Direct printing of miniscule aluminum alloy droplets and 3D structures by StarJet technology

Gerdes, B.; Zengerle, Roland; Koltay, Peter; Riegger, L.

doi:10.1088/1361-6439/aab928

Cited by 8 publications

(4 citation statements)

References 26 publications

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“…The relative motion of the nozzle and substrate creates a three-dimensional part. The momentum pulses required to create and control the rate and size of ejected droplets can be achieved piezoelectrically (Luo et al, 2016a;Wang et al, 2017Wang et al, , 2018, magneto-hydrodynamically (Simonelli et al, 2019;Sukhotskiy et al, 2017Sukhotskiy et al, , 2021, electro-hydrodynamically (Han & Dong, 2017a, b), pneumatically (Beck et al, 2020;Chang et al, 2020;Gerdes et al, 2018), electromagnetically (Luo et al, 2016b), or with a laser (Stein et al, 2018).…”

Section: Motivation and Objectivementioning

confidence: 99%

In-process monitoring and prediction of droplet quality in droplet-on-demand liquid metal jetting additive manufacturing using machine learning

et al. 2022

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In droplet-on-demand liquid metal jetting (DoD-LMJ) additive manufacturing, complex physical interactions govern the droplet characteristics, such as size, velocity, and shape. These droplet characteristics, in turn, determine the functional quality of the printed parts. Hence, to ensure repeatable and reliable part quality it is necessary to monitor and control the droplet characteristics. Existing approaches for in-situ monitoring of droplet behavior in DoD-LMJ rely on high-speed imaging sensors. The resulting high volume of droplet images acquired is computationally demanding to analyze and hinders real-time control of the process. To overcome this challenge, the objective of this work is to use time series data acquired from an in-process millimeter-wave sensor for predicting the size, velocity, and shape characteristics of droplets in DoD-LMJ process. As opposed to high-speed imaging, this sensor produces data-efficient time series signatures that allows rapid, real-time process monitoring. We devise machine learning models that use the millimeter-wave sensor data to predict the droplet characteristics. Specifically, we developed multilayer perceptron-based non-linear autoregressive models to predict the size and velocity of droplets. Likewise, a supervised machine learning model was trained to classify the droplet shape using the frequency spectrum information contained in the millimeter-wave sensor signatures. High-speed imaging data served as ground truth for model training and validation. These models captured the droplet characteristics with a statistical fidelity exceeding 90%, and vastly outperformed conventional statistical modeling approaches. Thus, this work achieves a practically viable sensing approach for real-time quality monitoring of the DoD-LMJ process, in lieu of the existing data-intensive image-based techniques.

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Section: Motivation and Objectivementioning

confidence: 99%

In-process monitoring and prediction of droplet quality in droplet-on-demand liquid metal jetting additive manufacturing using machine learning

et al. 2022

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“…Precise drop-on-demand printing is a low-cost, contactless, and environmentally friendly 3D printing technology. Droplets of wax 4 , solder 5,6 , aluminium 7 , gold 8 , copper 9,10 , and nano-particle suspension formulation 11,12 have been utilized to print structures with thin-walls and high aspect ratios 13 . Already, thin horizontal lines 5 , vertical 14 and inclined pillars 15 , slender walls 13,16 , and programmable 3D shapes 17 have been demonstrated to illustrate the droplet printing ability.…”

Section: Introductionmentioning

confidence: 99%

Omnidirectional 3D printing of metal micro-architectures via droplet sliding over curved surfaces

Luo¹,

Cheng²,

Dong³

et al. 2021

Preprint

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As a simple, fast, and green 3D printing method, droplet-based manufacturing is suitable to print various materials to manufacture structures. However, up to now, it has hardly been capable of manufacturing omnidirectional designs with slender horizontal or upside-down sections, since the required rotation of the nozzle or workpiece negatively affects printing performance. Here, we employed a novel slide-guided droplet deposition method for printing parts with arbitrary-angle overhangs up to 180o. Arc-shaped slides with metallophobic surfaces are used to smoothly deflect droplets and provide a full control of droplets’ impact angle onto the workpiece. We show that gently curved impact surfaces prevent droplet bouncing, and quantitatively describe the transition from sliding to bouncing by measurements, simulations, and theory that predict our process window. Furthermore, the temperature of the slides is controlled to modify the temperature of droplets after they leave the generator, enabling fabrication of horizontal pillars with locally tunable morphology and optimization of drop-to-drop adhesion. The versatility of the slide-guided deposition is highlighted by fabricating structures with high aspect ratios and free-standing branches in arbitrary angles including hung sections where droplets are deposited from a fully reversed direction. Since the proposed slide-guided deposition uniquely facilitates fabrication of slender metal structures from arbitrary angles without rotating the workpiece or nozzle, it helps advance the development of many other research fields, such as antennas, branch-like heat sinks, and metal micro-lattices, where complex structures are needed.

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“…The rinse gas also supports the precise droplet deposition from a large distance of several millimeters up to two centimeters to control thermal impact on 3D structures as well as on polymer substrates, while still providing high printing precision. Although the StarJet technology has been successfully applied to electrical interconnections on PCBs [15] and flexible polymer foils [16], metallization for solar cells [17], and 3D metallic structures [11], [18], there is still no systematic analysis of the interaction of molten solder droplets on commonly used electronic contact pads having an ENIG (electroless nickel immersion gold) finishing. Therefore, this paper is intended to provide a first comprehensive study of the adhesion properties of solder bumps printed via the contact-free drop-on-demand StarJet technology on ENIG surfaces, commonly used in microelectronics for PCB and chip metallization.…”

Section: Introductionmentioning

confidence: 99%

Direct Drop-on-Demand Printing of Molten Solder Bumps on ENIG Finishing at Ambient Conditions Through StarJet Technology

et al. 2020

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Direct printing of miniscule aluminum alloy droplets and 3D structures by StarJet technology

Cited by 8 publications

References 26 publications

In-process monitoring and prediction of droplet quality in droplet-on-demand liquid metal jetting additive manufacturing using machine learning

In-process monitoring and prediction of droplet quality in droplet-on-demand liquid metal jetting additive manufacturing using machine learning

Omnidirectional 3D printing of metal micro-architectures via droplet sliding over curved surfaces

Direct Drop-on-Demand Printing of Molten Solder Bumps on ENIG Finishing at Ambient Conditions Through StarJet Technology

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