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 Beyond the Toolpath: Site-Specific Melt Pool Size Control Enables Printing of Extra-Toolpath Geometry in Laser-Wire Directed Energy Deposition

2019 ◽  
Author(s):  
Brian T. Gibson ◽  
Brad S. Richardson ◽  
Taylor W. Sundermann ◽  
Lonnie J. Love
Keyword(s):  
Energy Deposition ◽  
Closed Loop ◽  
Size Control ◽  
Pool Size ◽  
Bead Geometry ◽  
Site Specific ◽  
Melt Pool ◽  
Directed Energy Deposition ◽  
Directed Energy ◽  
Melt Pool Size

A variety of techniques have been utilized in metal additive manufacturing (AM) for melt pool size management, including modeling and feed-forward approaches. In a few cases, closed-loop control has been demonstrated. In this research, closed-loop melt pool size control for large-scale, laser-wire based Directed Energy Deposition is demonstrated with a novel modification: site-specific changes to the controller set-point were commanded at trigger points, the locations of which were generated by the projection of a secondary geometry onto the primary 3D-printed component geometry. The present work shows that, through this technique, it is possible to print a specific geometry that occurs beyond the actual toolpath of the print head. This is denoted as an extra-toolpath geometry and is fundamentally different from other methods of generating component features in metal AM. A proof-of-principle experiment is presented in which a complex oak leaf geometry was embossed on an otherwise ordinary double-bead wall made from Ti-6Al-4V. The process is introduced and characterized primarily from a controls perspective with reports on the performance of the control system, the melt pool size response, and the resulting geometry. The implications of this capability, which extend beyond localized control of bead geometry to the potential mitigations of defects and functional grading of component properties, are discussed.

scholarly journals Beyond the Toolpath: Site-Specific Melt Pool Size Control Enables Printing of Extra-Toolpath Geometry in Laser Wire-Based Directed Energy Deposition

2019 ◽  
Vol 9 (20) ◽  
pp. 4355
Author(s):  
Brian T. Gibson ◽  
Bradley S. Richardson ◽  
Tayler W. Sundermann ◽  
Lonnie J. Love
Keyword(s):  
Energy Deposition ◽  
Closed Loop ◽  
Size Control ◽  
Pool Size ◽  
Bead Geometry ◽  
Site Specific ◽  
Melt Pool ◽  
Directed Energy Deposition ◽  
Directed Energy ◽  
Melt Pool Size

A variety of techniques have been utilized in metal additive manufacturing (AM) for melt pool size management, including modeling and feed-forward approaches. In a few cases, closed-loop control has been demonstrated. In this research, closed-loop melt pool size control for large-scale, laser wire-based directed energy deposition is demonstrated with a novel modification, i.e., site-specific changes to the controller setpoint were commanded at trigger points, the locations of which were generated by the projection of a secondary geometry onto the primary three-dimensional (3D) printed component geometry. The present work shows that, through this technique, it is possible to print a specific geometry that occurs beyond the actual toolpath of the print head. This is denoted as extra-toolpath geometry and is fundamentally different from other methods of generating component features in metal AM. A proof-of-principle experiment is presented in which a complex oak leaf geometry was embossed on an otherwise ordinary double-bead wall made from Ti-6Al-4V. The process is introduced and characterized primarily from a controls perspective with reports on the performance of the control system, the melt pool size response, and the resulting geometry. The implications of this capability, which extend beyond localized control of bead geometry to the potential mitigations of defects and functional grading of component properties, are discussed.

Melt pool size control through multiple closed-loop modalities in laser-wire directed energy deposition of Ti-6Al-4V

2020 ◽  
Vol 32 ◽  
pp. 100993 ◽  
Author(s):  
B.T. Gibson ◽  
Y.K. Bandari ◽  
B.S. Richardson ◽  
W.C. Henry ◽  
E.J. Vetland ◽  
...  
Keyword(s):  
Energy Deposition ◽  
Closed Loop ◽  
Size Control ◽  
Pool Size ◽  
Melt Pool ◽  
Directed Energy Deposition ◽  
Directed Energy ◽  
Melt Pool Size

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.

3D Build Melt Pool Predictive Modeling for Powder Bed Fusion Additive Manufacturing

2020 ◽  
Author(s):  
Zhuo Yang ◽  
Yan Lu ◽  
Ho Yeung ◽  
Sundar Kirshnamurty
Keyword(s):  
Neural Network ◽  
Additive Manufacturing ◽  
Pool Size ◽  
Powder Bed Fusion ◽  
Neural Network Approach ◽  
Validation Data ◽  
Powder Bed ◽  
Melt Pool ◽  
Scan Pattern ◽  
Melt Pool Size

Abstract Melt pool size is a critical intermediate measure that reflects the outcome of a laser powder bed fusion process setting. Reliable melt pool predictions prior to builds can help users to evaluate potential part defects such as lack of fusion and over melting. This paper develops a layer-wise Neighboring-Effect Modeling (L-NBEM) method to predict melt pool size for 3D builds. The proposed method employs a feedforward neural network model with ten layer-wise and track-wise input variables. An experimental build using a spiral concentrating scan pattern with varying laser power was conducted on the Additive Manufacturing Metrology Testbed at the National Institute of Standards and Technology. Training and validation data were collected from 21 completed layers of the build, with 6,192,495 digital commands and 118,928 in-situ melt pool coaxial images. The L-NBEM model using the neural network approach demonstrates a better performance of average predictive error (12.12%) by leave-one-out cross-validation method, which is lower than the benchmark NBEM model (15.23%), and the traditional power-velocity model (19.41%).

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.

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.

Effect of Time Spacing and Hatch Spacing on Thermal Material Properties and Melt Pool Geometry in Additive Manufacturing of S316L

2019 ◽  
Author(s):  
Elham Mirkoohi ◽  
Daniel E. Sievers ◽  
Steven Y. Liang
Keyword(s):  
Heat Transfer ◽  
Additive Manufacturing ◽  
Material Properties ◽  
Metal Additive Manufacturing ◽  
Melt Pool ◽  
Experimental Values ◽  
Melt Pool Size ◽  
Transfer Mechanisms ◽  
Hatch Spacing ◽  
Metal Additive

Abstract A physics-based analytical solution is proposed in order to investigate the effect of hatch spacing and time spacing (which is the time delay between two consecutive irradiations) on thermal material properties and melt pool geometry in metal additive manufacturing processes. A three-dimensional moving point heat source approach is used in order to predict the thermal behavior of the material in additive manufacturing process. The thermal material properties are considered to be temperature dependent since the existence of the steep temperature gradient has a substantial influence on the magnitude of the thermal conductivity and specific heat, and as a result, it has an influence on the heat transfer mechanisms. Moreover, the melting/solidification phase change is considered using the modified heat capacity since it has an influence on melt pool geometry. The proposed analytical model also considers the multi-layer aspect of metal additive manufacturing since the thermal interaction of the successive layers has an influence on heat transfer mechanisms. Temperature modeling in metal additive manufacturing is one of the most important predictions since the presence of the temperature gradient inside the build part affect the melt pool size and geometry, thermal stress, residual stress, and part distortion. In this paper, the effect of time spacing and hatch spacing on thermal material properties and melt pool geometry is investigated. Both factors are found statistically significant with regard to their influence on thermal material properties and melt pool geometry. The predicted melt pool size is compared to experimental values from independent reports. Good agreement is achieved between the proposed physics-based analytical model and experimental values.

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