scholarly journals Investigation of SLM Process in Terms of Temperature Distribution and Melting Pool Size: Modeling and Experimental Approaches

Materials ◽  
2019 ◽  
Vol 12 (8) ◽  
pp. 1272 ◽  
Author(s):  
Md Jonaet Ansari ◽  
Dinh-Son Nguyen ◽  
Hong Seok Park
Keyword(s):  
Temperature Distribution ◽  
3D Printing ◽  
Temperature Profile ◽  
Pool Size ◽  
Large Temperature ◽  
Thermal Imager ◽  
Powder Bed ◽  
Melt Pool ◽  
Melting Pool ◽  
Melt Pool Size

Selective laser melting (SLM) is an additive manufacturing (AM) technique that has the potential to produce almost any three-dimensional (3D) metallic part, even those with complicated shapes. Throughout the SLM process, the heat transfer characteristics of the metal powder plays a significant role in maintaining the product quality during 3D printing. Thus, it is crucial for 3D-printing manufacturers to determine the thermal behavior over the SLM process. However, it is a significant challenge to accurately determine the large temperature gradient and the melt pool size using only experiments. Therefore, the use of both experimental investigations and numerical analysis can assist in characterizing the temperature evaluation and the melt pool size in a more effective manner. In this study, 3D finite element analysis applying a moving volumetric Gaussian laser heat source was used to analyze the temperature profile on the powder bed and the resultant melt pool size throughout the SLM process. In the experiments, a TELOPS FAST-IR (M350) thermal imager was applied to determine the temperature profile of the melting pool and powder bed along the scanning direction during the SLM fabrication using Ti6Al4V powder. The numerically calculated results were compared with the experimentally determined temperature distribution. The comparison showed that the calculated peak temperature for single- and multi-track by the developed thermal model was in good agreement with the experiment results. Secondly, the developed model was verified by comparing the melting pool size for various laser powers and scanning speeds with the experimentally measured melting pool size from the published literature. The developed model could predict the melt pool width (with 2–5% error) and melt pool depth (with 5–6% error).

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.

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.

scholarly journals Process Parameter Optimization in Metal Laser-Based Powder Bed Fusion Using Image Processing and Statistical Analyses

Metals ◽  
2022 ◽  
Vol 12 (1) ◽  
pp. 87
Author(s):  
Faiyaz Ahsan ◽  
Jafar Razmi ◽  
Leila Ladani
Keyword(s):  
Contact Angle ◽  
Process Parameters ◽  
Laser Power ◽  
Pool Size ◽  
Scale Production ◽  
Powder Bed Fusion ◽  
Powder Bed ◽  
Melt Pool ◽  
Scan Speed ◽  
Melt Pool Size

The powder bed fusion additive manufacturing process has received widespread interest because of its capability to manufacture components with a complicated design and better surface finish compared to other additive techniques. Process optimization to obtain high quality parts is still a concern, which is impeding the full-scale production of materials. Therefore, it is of paramount importance to identify the best combination of process parameters that produces parts with the least defects and best features. This work focuses on gaining useful information about several features of the bead area, such as contact angle, porosity, voids, melt pool size and keyhole that were achieved using several combinations of laser power and scan speed to produce single scan lines. These features are identified and quantified using process learning, which is then used to conduct a comprehensive statistical analysis that allows to estimate the effect of the process parameters, such as laser power and scan speed on the output features. Both single and multi-response analyses are applied to analyze the response parameters, such as contact angle, porosity and melt pool size individually as well as in a collective manner. Laser power has been observed to have a more influential effect on all the features. A multi-response analysis showed that 150 W of laser power and 200 mm/s produced a bead with the best possible features.

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%).

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 Thermo-Fluid-Dynamic Modeling of the Melt Pool during Selective Laser Melting for AZ91D Magnesium Alloy

Materials ◽  
2020 ◽  
Vol 13 (18) ◽  
pp. 4157
Author(s):  
Hongyao Shen ◽  
Jinwen Yan ◽  
Xiaomiao Niu
Keyword(s):  
Temperature Distribution ◽  
Magnesium Alloy ◽  
Selective Laser Melting ◽  
Laser Power ◽  
Large Temperature ◽  
Fe Model ◽  
Laser Melting ◽  
Az91d Magnesium Alloy ◽  
Melt Pool ◽  
Flow Activity

A three dimensional finite element model (FEM) was established to simulate the temperature distribution, flow activity, and deformation of the melt pool of selective laser melting (SLM) AZ91D magnesium alloy powder. The latent heat in phase transition, Marangoni effect, and the movement of laser beam power with a Gaussian energy distribution were taken into account. The influence of the applied linear laser power on temperature distribution, flow field, and the melt-pool dimensions and shape, as well as resultant densification activity, was investigated and is discussed in this paper. Large temperature gradients and high cooling rates were observed during the process. A violent flow occurred in the melt pool, and the divergent flow makes the melt pool wider and longer but shallower. With the increase of laser power, the melt pool’s size increases, but the shape becomes longer and narrower. The width of the melt pool in single-scan experiment is acquired, which is in good agreement with the results predicted by the simulation (with error of 1.49%). This FE model provides an intuitive understanding of the complex physical phenomena that occur during SLM process of AZ91D magnesium alloy. It can help to select the optimal parameters to improve the quality of final parts and reduce the cost of experimental research.

