scholarly journals Measuring the melt pool size in a wire arc additive manufacturing process using a high dynamic range two-colored pyrometric camera

2020 ◽  
Vol 64 (8) ◽  
pp. 1349-1356
Author(s):  
C. Halisch ◽  
T. Radel ◽  
D. Tyralla ◽  
T. Seefeld
Keyword(s):  
Additive Manufacturing ◽  
Manufacturing Process ◽  
Dynamic Range ◽  
High Dynamic Range ◽  
Pool Size ◽  
Melt Pool ◽  
High Dynamic ◽  
Melt Pool Size ◽  
Wire Arc Additive Manufacturing

scholarly journals Development of Defect Detection AI Model for Wire + Arc Additive Manufacturing Using High Dynamic Range Images

2021 ◽  
Vol 11 (16) ◽  
pp. 7541
Author(s):  
Chaekyo Lee ◽  
Gijeong Seo ◽  
Duckbong Kim ◽  
Minjae Kim ◽  
Jong-Ho Shin
Keyword(s):  
Additive Manufacturing ◽  
Arc Welding ◽  
Dynamic Range ◽  
Welding Process ◽  
High Dynamic Range ◽  
Detection Accuracy ◽  
High Deposition Rate ◽  
Correct Judgment ◽  
High Dynamic ◽  
Wire Arc Additive Manufacturing

Wire + arc additive manufacturing (WAAM) utilizes a welding arc as a heat source and a metal wire as a feedstock. In recent years, WAAM has attracted significant attention in the manufacturing industry owing to its advantages: (1) high deposition rate, (2) low system setup cost, (3) wide diversity of wire materials, and (4) sustainability for constructing large-sized metal structures. However, owing to the complexity of arc welding in WAAM, more research efforts are required to improve its process repeatability and advance part qualification. This study proposes a methodology to detect defects of the arch welding process in WAAM using images acquired by a high dynamic range camera. The gathered images are preprocessed to emphasize features and used for an artificial intelligence model to classify normal and abnormal statuses of arc welding in WAAM. Owing to the shortage of image datasets for defects, transfer learning technology is adopted. In addition, to understand and check the basis of the model’s feature learning, a gradient-weighted class activation mapping algorithm is applied to select a model that has the correct judgment criteria. Experimental results show that the detection accuracy of the metal transfer region-of-interest (RoI) reached 99%, whereas that of the weld-pool and bead RoI was 96%.

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.

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.

Microfluidic Manifolds with High Dynamic Range in Structural Dimensions Replicated in Thermoplastic Materials

2009 ◽  
Vol 1191 ◽  
Author(s):  
Holger Becker ◽  
Erik Beckert ◽  
Claudia Gärtner
Keyword(s):  
Structural Dynamics ◽  
Manufacturing Process ◽  
Dynamic Range ◽  
Single Point ◽  
Mold Insert ◽  
High Dynamic Range ◽  
Diamond Turning ◽  
Precision Machining ◽  
Wetting Properties ◽  
High Dynamic

AbstractIn this paper, we present the manufacturing process of a polymer microfluidic device which is currently being used to investigate wetting properties of nanostructured microchannels replicated in hydrophobic thermoplastic materials like cyclo-olefin co-polymer (COC), polypropylene (PP) or polymethylmetacrylate (PMMA). These devices feature large structural dynamics (feature sizes between 200 μm and 200 nm). The mold insert necessary was fabricated using a combination of precision machining with single-point diamond turning (SPDT).

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.

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.

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%).

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