The objective of quantitative metallography/stereology is to describe the geometric characteristics of the features. This article discusses the geometric attributes of microstructural features that can be divided into: the numerical extents and the number density of microstructural features; derived microstructural properties; feature specific size, shape, and orientation distributions; and descriptors of microstructural spatial clustering and correlations. It emphasizes on the practical aspects of the measurement techniques and applications. The article also provides information on the quantitative metallographic methods for estimation of volume fraction, total surface area per unit volume, and total length of per unit volume.

Primary silicon in hypereutectic aluminum-silicon alloys is very hard, not only imparting improved wear resistance but also decreasing tool life during machining. This article discusses the importance of primary silicon refinement and the process of accomplishing primary silicon refinement.

This article describes the results obtained by Volmer, Weber, Farkas, Becker, and Doring, which constitute the classical nucleation theory. These results are the predictions of the precipitate size distribution, steady-state nucleation rate, and incubation time. The article reviews a nucleating system as a homogeneous phase using the classical nucleation theory, along with heterophase fluctuations that led to the formation of precipitates. It discusses the gas cluster dynamics using the kinetic approach to describe nucleation. The article presents key parameters, such as cluster condensation and evaporation rates, to describe the time evolution of the system. The predictions and extensions of the classical nucleation theory are discussed. The article also provides the limitations of classical nucleation theories in cluster dynamics.

This article describes the operation and capabilities of surface analysis methods of metals, including scanning electron microscopy, electron probe microanalysis, transmission electron microscopy, secondary ion mass spectroscopy, and X-ray photoelectron spectroscopy. It provides information on the capabilities, typical uses, spatial resolution, elemental analysis detection threshold and precision, limitations, sample requirements, and operating principles of the scanning auger microprobe.

Process optimization is the discipline of adjusting a process to optimize a specified set of parameters without violating engineering constraints. This article reviews data-driven optimization methods based on genetic algorithms and stochastic models and demonstrates their use in powder-bed fusion and directed energy deposition processes. In the latter case, closed-loop feedback is used to control melt pool temperature and cooling rate in order to achieve desired microstructure.

The use of additive manufacturing (AM) is increasing for high-value, critical applications across a range of disparate industries. This article presents a discussion of high-valued engineering components predominantly used in the aerospace and medical industries. Applications involving metal AM, including methods to identify pores and voids in AM materials, are the focus. The article reviews flaw formation in laser-based powder-bed fusion, summarizes sensors used for in situ process monitoring, and outlines advances made with in situ process-monitoring data to detect AM process flaws. It reviews investigations of ML-based strategies, identifies challenges and research opportunities, and presents strategies for assessing anomaly detection performance.

This article examines how cellular automaton (CA) can be applied to the simulation of static and dynamic recrystallization. It describes the steps involved in the CA simulation of recrystallization. These include defining the CA framework, generating the initial microstructure, distributing nuclei of recrystallized grains, growing the recrystallized grains, and updating the dislocation density. The article concludes with information on the developments in CA simulations.

This article describes the integration of thermodynamic modeling, mobility database, and phase-transformation crystallography into phase-field modeling and its combination with transformation texture modeling to predict phase equilibrium, phase transformation, microstructure evolution, and transformation texture development during heat treatment of multicomponent alpha/beta and beta titanium alloys. It includes quantitative description of Burgers orientation relationship and path, discussion of lattice correspondence between the alpha and beta phases, and determination of the total number of Burgers correspondence variants and orientation variants. The article also includes calculation of the transformation strain with contributions from defect structures developed at alpha/beta interfaces as a precipitates grow in size. In the CALculation of PHAse Diagram (CALPHAD) framework, the Gibbs free energies and atomic mobilities are established as functions of temperature, pressure, and composition and serve directly as key inputs of any microstructure modeling. The article presents examples of the integrated computation tool set in simulating microstructural evolution.

This article discusses the fundamental aspects of phase-field microstructure modeling. It describes the evolution of microstructure modeling, including nucleation, growth, and coarsening. The article reviews two approaches used in the modeling nucleation of microstructure: the Langevin force approach and explicit nucleation algorithm. Calculation of activation energy and critical nucleus configuration is discussed. The article presents the deterministic phase-field kinetic equations for modeling growth and coarsening of microstructure. It also describes the material-specific model inputs, chemical free energy and kinetic coefficients, for phase-field microstructure modeling. The article provides four examples that illustrate some aspects of phase-field modeling.

This article discusses selected highlights of thermodynamic relationships during solidification and nucleation kinetics behavior in connection with the basis of nucleation treatments, such as grain refinement and inoculation, to provide a summary of nucleation phenomena during casting. The article describes nucleation phenomenon such as homogeneous nucleation and heterogeneous nucleation. It examines various grain refinement models, such as the carbide-boride model, the free growth model, and the constitutional undercooling model. The article concludes with information on the thermal analysis techniques for assessing grain-refining characteristics during master alloy processing.

