scholarly journals Estimation of track dimensions obtained in Laser Metal Deposition-powder thanks to a semi-analytical model coupled to an Eulerian thermal simulation

2021 ◽  
Author(s):  
Cécile Leroy-Dubief ◽  
Fabien Poulhaon ◽  
Pierre Joyot
Keyword(s):  
Thermal Simulation ◽  
Laser Metal Deposition ◽  
Transmitted Power ◽  
Track Geometry ◽  
Melt Pool ◽  
Beam Attenuation ◽  
Model Predictions ◽  
Powder Particles ◽  
Working Distance ◽  
Melt Pool Size

Originally issued from cladding, the LMD-p process widens the field of possibilities in terms of manufacturing. Depending on the targeted application, the needs regarding the track geometry are different and the ability to adapt it is a key challenge. In LMD-p, the laser beam attenuation as well as the powder particles preheating are both determined by laser-powder interactions before the powder reaches the substrate. The track dimensions are directly correlated to the melt pool size: a larger pool will tend to capture more powder resulting in a higher deposition rate. The model presented here intends to determine, for a given working distance, the partition of energy, and to estimate the area of the generated melt pool and finally the dimensions of the deposited track. It is first based on a semi-analytical approach that models the powder distribution and calculates the transmitted power to both substrate and powder particles. The attenuated power density is then an input for a light Eulerian thermal simulation from which the contour of the molten zone is extracted. Several iterations are carried out to account for the energy loss caused by the heating and melting of the powder entering the pool. Lastly, the track dimensions are estimated from the stabilized melt pool configuration. Track geometries obtained with a BeAM® machine are compared to the model predictions. Such an approach opens very interesting perspectives in studying the influence of the working distance and its optimization for a given material and/or a given application.

scholarly journals A multi-parameter control of track geometry and melt pool size for laser metal deposition

Procedia CIRP ◽  
2020 ◽  
Vol 94 ◽  
pp. 430-435 ◽  
Author(s):  
Dieter Tyralla ◽  
Henry Köhler ◽  
Thomas Seefeld ◽  
Claus Thomy ◽  
Ryuichi Narita
Keyword(s):  
Metal Deposition ◽  
Pool Size ◽  
Parameter Control ◽  
Laser Metal Deposition ◽  
Track Geometry ◽  
Melt Pool ◽  
Melt Pool Size

A Physics-Informed Two-Level Machine Learning Model for Predicting Melt-Pool Size in Laser Powder Bed Fusion

2021 ◽  
Author(s):  
Yong Ren ◽  
Qian Wang ◽  
Panagiotis (Pan) Michaleris
Keyword(s):  
Machine Learning ◽  
Initial Temperature ◽  
Pool Size ◽  
Powder Bed Fusion ◽  
Laser Powder Bed Fusion ◽  
Powder Bed ◽  
Melt Pool ◽  
Input Feature ◽  
Melt Pool Size ◽  
Melt Pool Characteristics

Abstract Laser powder bed fusion (L-PBF) additive manufacturing (AM) is one type of metal-based AM process that is capable of producing high-value complex components with a fine geometric resolution. As melt-pool characteristics such as melt-pool size and dimensions are highly correlated with porosity and defects in the fabricated parts, it is crucial to predict how process parameters would affect the melt-pool size and dimensions during the build process to ensure the build quality. This paper presents a two-level machine learning (ML) model to predict the melt-pool size during the scanning of a multi-track build. To account for the effect of thermal history on melt-pool size, a so-called (pre-scan) initial temperature is predicted at the lower-level of the modeling architecture, and then used as a physics-informed input feature at the upper-level for the prediction of melt-pool size. Simulated data sets generated from the Autodesk's Netfabb Simulation are used for model training and validation. Through numerical simulations, the proposed two-level ML model has demonstrated a high prediction performance and its prediction accuracy improves significantly compared to a naive one-level ML without using the initial temperature as an input feature.

Accelerating Large-Format Metal Additive Manufacturing: How Controls R&D Is Driving Speed, Scale, and Efficiency

2020 ◽  
Author(s):  
Brian T. Gibson ◽  
Paritosh Mhatre ◽  
Michael C. Borish ◽  
Justin L. West ◽  
Emma D. Betters ◽  
...  
Keyword(s):  
Additive Manufacturing ◽  
Large Scale ◽  
Closed Loop ◽  
Size Control ◽  
Pool Size ◽  
Large Format ◽  
Layer Height ◽  
Melt Pool ◽  
The Impact ◽  
Melt Pool Size

