In-situ high-speed X-ray imaging of piezo-driven directed energy deposition additive manufacturing

Wolff, Sarah; Wu, Hong‐Ke; Parab, Niranjan D.; Zhao, Cang; Ehmann, Kornel F.; Sun, Tao; Cao, Jian

doi:10.1038/s41598-018-36678-5

Cited by 115 publications

(43 citation statements)

References 39 publications

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“…Recent developments in quality monitoring includes high-energy X-ray synchrotron studies of DED. These encompass: High energy synchrotron X-ray source and high speed imaging camera used in tandem to detect the in situ melt pool geometries and deduce the phase transformations of Ti-6Al-4V [108]; a piezo driven powder delivery in conjunction with a laser heat source to investigate the powder-melt pool interaction during printing of Ti-6Al-4V [109]. These studies provide insights into the DED process physics, but are still far from mimicking all the components in a real DED system.…”

Section: Ded Process Control and Monitoringmentioning

confidence: 99%

“…Detects phase transformations and melt pool dynamics [73,108,109] Repetitive process controller Used to optimize layer height during the process [119] 6. Determination of Optimal Process Parameters for Laser Based Powder-Fed DED DED is an emerging field in the area of metal AM, and our goal was to create efficient process maps which provide a holistic picture of the DED process parameters.…”

Section: Study Technique Function Reported Literaturementioning

confidence: 99%

See 1 more Smart Citation

State of the Art in Directed Energy Deposition: From Additive Manufacturing to Materials Design

2019

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Additive manufacturing (AM) is a new paradigm for the design and production of high-performance components for aerospace, medical, energy, and automotive applications. This review will exclusively cover directed energy deposition (DED)-AM, with a focus on the deposition of powder-feed based metal and alloy systems. This paper provides a comprehensive review on the classification of DED systems, process variables, process physics, modelling efforts, common defects, mechanical properties of DED parts, and quality control methods. To provide a practical framework to print different materials using DED, a process map using the linear heat input and powder feed rate as variables is constructed. Based on the process map, three different areas that are not optimized for DED are identified. These areas correspond to the formation of a lack of fusion, keyholing, and mixed mode porosity in the printed parts. In the final part of the paper, emerging applications of DED from repairing damaged parts to bulk combinatorial alloys design are discussed. This paper concludes with recommendations for future research in order to transform the technology from “form” to “function,” which can provide significant potential benefits to different industries.

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Section: Ded Process Control and Monitoringmentioning

confidence: 99%

Section: Study Technique Function Reported Literaturementioning

confidence: 99%

State of the Art in Directed Energy Deposition: From Additive Manufacturing to Materials Design

2019

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“…In a stable state (Fig. 3A, t = 3.5 ms), the keyhole was characterized by a cone-shaped channel, which was wider near the surface and narrowed in depth 11,20,[42][43][44] . As it contains mostly overheated material vapor, the laser beam can propagate deeper inside the material.…”

Section: Resultsmentioning

confidence: 99%

Supervised deep learning for real-time quality monitoring of laser welding with X-ray radiographic guidance

Shevchik

Le-Quang

Meylan

et al. 2020

Sci Rep

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Laser welding is a key technology for many industrial applications. However, its online quality monitoring is an open issue due to the highly complex nature of the process. this work aims at enriching existing approaches in this field. We propose a method for real-time detection of process instabilities that can lead to defects. Hard X-ray radiography is used for the ground truth observations of the sub-surface events that are critical for the quality. A deep artificial neural network is applied to reveal the unique signatures of those events in wavelet spectrograms from the laser back-reflection and acoustic emission signals. The autonomous classification of the revealed signatures is tested on reallife data, while the real-time performance is reached by means of parallel computing. The confidence of the quality classification ranges between 71% and 99%, with a temporal resolution down to 2 ms and a computation time per classification task as low as 2 ms. This approach is a new paradigm in the digitization of industrial processes and can be exploited to provide feedbacks in a closed-loop quality control system. The introduction of laser technology in metal welding of metals is dated back to the late 1960s 1,2 when it immediately showed advantages as compared to traditional arc welding 3. The attractions of this technique are in the non-contact processing, the absence of tool wear, high aspect ratio of the melt pool, better material fusion, possibility to process refractory materials, low running costs and high processing speed 3,4. Today, laser welding is a key technology in many fields e.g. automotive 5 and aerospace 3,6 industries, naval and heavy machinery production 7 , medicine and micromechanics 3. Unfortunately, the potential of this technology is not fully exploited, particularly in applications that require the guarantee of high weld quality. The reason is the non-linear nature of light-matter interactions, which complicates the reproducibility of the weld quality in mass production 8-10. The complex dynamics of the process, especially in keyhole welding regime, and its instabilities can cause various defects at the joint 3,10-12. A defect type of particular interest is porosity, which is a hidden threat for the mechanical properties of the workpieces 3,9-11. Obviously, an adequate, robust and low cost quality monitoring system is of great desire. The major challenge in developing such technique is in the difficulties to inspect directly the sub-surface behavior of the process zone in real-life conditions 13. Multiple approaches have been proposed, which are mostly based on mathematical modeling aiming to reconstruct the under surface dynamics using inspections of the surface via measurements of temperature 11,12,14,15 , optical 16,17 and/or acoustic 18,19 emissions (AE). However, those approaches face three main problems. Firstly, modeling often suffers inaccuracies originating from the deviations of the model assumptions from the real parameters' values. More complicated assumptions can be used to imp...

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“…The high-contrast imaging of various test objects clearly showed the defects in AM, and the possibility of field measurement of AM in the future was discussed. Wolff et al 56 performed field high-speed x-ray imaging of individual powder particles flowing into the molten pool, and the effect of beam-matter interaction on flow and pore formation of powder was revealed. In addition, Sharples et al 57 developed a spatially resolved acoustic spectroscopy (SRAS) system, which is a laser ultrasound inspection technique for imaging the material microstructure of metals.…”

Section: Other Monitoringmentioning

confidence: 99%

Review on adaptive control of laser-directed energy deposition

et al. 2020

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Laser directed energy deposition (LDED) is one of the most important parts of metal additive manufacturing, which can provide fast building speed, allows for large building volumes, and is suitable for part repair. LDED can manufacture components layer by layer through processes of rapid heating, melting, solidification, and cooling with the laser beam as a heat source. However, deposition quality and repeatability of components produced by LDED are poor because of the complex thermal cycle and processing environment, hindering the spread of this technique. Adaptive control technology (ACT) is consistently considered an effective and potential way to solve the problem. Many studies have focused on LDED and established the relations of process parameters, process signatures, and product qualities, which promote the rapid development of ACT, with the development of monitoring devices and data processing technology. We review and discuss the problems existing in the ACT of LDED.

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In-situ high-speed X-ray imaging of piezo-driven directed energy deposition additive manufacturing

Cited by 115 publications

References 39 publications

State of the Art in Directed Energy Deposition: From Additive Manufacturing to Materials Design

State of the Art in Directed Energy Deposition: From Additive Manufacturing to Materials Design

Supervised deep learning for real-time quality monitoring of laser welding with X-ray radiographic guidance

Review on adaptive control of laser-directed energy deposition

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