Estimation of melt pool size by complementary use of external illumination and process emission in coaxial monitoring of selective laser melting

2019 ◽  
Vol 31 (2) ◽  
pp. 022305 ◽  
Author(s):  
Matteo Pacher ◽  
Luca Mazzoleni ◽  
Leonardo Caprio ◽  
Ali Gökhan Demir ◽  
Barbara Previtali
Keyword(s):  
Selective Laser Melting ◽  
Pool Size ◽  
Laser Melting ◽  
Melt Pool ◽  
Melt Pool Size

scholarly journals Numerical Investigation of Thermo-Mechanical Field during Selective Laser Melting Process with Experimental Validation

Metals ◽  
2021 ◽  
Vol 11 (7) ◽  
pp. 1003
Author(s):  
Lan Li ◽  
Frank Liou
Keyword(s):  
Stainless Steel ◽  
Selective Laser Melting ◽  
Thermal Stresses ◽  
Pool Size ◽  
Solidification Structure ◽  
Temperature History ◽  
304L Stainless Steel ◽  
Laser Melting ◽  
Melt Pool ◽  
Melt Pool Size

In this study, thermo-mechanical simulation was conducted to predict thermal and stress behavior in Selective Laser Melting (SLM). Temperature-dependent material properties for processed material 304L stainless steel were incorporated into the model in order to capture the change from powder to fully dense solid stainless steel. Temperature and thermal stress history were tracked under conditions of different parameter sets which were designed to reduce defect formation. The thermal model predicted the temperature history for multi-track scans under different process parameters, such as laser power, effective scanning speed and hatch spacing. Subsequently, the corresponding melt-pool size, solidification rate and temperature gradients could be calculated from simulated temperature data. These three parameters from the simulation were compared with experimental melt pool size, grain structure and cell spacing data obtained from a Renishaw AM250. The experimental data were also used to determine unknown simulation parameters required by the continuum model, e.g., the optical penetration depth and thermal conductivity multiplier for the molten region. This allowed the simulation model to accurately predict melt pool size and solidification structure of SLM 304L stainless steel. Simulated stress showed that the subsequent thermal cyclic melting in successive scanned tracks resulted in alternating compressive and tensile thermal stresses. This work will provide insight for studying microstructure morphology, residual stress and deformations in the SLM process of 304L stainless steel.

scholarly journals Effect of process parameters on the microstructure and mechanical properties of AA2024 fabricated using selective laser melting

2020 ◽  
Vol 112 (1-2) ◽  
pp. 175-192
Author(s):  
Mulla Ahmet Pekok ◽  
Rossitza Setchi ◽  
Michael Ryan ◽  
Quanquan Han ◽  
Dongdong Gu
Keyword(s):  
Mechanical Properties ◽  
Selective Laser Melting ◽  
Scanning Speed ◽  
High Energy ◽  
Incomplete Fusion ◽  
Laser Melting ◽  
Melt Pool ◽  
2024 Alloy ◽  
Manufacturing Techniques ◽  
Melt Pool Size

AbstractSelective laser melting (SLM) offers significant benefits, including geometric freedom and rapid production, when compared with traditional manufacturing techniques. However, the materials available for SLM production remain limited, restricting the industrial adoption of the technology. The mechanical properties and microstructure of many aluminium alloys have not been fully explored, as their manufacturability using SLM is extremely challenging. This study investigates the effect of laser power, hatch spacing and scanning speed on the mechanical and microstructural properties of as-fabricated aluminium 2024 alloy (AA2024) manufactured using SLM. The results reveal that almost crack-free structures with high relative density (99.9%) and Archimedes density (99.7%) have been achieved. It is shown that when using low energy density (ED) levels, large cracks and porosities are a major problem, owing to incomplete fusion; however, small gas pores are prevalent at high-energy densities due to the dissolved gas particles in the melt pool. An inversely proportional relationship between ED and microhardness has also been observed. Lower ED decreases the melt pool size and temperature gradients but increases the cooling rate, creating a fine-grained microstructure, which restricts dislocation movement, therefore increasing the microhardness. The highest microhardness (116 HV0.2), which was obtained from one of the lowest EDs used (100 J/mm3), is 45% higher than as-cast AA2024-0, but 17% lower than wrought AA2024-T6 alloy.

scholarly journals Feedback Control of Melt Pool Area in Selective Laser Melting Additive Manufacturing Process