Abstract This article highlights work at Oak Ridge National Laboratory’s Manufacturing Demonstration Facility to develop closed-loop, feedback control for laser-wire based Directed Energy Deposition, a form of metal Big Area Additive Manufacturing (m-BAAM), a process being developed in partnership with GKN Aerospace specifically for the production of Ti-6Al-4V pre-forms for aerospace components. A large-scale structural demonstrator component is presented as a case-study in which not just control, but the entire 3D printing workflow for m-BAAM is discussed in detail, including design principles for large-format metal AM, toolpath generation, parameter development, process control, and system operation, as well as post-print net-shape geometric analysis and finish machining. In terms of control, a multi-sensor approach has been utilized to measure both layer height and melt pool size, and multiple modes of closed-loop control have been developed to manipulate process parameters (laser power, print speed, deposition rate) to control these variables. Layer height control and melt pool size control have yielded excellent local (intralayer) and global (component-level) geometry control, and the impact of melt pool size control in particular on thermal gradients and material properties is the subject of continuing research. Further, these modes of control have allowed the process to advance to higher deposition rates (exceeding 7.5 lb/hr), larger parts (1-meter scale), shorter build times, and higher overall efficiency. The control modes are examined individually, highlighting their development, demonstration, and lessons learned, and it is shown how they operate concurrently to enable the printing of a large-scale, near net shape Ti-6Al-4V component.

scholarly journals Study of the Influence of Shielding Gases on Laser Metal Deposition of Inconel 718 Superalloy

Materials ◽  
2018 ◽  
Vol 11 (8) ◽  
pp. 1388 ◽  
Author(s):  
Jose Ruiz ◽  
Magdalena Cortina ◽  
Jon Arrizubieta ◽  
Aitzol Lamikiz
Keyword(s):  
Metal Deposition ◽  
Slight Reduction ◽  
Laser Metal Deposition ◽  
Melt Pool ◽  
Industrial Sectors ◽  
Inconel 718 Superalloy ◽  
Melt Pool Temperature ◽  
Different Gases ◽  
Die And Mold

The use of the Laser Metal Deposition (LMD) technology as a manufacturing and repairing technique in industrial sectors like the die and mold and aerospace is increasing within the last decades. Research carried out in the field of LMD process situates argon as the most usual inert gas, followed by nitrogen. Some leading companies have started to use helium and argon as carrier and shielding gas, respectively. There is therefore a pressing need to know how the use of different gases may affect the LMD process due there being a lack of knowledge with regard to gas mixtures. The aim of the present work is to evaluate the influence of a mixture of argon and helium on the LMD process by analyzing single tracks of deposited material. For this purpose, special attention is paid to the melt pool temperature, as well as to the characterization of the deposited clads. The increment of helium concentration in the gases of the LMD processes based on argon will have three effects. The first one is a slight reduction of the height of the clads. Second, an increase of the temperature of the melt pool. Last, smaller wet angles are obtained for higher helium concentrations.

scholarly journals Analysis of Melt Pool Characteristics and Process Parameters Using a Coaxial Monitoring System during Directed Energy Deposition in Additive Manufacturing

Materials ◽  
2019 ◽  
Vol 12 (2) ◽  
pp. 308 ◽  
Author(s):  
Christian Kledwig ◽  
Holger Perfahl ◽  
Martin Reisacher ◽  
Frank Brückner ◽  
Jens Bliedtner ◽  
...  
Keyword(s):  
Stable Process ◽  
Base Material ◽  
Industrial Applications ◽  
Process Conditions ◽  
Melt Pool ◽  
Temporal Variance ◽  
Laser Deposition Welding ◽  
Working Distance ◽  
Melt Pool Characteristics ◽  
Pool Area

The growing number of commercially available machines for laser deposition welding show the growing acceptance and importance of this technology for industrial applications. Their increasing usage in research and production requires process stability and user-friendly handling. A commercially available DMG MORI LT 65 3D hybrid machine used in combination with a CCD-based coaxial temperature measurement system was utilized in this work to investigate what information relating to the intensity distribution of melt pool surfaces could be appropriate to draw conclusions about process conditions. In this study it is shown how the minimal required specific energy for a stable process can be determined, and it is indicated that the evolution of a plasma plume depends on thermal energy within the base material. An estimated melt pool area—calculated by the number of pixels (NOP) with intensities larger than a fixed, predefined threshold—builds the main measure in analysing images from the process camera. The melt pool area and its temporal variance can also serve as an indicator for an increased working distance.

On Process Temperature in Powder-Bed Electron Beam Additive Manufacturing: Model Development and Validation

2014 ◽  
Vol 136 (6) ◽  
Author(s):  
Bo Cheng ◽  
Steven Price ◽  
James Lydon ◽  
Kenneth Cooper ◽  
Kevin Chou
Keyword(s):  
Thermal Conductivity ◽  
Electron Beam ◽  
Additive Manufacturing ◽  
Thermal Characteristics ◽  
Pool Size ◽  
Process Temperature ◽  
Thermal Imager ◽  
Powder Bed ◽  
Melt Pool ◽  
Melt Pool Size