Discontinuously reinforced aluminum (DRA) alloy metal-matrix composites (MMCs) represent an advanced aluminum materials concept whereby ceramic particles, or whiskers, are added to aluminum-base alloys through the use of either ingot-melting or casting and/or powder-metallurgy (P/M) techniques. This article begins with a summary of general observations on the forging of discontinuously reinforced composites. It provides information on some of the specific experimental results obtained on various DRA systems, including 2xxx DRA alloys and cast DRA alloys. The article reviews the efforts on the modeling of behavior of specific alloy systems, with a comparison of experimental results to the modeling attempts. It concludes with information on the properties of deformation-processed DRA alloys.

This article focuses on the metallography and microstructures of wrought and cast aluminum and aluminum alloys. It describes the role of major alloying elements and their effect on phase formation and the morphologies of constituents formed by liquid-solid and/or solid-state transformations. The article also describes specimen preparation procedures and examines the microstructure of several alloy samples.

Three-dimensional microscopy can be used to reveal the shape, distribution, and connectivity of three-dimensional (3D) features that lie buried within an opaque material. This article discusses several experimental techniques that can be used to generate 3D images. These include serial sectioning, focused ion beam tomography, atom probe tomography, and X-ray microtomography. Nine case studies are presented that represent the work of the various research groups currently working on 3D microscopy using serial sectioning and illustrate the variants of the basic experimental techniques. The article also discusses the techniques for reconstruction and visualization of 3D microstructures with advanced computer software and hardware.

A computational tool would require the contribution of the strengthening mechanisms of metallic material to be predicted and then summed in an appropriate way to derive an estimate of the tensile properties. This article focuses on the modeling of deformation mechanisms pertinent to structural materials, namely, solid-solution strengthening, age/precipitation hardening, dispersion strengthening, grain size reduction, strengthening from cold work, and strengthening from interfaces. It explains the application of predictive models in the atomistic modeling of dislocation structures and cast aluminum property prediction. The article concludes with information on the use of rules-based approaches and data-mining techniques for quantitative predictions of tensile properties.

This article discusses various concepts of micro x-ray diffraction (XRD) used for the examination of materials in situ. The discussion covers the principles, equipment used, sample preparation procedure, considerations for calibrating a detector, steps for performing data analysis, and applications and interpretation of micro-XRD.

This article presents the analytics challenges in additive manufacturing. It discusses the types and applications of data analytics. Data analytics can be classified into four types: descriptive, diagnostic, predictive, and prescriptive. The diverse applications of data analytics and machine learning include design, process-structure-properties (PSP) relationships, and process monitoring and quality control. The article also presents tools used for data analytics.

Additive manufacturing (AM) provides exceptional design flexibility, enabling the manufacture of parts with shapes and functions not viable with traditional manufacturing processes. The two paradigms aiming to leverage computational methods to design AM parts imbuing the design-for-additive-manufacturing (DFAM) principles are design optimization (DO) and simulation-driven design (SDD). In line with the adoption of AM processes by industry and extensive research efforts in the research community, this article focuses on powder-bed fusion for metal AM and material extrusion for polymer AM. It includes detailed sections on SDD and DO as well as three case studies on the adoption of SDD, DO, and artificial-intelligence-based DFAM in real-life engineering applications, highlighting the benefits of these methods for the wider adoption of AM in the manufacturing industry.

Analytical transmission electron microscopy (ATEM) is unique among materials characterization techniques as it enables essentially the simultaneous examination of microstructural features through high-resolution imaging and the acquisition of chemical and crystallographic information from small regions of the specimen. This article illustrates the effectiveness of the technique in solving materials problems. The first section of the article provides information on analytical electron microscope (AEM) and its basic operational characteristics as well as on electron optics, electron beam/specimen interactions and the generation of a signal, signal detectors, electron diffraction, imaging, x-ray microanalysis, electron energy loss spectroscopy, and sample preparation. The second section consists of 12 examples, each illustrating a specific type of materials problem that can be solved, at least in part, with AEM.

This article focuses on the principles and applications of high-sputter-rate dynamic secondary ion mass spectroscopy (SIMS) for depth profiling and bulk impurity analysis. It begins with an overview of various factors pertinent to sputtering. This is followed by a discussion on the effects of ion implantation and electronic excitation on the charge of the sputtered species. The design and operation of the various instrumental components of SIMS is then reviewed. Details on a depth-profiling analysis of SIMS, the quantitative analysis of SIMS data, and the static mode of operation of time-of-flight SIMS are covered. Instrumental features required for secondary ion imaging are presented and the differences between quadrupole and high-resolution magnetic mass filters are described. The article also reviews the optimum method for analysis of nonmetallic samples and high detection sensitivity of SIMS. It ends with a discussion on a variety of examples of SIMS applications.

This article focuses specifically on material modeling applied to structure-property predictions. It provides general guidelines and considerations in terms of modeling the salient material features that ultimately impact the mechanical performance of parts produced by additive manufacturing (AM). Two of the primary ingredients needed to predict structure-property relationships via material modeling include a geometrical representation of the microstructural features of interest (e.g., grain structure and void defects) and a suitable constitutive model describing the material behavior, both of which can be scale and resource dependent. The article also presents modeling challenges to predict various aspects of (process-) structure-property relationships in AM.