Search Results for melt pool temperature

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 covers in-line process monitoring of the metal additive manufacturing (AM) methods of laser and electron beam (e-beam) powder-bed fusion (PBF) and directed-energy deposition (DED). It focuses on methods that monitor the component directly throughout the build process. This article is organized by the type of AM process and by the physics of the monitoring method. The discussion covers two types of monitoring possible with the PBF process: monitoring the area of the powder bed and component and monitoring the melt pool created by the laser or e-beam. Methods for layer monitoring include optical and thermal methods that monitor light reflected or emitted in the visible and infrared wavelengths, respectively. Monitoring methods for laser directed-energy deposition (DED) discussed are those that measure the size and shape of the melt pool, the temperature of the melt pool, and the plasma generated by the laser as it interacts with the molten metal.

Additive manufacturing produces a change in the shape of a substrate by adding material progressively. This article discusses the simulation of laser deposition and three principal thermomechanical phenomena during the laser deposition process: absorption of laser radiation; heat conduction, convection, and phase change; and elastic-plastic deformation. It provides a description of four sets of data used for modeling and simulation of additive manufacturing processes, namely, material constitutive data, solid model, initial and boundary conditions, and laser deposition process parameters. The article considers three aspects of simulation of additive manufacturing: simulation for initial selection of process parameter setup, simulation for in situ process control, and simulation for ex situ process optimization. It also presents some examples of computational mechanics solutions for automating various components of additive manufacturing simulation.

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.

This article provides readers with a brief review of the applications of thermography in additive manufacturing (AM), which still is largely a research and development (R&D) effort. There is a particular focus on metals-based laser powder-bed fusion (L-PBF), although applications in directed-energy deposition (DED) and electron beam PBF (E-PBF) also are mentioned. The metrological basis of thermography is discussed in the article. Background information on radiation thermometry is provided, including how the various equations are applied. Finally, specific examples and lessons learned from various AM thermographic studies at the National Institute of Standards and Technology (NIST) are provided.

This article presents a general understanding of causes and possible solutions for defects in the most common metal additive manufacturing (AM) processes: laser powder-bed fusion (L-PBF), laser directed-energy deposition (DED-L), and binder jetting (BJ).

Fusion welding processes involve four phase changes, namely, solid-solid state, solid-liquid, liquid-vapor, and vapor-plasma. Each has its own thermal, momentum, and stress history. This article discusses some important techniques to validate temperature, momentum, stress, and residual strain history observed in the heat-affected zone of fusion welded materials.

The vacuum arc remelting (VAR) process is widely used to improve the cleanliness and refine the structure of standard air melted or vacuum induction melted (VIM) ingots. It is also used in the triplex production of superalloys. This article illustrates the VAR process and the capabilities and variables of the process. It also presents a discussion on the melt solidification, resulting structure, and ingot defects. The article concludes with a discussion on the VAR process of superalloy and titanium and titanium alloy.

This article provides an overview of the studies on computational modeling of the vacuum arc remelting (VAR) and electroslag remelting (ESR) processes. These models involve the axisymmetric analysis of the electromagnetic, flow, heat-transfer, and phase-change phenomena to predict the pool shape and thermal history of an ingot using two-dimensional axisymmetric models for VAR and ESR. Analysis of segregation of alloying elements during solidification that gives rise to macrolevel compositional nonuniformity in titanium alloy ingots is also described. The article discusses the important features of the control-volume-based computational method to review the unique aspects of the processes. Measurement of the properties of alloys and slags is explained and an analysis of the process variants for improving the predictive accuracy of the models is presented.

This article provides the basic physics of the two most widely used arc welding processes: gas tungsten arc welding and gas metal arc welding. It describes the various control parameters of these processes such as arc length control, voltage control, heat input control, and metal-transfer control.

Electroslag welding (ESW) involves high energy input relative to other welding processes, resulting generally in inferior mechanical properties and specifically in lower toughness of the heat-affected zone. Electrogas welding (EGW) is a method of gas metal or flux cored arc welding, wherein an external gas is supplied to shield the arc, and molding shoes are used to confine the molten weld metal for vertical-position welding. This article describes the fundamentals, temperature relations, consumables, metallurgical and chemical reactions, and process development of ESW. The problems, quality control, and process applications of ESW and EGW are also discussed.

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.

Electroslag welding (ESW) and electrogas welding (EGW) are two related procedures that are used to weld thick-section materials in the vertical or near-vertical position between retaining shoes. This article discusses the fundamentals of the electroslag process in terms of heat flow conditions and metal transfer and weld pool morphology. It presents constitutive equations for welding current, voltage, and travel rate for ESW. The article describes the metallurgical and chemical reactions in terms of fusion zone compositional effects, weld metal inclusions, solidification structure, and solid-state transformations. It describes the electroslag process development and the applications of electroslag and electrogas processes. The article concludes with a discussion on weld defects, such as temper embrittlement, hydrogen cracking, and weld distortion.

Additive manufacturing (AM) is the process of joining materials to make parts from three-dimensional (3D) model data, usually layer upon layer, as opposed to subtractive manufacturing and formative manufacturing methodologies. This article discusses various defects in AM components, such as porosity, inclusions, cracking, and residual stress, that can be avoided by using vendor recommended process parameters and approved materials. It describes the development of process-structure-property-performance modeling. The article explains the practical considerations in nondestructive evaluation for additively manufactured metallic parts. It also examines nondestructive testing (NDT) inspection and characterization methods for each of the manufacturing stages in their natural order. The article provides information on various inspection techniques for completed AM manufactured parts. The various electromagnetic and eddy current techniques that can be used to detect changes to nearsurface geometric anomalies or other defects are also discussed. These include ultrasonic techniques, radiographic techniques, and neutron imaging.