Powder-bed beam-based metal additive manufacturing (AM) such as electron beam additive manufacturing (EBAM) has a potential to offer innovative solutions to many challenges and difficulties faced in the manufacturing industry. However, the complex process physics of EBAM has not been fully understood, nor has process metrology such as temperatures been thoroughly studied, hindering part quality consistency, efficient process development and process optimizations, etc., for effective EBAM usage. In this study, numerical and experimental approaches were combined to research the process temperatures and other thermal characteristics in EBAM using Ti–6Al–4V powder. The objective of this study was to develop a comprehensive thermal model, using a finite element (FE) method, to predict temperature distributions and history in the EBAM process. On the other hand, a near infrared (NIR) thermal imager, with a spectral range of 0.78 μm–1.08 μm, was employed to acquire build surface temperatures in EBAM, with subsequent data processing for temperature profile and melt pool size analysis. The major results are summarized as follows. The thermal conductivity of Ti–6Al–4V powder is porosity dependent and is one of critical factors for temperature predictions. The measured thermal conductivity of preheated powder (of 50% porosity) is 2.44 W/m K versus 10.17 W/m K for solid Ti–6Al–4V at 750 °C. For temperature measurements in EBAM by NIR thermography, a method was developed to compensate temperature profiles due to transmission loss and unknown emissivity of liquid Ti–6Al–4V. At a beam speed of about 680 mm/s, a beam current of about 7.0 mA and a diameter of 0.55 mm, the peak process temperature is on the order around 2700 °C, and the melt pools have dimensions of about 2.94 mm, 1.09 mm, and 0.12 mm, in length, width, and depth, respectively. In general, the simulations are in reasonable agreement with the experimental results with an average error of 32% for the melt pool sizes. From the simulations, the powder porosity is found critical to the thermal characteristics in EBAM. Increasing the powder porosity will elevate the peak process temperature and increase the melt pool size.

Distributed Temperature Control in Laser-Based Manufacturing

2020 ◽  
Vol 142 (6) ◽  
Author(s):  
Chengjian Zheng ◽  
John T. Wen ◽  
Mamadou Diagne
Keyword(s):  
Temperature Field ◽  
Steady State ◽  
Cooling Rate ◽  
Temperature Control ◽  
Heat Dissipation ◽  
Pool Size ◽  
Scanning Laser ◽  
Measured Temperature ◽  
Melt Pool ◽  
Melt Pool Size

Abstract Temperature control is essential for regulating material properties in laser-based manufacturing. Motion and power of the scanning laser affect local temperature evolution, which in turn determines the a posteriori microstructure. This paper addresses the problem of adjusting the laser speed and power to achieve the desired values of key process parameters: cooling rate and melt pool size. The dynamics of a scanning laser system is modeled by a one-dimensional (1D) heat conduction equation, with laser power as the heat input and heat dissipation to the ambient. Since the model is 1D, length and size are essentially the same. We pose the problem as a regulation problem in the (moving) laser frame. The first step is to obtain the steady-state temperature distribution and the corresponding input based on the desired cooling rate and melt pool size. The controller adjusts the input around the steady-state feedforward based on the deviation of the measured temperature field from the steady-state distribution. We show that with suitably defined outputs, the system is strictly passive from the laser motion and power. To avoid over-reliance on the model, the steady-state laser speed and power are adaptively updated, resulting in an integral-like update law for the feedforward. Moreover, the heat transfer coefficient to the ambient may be uncertain, and can also be adaptively updated. The final form of the control law combines passive error temperature field feedback with adaptive feedforward and parameter estimation. The closed-loop asymptotical stability is shown using the Lyapunov arguments, and the controller performance is demonstrated in a simulation.

A Neighborhood-Based Neural Network for Melt Pool Prediction and Control

2020 ◽  
Author(s):  
Yaqi Zhang ◽  
Vadim Shapiro ◽  
Paul Witherell
Keyword(s):  
Neural Network ◽  
Heat Source ◽  
Surrogate Model ◽  
Laser Power ◽  
Moving Heat Source ◽  
Melt Pool ◽  
The Neural Network ◽  
Scan Path ◽  
Melt Pool Size ◽  
Manufacturing Parameters

Abstract One of the most prevalent additive manufacturing processes, the powder bed fusion process, is driven by a moving heat source that melts metals to build a part. This moving heat source, and the subsequent formation and moving of a melt pool, plays an important role in determining both the geometric and mechanical properties of the printed components. The ability to control the melt pool during the build process is a sought after mechanism for improving quality control and optimizing manufacturing parameters. For this reason, efficient models that can predict melt pool size based on the process input (i.e., laser power, scan speed, spot size and scan path) offer a path to improved process control. Towards improved process control, a data-driven melt pool prediction model is built with a neighborhood-based neural network and trained using experimental data from the National Institute of Standards and Technology (NIST). The model considers the influence of both manufacturing parameters and laser scan paths. The scan path information is encoded using two novel neighborhood features of the neural network through locality. The neural network is used to generate a surrogate model, and we demonstrate how the performance of the resulting surrogate model can be further improved by using several ensemble methods. We then demonstrate how the trained surrogate model can be used as a forward solver for developing novel laser power design algorithms. The resulting laser power plan is designed to keep melt pool size as constant as possible for any given scan pattern. The algorithm is implemented and validated with numerical experiments.

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