Computational fluid dynamics (CFD) provides an efficient, alternate, virtual approach for simulating and analyzing quenching processes with an impact on component design, manufacturing process, and quality. This article provides domain insights for quenching researchers and CFD practitioners for the modeling of the industrial quenching process and for supporting the diverse multifunctional needs in an industry, ranging from primary metallurgical companies (steel, aluminum, and other alloys), original equipment manufacturers, engineering companies, captive and commercial heat treating facilities, quench system manufacturers, and quench fluid suppliers. It describes the governing differential equations for the fluid flow and heat-transfer phenomena during quenching. The article also discusses different modeling categories to determine a CFD methodology for quenching.

Flow visualization is an important characterization process to not only understand uniformity of the interfacial cooling mechanisms, but also to characterize the overall impact of agitation on the uniformity of the overall cooling process. This article focuses on thermal flow simulation and visualization in the quenching process. The study presents the effect of bubbling, boiling, and breaking the steam film on the heat-transfer coefficient during the agitated quenching process.

Mathematical models have been used for over five decades in industrial heat-treating operations. Most of these modeling efforts have emanated from academia or research institutes, with the primary approach of mathematically capturing heat-treating processes and validating quality predictions. In this article, a contrarian but more realistic scenario is considered, where two industrial problem descriptions become the starting point. The technical complexity of the industry problem has been elaborated for a deeper understanding of the issue along with elaboration of the approach and potential methods for determining a solution. Then, quantitative analyses of practical industrial problems are demonstrated. Finally, the potential shift in these approaches with the advent of Industry 4.0 is outlined.

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.

This article provides an introduction to architected cellular materials, their design, fabrication, and application domain. It discusses design decisions involving the selection, sizing, and spatial distribution of the unit cell, property-scaling relationships, and the integration of cells within an external boundary. It describes how manufacturing constraints influence achievable feature resolution, dimensional accuracy, properties, and defects. It also discusses the mechanical behavior of architected cellular materials and the role of additive manufacturing in their fabrication.

Additive manufacturing (AM) is a revolutionary technology that fabricates parts layerwise and provides many advantages. This article discusses polymer AM processes such as material extrusion, vat photopolymerization (VPP), powder-bed fusion (PBF), binder jetting (BJ), material jetting (MJ), and sheet lamination (SL). It presents the benefits of online monitoring and process control for polymer AM. It also introduces the respective monitoring devices used, including the models and algorithms designed for polymer AM online monitoring and control.

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.

This article provides an overview of different modeling approaches used to capture the phenomena present in the additive manufacturing (AM) process. Inherent to the thermomechanical processing that occurs in AM for metals is the development of residual stresses and distortions. The article then provides an overview of thermal modeling. It presents a discussion on solid mechanics simulation and microstructure simulation.

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.

Data formats play an integral role in leveraging the flexibility of additive manufacturing and achieving consistent part quality. This article compares and contrasts data formats optimized for design, materials, processes, and inspection methods. It also discusses the types of data associated with the six phases of additive manufacturing, namely design, build, design with build plan, design with machine-specific build plan, post-processed part, and qualified part.

To improve the performance of additive manufacturing (AM) systems, a critical need exists for collaborative generation, acquisition, curation, and analysis of digital AM data across the product life cycle. This article discusses the history, development, and potential benefits of the AM Common Data Dictionary (AM-CDD), along with future directions.

Additive manufacturing (AM) creates parts layer by layer directly from three-dimensional computer-aided design data. This article discusses systematic ways to address the challenges in AM data integration by exploring various AM-specific data-integration scenarios that can improve the current AM ecosystem. Representative AM data sources are also described. A reference framework that captures the heterogenous AM data sources and existing data-integration mechanisms are used. General data-integration practices—based on existing manufacturing data and lab information system integration experiences—are recommended to automate AM data flow, operations, and development. Lastly, the article discusses the seven steps in the big-data-integration workflow.

Physics-based feedforward control is discussed in this article for two important laser-based metal additive manufacturing (AM) processes: directed-energy deposition and laser powder-bed fusion. For each type of process, control-oriented, lumped-parameter models that characterize melt pool dynamics as a function of process parameters are discussed first. Then, the derivation of model-based controllers is illustrated, followed by experimental evaluations of the model-based controller implemented as a feedforward control on a commercial AM system.

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.

Additive manufacturing (AM) technologies print three-dimensional (3D) parts through layer-by-layer deposition based on the digital input provided by a computer-aided design file. This article focuses on the binder jet printing process, common biomaterials used in this AM technique, and the clinical applications relevant to these systems. It reviews the challenges and future directions of binder-jetting-based 3D printing.

This article focuses on the directed-energy deposition (DED) additive manufacturing (AM) technique of biomedical alloys. First, it provides an overview of the DED process. This is followed by a section describing the design and development of the multiphysics computational modeling of the layer-by-layer fusion-based DED process. A brief overview of the primary governing equations, boundary conditions, and numerical methods prescribed for modeling laser-based metal AM is then presented. Next, the article discusses fundamental concepts related to laser surface melting and laser-assisted bioceramic coatings/composites on implant surfaces, with particular examples related to biomedical magnesium and titanium alloys. It then provides a review of the processes involved in DED of biomedical stainless steels, Co-Cr-Mo alloys, and biomedical titanium alloys. Further, the article covers novel applications of DED for titanium-base biomedical implants. It concludes with a section on the forecast of DED in biomedical applications.

An ankle-foot orthosis (AFO) is a support designed to regulate the ankle's position and mobility, compensate for weakness, or rectify abnormalities. This article focuses on the biomechanical affects and mechanical properties of custom-made 3D-printed AFOs and compares them to traditionally created AFOs. Investigations in the fields of 3D scanning, 3D printing, and computer-aided design and analysis for the production of custom-made AFOs are also covered.

Additive manufacturing (AM), also referred to as three-dimensional printing or rapid prototyping, is a set of technologies that has rapidly evolved and has drawn much research attention in the manufacturing of high value-added products. This article focuses on dentistry, one of the fields in which AM has gained much traction. It discusses the AM processes used to produce dentures, crowns, and bridges. Digitization techniques, which are the first step and provide the CAD model for AM processes, are presented. Scanning technologies that are widely used in dental manufacturing are presented in detail, and the strengths and weaknesses of each process within their applications are discussed. AM processes are discussed in detail, and the materials that are widely used in AM-embedded dental manufacturing are briefly surveyed. The final section concludes with remarks and a preview of future research and practice directions.