TEMPERATURE DISTRIBUTION AND MELT POOL SIZE IN A SEMI-INFINITE BODY DUE TO A MOVING LASER HEAT SOURCE

1997 ◽  
Vol 31 (7) ◽  
pp. 783-796 ◽  
Author(s):  
Ali A. Rostami ◽  
A. Raisi
Keyword(s):  
Temperature Distribution ◽  
Heat Source ◽  
Pool Size ◽  
Infinite Body ◽  
Melt Pool ◽  
Laser Heat ◽  
Laser Heat Source ◽  
Melt Pool Size

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).

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.

Development of Multi-Axis Laser-Assisted Milling Device

2017 ◽  
Author(s):  
Dong-Hyeon Kim ◽  
Wan-Sik Woo ◽  
Won-Shik Chu ◽  
Sung-Hoon Ahn ◽  
Choon-Man Lee
Keyword(s):  
Heat Source ◽  
Tool Path ◽  
Cutting Tool ◽  
Field Studies ◽  
Flat Surfaces ◽  
Laser Heat ◽  
Complex Shapes ◽  
Laser Heat Source ◽  
Removal Processes ◽  
Machining Productivity

Laser-assisted machining (LAM) process is an effective method to facilitate material removal processes for difficult-to-cut materials. In LAM process, the mechanical strength of various materials is reduced by a laser heat source focused in front of the cutting tool during machining. Since the laser heat source is located ahead of the cutting tool, the workpiece is preheated by the heat source. This enables difficult-to-cut materials to be machined more easily with low cutting energy, increasing the machining productivity and accuracy. It is difficult to apply laser-assisted milling (LAMilling) to workpieces having complex shapes, because it is not easy to control laser preheating and the cutting tool path for three-dimensionally shaped workpieces. LAMilling has only been used in limited fields such as single-direction machining of flat surfaces. To apply this process in the industrial field, studies on workpieces having various shapes are needed. This study aims to develop a laser-assisted milling device having multiple axes and to investigate the machining characteristics of several difficult-to-cut materials.

Laser Lock-In Thermography for Fatigue Crack Detection

2013 ◽  
Vol 558 ◽  
pp. 76-83 ◽  
Author(s):  
Yun Kyu An ◽  
Ji Min Kim ◽  
Hoon Sohn
Keyword(s):  
Fatigue Crack ◽  
Heat Source ◽  
High Power ◽  
Crack Detection ◽  
Laser Source ◽  
Cw Laser ◽  
Laser Heat ◽  
Laser Heat Source ◽  
Fatigue Crack Detection ◽  
Lock In

This study proposes a new nondestructive evaluation methodology named laser lock-in thermography (LLT) for fatigue crack detection. LLT utilizes a high power continuous wave (CW) laser as a heat generation source for lock-in thermography instead of commonly used flash and halogen lamps. The advantages of the proposed LLT method are that (1) the laser heat source can be positioned at an extended distance from a target structure thank to the directionality and low energy loss of the laser source, (2) thermal image degradation due to surrounding temperature disturbances can be minimized because of high temperature gradient generated by the laser source and (3) a large target surface can be inspected using a scanning laser heat source. The developed LLT system is composed of a modulated high power CW laser, galvanometer and infrared camera. Then, a holder exponent-based data processing algorithm is proposed for intuitive damage evaluation. The developed LLT is employed to detect a micro fatigue crack in a metal plate. The test result confirms that 5 μm (or smaller) fatigue crack in a dog-bone shape aluminum plate with a dimension of 400 x 140 x 3 mm3 can be detected.

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.

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