Processes ◽  
2021 ◽  
Vol 9 (9) ◽  
pp. 1547
Author(s):  
Syed Zahid Hussain ◽  
Zareena Kausar ◽  
Zafar Ullah Koreshi ◽  
Shakil R. Sheikh ◽  
Hafiz Zia Ur Rehman ◽  
...  
Keyword(s):  
Additive Manufacturing ◽  
Feedback Control ◽  
Selective Laser Melting ◽  
Manufacturing Process ◽  
Thermal Model ◽  
Laser Melting ◽  
Powder Bed ◽  
Melt Pool ◽  
Melt Pool Size ◽  
Pool Area

Selective laser melting (SLM), a metal powder fusion additive manufacturing process, has the potential to manufacture complex components for aerospace and biomedical implants. Large-scale adaptation of these technologies is hampered due to the presence of defects such as porosity and part distortion. Nonuniform melt pool size is a major cause of these defects. The melt pool size changes due to heat from the previous powder bed tracks. In this work, the effect of heat sourced from neighbouring tracks was modelled and feedback control was designed. The objective of control is to regulate the melt pool cross-sectional area rejecting the effect of heat from neighbouring tracks within a layer of the powder bed. The SLM process’s thermal model was developed using the energy balance of lumped melt pool volume. The disturbing heat from neighbouring tracks was modelled as the initial temperature of the melt pool. Combining the thermal model with disturbance model resulted in a nonlinear model describing melt pool evolution. The PID, a classical feedback control approach, was used to minimize the effect of intertrack disturbance on the melt pool area. The controller was tuned for the desired melt pool area in a known environment. Simulation results revealed that the proposed controller regulated the desired melt pool area during the scan of multiple tracks of a powder layer within 16 milliseconds and within a length of 0.04 mm reducing laser power by 10% approximately in five tracks. This reduced the chance of pore formation. Hence, it enhances the quality of components manufactured using the SLM process, reducing defects.

scholarly journals Effect of Hatch Spacing on Melt Pool and As-built Quality During Selective Laser Melting of Stainless Steel: Modeling and Experimental Approaches

Materials ◽  
2018 ◽  
Vol 12 (1) ◽  
pp. 50 ◽  
Author(s):  
Zhichao Dong ◽  
Yabo Liu ◽  
Weibin Wen ◽  
Jingran Ge ◽  
Jun Liang
Keyword(s):  
Stainless Steel ◽  
Selective Laser Melting ◽  
Three Dimensional ◽  
Element Model ◽  
Laser Melting ◽  
Melt Pool ◽  
Simulation Results ◽  
Melt Pool Size ◽  
Overlap Rate ◽  
Hatch Spacing

In this study, a combined simulation and experimental approach is utilized to investigate the influence of hatch spacing on the microstructure and as-built quality of 316L stainless steel (SS) samples fabricated by selective laser melting (SLM). A three-dimensional finite element model (FEM) is employed to investigate heat transfer and melt pool during the SLM of 316L SS. The phase transformation and variation of the thermo-physical properties of the materials are considered in this model. The effects of hatch spacing (H) on the temperature field, microstructure and melt pool size, overlap rate, surface quality, and relative density during the SLM of 316L SS are investigated. The simulated results indicate that, as the hatch spacing increases, the depth increases and the width of the melt pool decreases. Meanwhile, with the increase of hatch spacing, the simulated temperature of the subsequent tracks falls below the melting temperature of the first track. Moreover, the microstructures were found to coarsen with the increasing hatch spacing due to the reduced cooling rate. The optimized hatch spacing and overlap rate between adjacent tracks were obtained from numerical simulations. Simulation results illustrate that, when the optimized hatch spacing of 100 μm is adopted, fully dense parts with a smooth surface can be fabricated by SLM, thus experimentally validating the simulation results.

In-situ capture of melt pool signature in selective laser melting using U-Net-based convolutional neural network

2021 ◽  
Vol 68 ◽  
pp. 347-355
Author(s):  
Qihang Fang ◽  
Zhenbiao Tan ◽  
Hui Li ◽  
Shengnan Shen ◽  
Sheng Liu ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Selective Laser Melting ◽  
Laser Melting ◽  
Melt Pool

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 Prediction of lack of fusion porosity in selective laser melting based on melt pool monitoring data

2019 ◽  
Vol 25 ◽  
pp. 347-356 ◽  
Author(s):  
Sam Coeck ◽  
Manisha Bisht ◽  
Jan Plas ◽  
Frederik Verbist
Keyword(s):  
Selective Laser Melting ◽  
Monitoring Data ◽  
Laser Melting ◽  
Lack Of Fusion ◽  
Melt Pool
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