therefore, fast thermal simulation methods are needed for a variety of AM tasks, from temperature history prediction for part design and process planning to in situ temperature monitoring and control during manufacturing. However, conventional numerical simulation methods fall short in satisfying the strict requirements of time efficiency in these applications due to the large space and time scales of the required multiscale simulation. While data-driven surrogate models are of interest for their rapid computation capabilities, the performance of these models relies on the size and quality of the training data, which is often prohibitively expensive to create. Physics-informed neural networks (PINNs) mitigate the need for large datasets by imposing physical principles during the training process. This work investigates the use of a PINN to predict the time-varying temperature distribution in a part during manufacturing with laser powder bed fusion (L-PBF). Notably, the use of the PINN in this study enables the model to be trained solely on randomly synthesized data. These training data are both inexpensive to obtain, and the presence of stochasticity in the dataset improves the generalizability of the trained model. Results show that the PINN model achieves higher accuracy than a comparable artificial neural network trained on labeled data. Further, the PINN model trained in this work maintains high accuracy in predicting temperature for laser path scanning strategies unseen in the training data.

A data-driven modeling approach to quantify morphology effects on transport properties in nanostructured NMC particles

We present a data-driven modeling approach to quantify morphology effects on transport properties in nanostructured materials. Our approach is based on the combination of stochastic modeling of the 3D nanostructure and numerical modeling of effective transport properties, which is used to investigate process-structure–property relationships of hierarchically structured cathode materials for lithium-ion batteries. We focus on nanostructured LiNi1/3Mn1/3Co1/3O2 (NMC) particles, the nanoporous morphology of which has a crucial impact on their effective transport properties (i.e, effective ionic and electric conductivity) and thus on the performance of the cell. First, we develop a parametric stochastic model for the 3D morphology of the nanostructured NMC particles based on excursion sets of so-called χ2-fields. This model, which has only two parameters, is then fitted to FIB-SEM image data of the NMC particles manufactured with different calcination temperatures and different particle sizes. This way it is possible to generate digital twins of the NMC particles. In a second step, measured 3D image data and corresponding digital twins are used as input for the numerical simulation of effective transport properties. Based on the results obtained by these simulations, we can quantify process-structure–property relationships. Overall, we present a methodological framework that allows for an efficient optimization of the fabrication process of nanostructured NMC particles.

Process-Structure-Property Relationships of Laser Powder Bed Fusion Lattice Structures

Abstract Lattice structure metamaterials offer a variety of unique and tailorable properties, yet industrial adoption is slowed by manufacturability and inspection-related difficulties. Despite recent advances in laser powder bed fusion additive manufacturing, the sub-millimeter features of lattices are at the edge of process capabilities and suffer from low geometric quality. To better understand their complex process-structure-property (PSP) relationships, octahedron structures were manufactured across a power spectrum, inspected, and mechanically tested. X-ray computed tomography was used to characterize lattice geometry, and demonstrated that lattice strut geometry measures, increased significantly as a function of laser power. Furthermore, lattices are shown to exhibit a direct correlation between laser power and mechanical performance metrics. Performance variations up to 60% are shown as a function of process parameters despite nominally identical geometry. Significant geometry variations are found to be the cause of performance variation, while material properties as measured by microindentation hardness are constant across the studied parameter range. PSP relationships are modeled, and the limitations of these models are explored. It was found that resulting models can predict mechanical performance based on geometric characteristics with R2 values of up to 0.86. Finally, mechanistic causes of observed performance changes are discussed.

Characterization of porous membranes using artificial neural networks

Porous membranes have been utilized intensively in a wide range of fields due to their special characteristics and a rigorous characterization of their microstructures is crucial for understanding their properties and improving the performance for target applications. A promising method for the quantitative analysis of porous structures leverages the physics-based generation of porous structures at the pore scale, which can be validated against real experimental microstructures, followed by building the process–structure–property relationships with data-driven algorithms such as artificial neural networks. In this study, a Variational AutoEncoder (VAE) neural network model is used to characterize the 3D structural information of porous materials and to represent them with low-dimensional latent variables, which further model the structure–property relationship and solve the inverse problem of process–structure linkage combined with the Bayesian optimization method. Our methods provide a quantitative way to learn structural descriptors in an unsupervised manner which can characterize porous microstructures robustly.