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 numerical investigation of thermal property effects on melt pool characteristics in powder-bed electron beam additive manufacturing

2016 ◽  
Vol 232 (9) ◽  
pp. 1615-1627 ◽  
Author(s):  
Bo Cheng ◽  
Kevin Chou
Keyword(s):  
Thermal Conductivity ◽  
Electron Beam ◽  
Thermal Properties ◽  
Additive Manufacturing ◽  
Melting Point ◽  
Thermal Property ◽  
Manufacturing Process ◽  
Powder Bed ◽  
Melt Pool ◽  
Melt Pool Size

Powder-bed electron beam additive manufacturing has the potential to be a cost-effective alternative in producing complex-shaped, custom-designed metal parts using various alloys. Material thermal properties have a rather sophisticated effect on the thermal characteristics such as the melt pool geometry in fabrications, impacting the build part quality. The objective of this study is to achieve a quantitative relationship that can correlate the material thermal properties and the melt pool geometric characteristics in the electron beam additive manufacturing process. The motivation is to understand the interactions of material property effect since testing individual properties is insufficient because of the change of almost all thermal properties when switching from one to the other material. In this research, a full-factorial simulation experiment was conducted to include a wide range of the thermal properties and their combinations. A developed finite element thermal model was applied to perform electron beam additive manufacturing process thermal simulations incorporating tested thermal properties. The analysis of variance method was utilized to evaluate different thermal property effects on the simulated melt pool geometry. The major results are summarized as follows. (1) The material melting point is the most dominant factor to the melt pool size. (2) The role of the material thermal conductivity may outweigh the melting point and strongly affects the melt pool size, if the thermal conductivity is very high. (3) Regression equations to correlate the material properties and the melt pool dimension and shape have been established, and the regression-predicted results show a reasonable agreement with the simulation results for tested real-world materials. However, errors still exist for materials with a small melt pool such as copper.

Ultrasonic Frequency Effects on the Melt Pool Formation, Porosity, and Thermal-Dependent Property of Inconel 718 Fabricated by Ultrasonic Vibration-Assisted Directed Energy Deposition

2020 ◽  
Vol 143 (5) ◽  
Author(s):  
Fuda Ning ◽  
Dayue Jiang ◽  
Zhichao Liu ◽  
Hui Wang ◽  
Weilong Cong
Keyword(s):  
Ultrasonic Vibration ◽  
Inconel 718 ◽  
Energy Deposition ◽  
Pool Size ◽  
Peak Temperature ◽  
Ultrasonic Frequency ◽  
Melt Pool ◽  
Directed Energy ◽  
Pool Formation ◽  
Melt Pool Size

Abstract Ultrasonic vibration-assisted (UV-A) directed energy deposition (DED) has become a promising technology to improve the as-built quality and mechanical performance of metal parts. Ultrasonic frequency, a critical parameter of the ultrasonic vibration, can remarkably affect the ultrasonic vibration behaviors in assisting DED processes. However, leveraging varied ultrasonic frequencies in UV-A DED attracts little attention, and the effects of ultrasonic frequency have been thus overlooked. Linking ultrasonic frequency and part performance emphasizes the need for an understanding of the underlying thermodynamics in the melt pool due to the key role of thermal history in the DED process. In this work, we fabricated Inconel 718 (IN718) parts using the UV-A DED process under different levels of ultrasonic vibration frequency (including 0, 25 kHz, 33 kHz, and 41 kHz). For the first time, melt pool size, temperature distribution, and peak temperature within the melt pool, as well as the peak temperature fluctuation within a layer deposition, were studied. Porosity and thermal-dependent properties including grain size and microhardness were also investigated. The results indicated that the increase in ultrasonic frequency led to an increase in both melt pool size and peak temperature. Moreover, the lowest porosity was obtained at an ultrasonic frequency of 25 kHz, while grain refinement and microhardness enhancement were achieved at the highest frequency of 41 kHz. This investigation provides great insights into the link among ultrasonic frequency, melt pool formation, temperature field, porosity, and thermal-dependent properties in the UV-A DED-built IN718 parts.

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