Minimization of cross-talk for an inkjet printhead using MIMO ILC

Wassink, M.B. Groot; Bosgra, О.H.; Koekebakker, Sjirk

doi:10.1109/acc.2006.1655483

Cited by 6 publications

(4 citation statements)

References 12 publications

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“…In addition, the residual vibration and cross-talk effect could also be removed by the iterative learning control (e.g. Oosten et al [187], Wassink et al [188,189] and Velden et al [190]), and the droplet velocity and volume variations were reduced by 52% and 41% in terms of the standard deviation. A novel closed-loop control method based on adaptive wavelet neural network controller was proposed, and this method not only outperformed the PID control but also worked well in the performance optimization [191].…”

Section: Additive and Other Control Strategymentioning

confidence: 99%

A review on droplet-based 3D printing with piezoelectric micro-jet device

Feng,

Liu,

Deng

et al. 2024

Smart Mater. Struct.

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The droplet-based 3D printing with piezoelectric micro-jet device (PMJD), also known as fabrication with droplet ejection, has shown its unique advantages exceeding typical processing in direct rapid manufacturing (DRM) of miniature and complicated metal parts, function components, and other heterogeneous devices. Recently, the droplet-based 3D printing with higher printing velocity, controllable precision and smaller printing volume is need more and more in additive manufacturing (AM) fields, which promotes the development of the droplet-based printing technology to a faster, accurate and flexible direction. In this regard, the droplet-based 3D printing with PMJD has received a lot of attentions from universities, enterprises, and other research institutes. In this review, based on introducing the droplet-based 3D printing technologies, we will clarify the advantages of droplet-based 3D printing, especially the droplet ejection with PMJD. Then, the connections and differences between the ejection of non-metallic droplets and metal droplets based on piezoelectric micro-jet are also clarified, meanwhile, the challenges, such as the consistency, reliability, and stability of piezoelectric actuated droplet ejection, are concluded and analyzed. In general, the future developments of droplet-based 3D printing technology are prospected, and the review can provide some further references for the related works.

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Section: Additive and Other Control Strategymentioning

confidence: 99%

A review on droplet-based 3D printing with piezoelectric micro-jet device

Feng,

Liu,

Deng

et al. 2024

Smart Mater. Struct.

View full text Add to dashboard Cite

show abstract

“…Data-driven control learning strategies enables high performance for industrial motion systems based on measured data from previous motion tasks (Blanken et al, 2018). Iterative learning control strategies can encompass the mentioned range of objectives and contribute to addressing the stated issues (Koçan, 2020;Blanken et al, 2017Blanken et al, , 2016Bolder, 2015;Oomen, 2018;Bristow et al, 2006;Boeren et al, 2016;Oomen, 2017;Strijbosch et al, 2022;Barton et al, 2011;Wallen et al, 2011;Bolder et al, 2014;Strijbosch et al, 2019;van der Meulen et al, 2008;Oomen et al, 2017;Son et al, 2016;Oomen et al, 2024;Groot et al, 2006;Barton et al, 2008).…”

Section: Introductionmentioning

confidence: 99%

“…After each repetition or iteration, the control action for the next iteration is improved by learning from the error observed in past iterations (Strijbosch et al, 2022). Many successful applications have been reported (Strijbosch et al, 2022), including additive manufacturing machines (Barton et al, 2011), robotic arm (Wallen et al, 2011, printing systems (Bolder et al, 2014;Groot et al 2006), pick-and-place machines, electron microscopes (Strijbosch et al, 2019), and wafer stages (van der Meulen et al, 2008;Oomen et al, 2017). Typical ILC design approaches that have been successfully implemented should have favourable properties (Stribosch et al, 2022;Son et al, 2016;Oomen et al, 2024;Barton, et al, 2008), including an explicit learning update, instead of performing an optimization at each iteration (Oomen et al, 2017;Strijbosch et al, 2022), and achieving monotonic convergence in an appropriate norm of either the sequence of control inputs or the sequence of error signals (Son et al, 2016;Strijbosch et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Using Iterative Learning Control in a Walking Beam Motion System with a Flexible Coupled Robotic Mechanism

Kostić,

Matijević

2024

Preprint

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The paper proposes a modification to enhance the performance of the control structure with the norm-optimal iterative learning control (ILC) algorithm for precise positioning of the gripper tip of a robotic mechanism that is flexibly coupled to an industrial walking beam motion system. For feedback systems involving controlled plants with unstable or poorly damped poles, it is more suitable to use a serial norm-optimal ILC controller, which may exhibit a tendency towards controller fragility and/or the generation of aggressive and excessive changes in control signals. To address this issue, adjustments to the implementation of the feedback controller are discussed, and modifications to the synthesis of the overall controller structure are proposed. This is done to prevent controller fragility and ensure achievable dynamics of the control variable without significantly impacting the attained system performance. Simulation verification is performed using the Matlab/Simulink software package by developing a nonlinear plant model in Simscape/Simulink, providing comprehensive insights into the system's behavior.

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“…The small-scale motions are relevant, as they set the initial shape and velocity of the meniscus which may lead to asymmetries or variations in the droplet properties, e.g., drop volume and velocity. These motions are often discussed in terms of cross-talk, [24][25][26] where the actuation of neighboring nozzles set the liquid in motion. However, these motions can also result from residual pressure oscillation in the very same nozzle.…”

Section: Introductionmentioning

confidence: 99%

Experimental Techniques for Retrieving Flow Information from within Inkjet Nozzles

Meulen¹,

Reinten²,

Dijksman³

et al. 2016

jist

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In drop-on-demand (DOD) inkjet printing microdroplets are ejected from the nozzle as a result of the internal acoustics set in motion by a pressure pulse from an expanding bubble or a piezoelement. The acoustic response, both in the frequency domain and in the time domain, and the resulting droplet formation processes are well-modeled and characterized by various experimental techniques. However, the behavior of the liquid meniscus in the nozzle is a critical mediator between these regimes and poorly accessible in experiment. The meniscus shape and motion vary between different print head designs, electrical pulse shapes and wetting conditions. In the last decade several novel approaches have been proposed and implemented to study experimentally the meniscus motion within inkjet nozzles. These experimental methods are reviewed here and compared in terms of accuracy and applicability.

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Minimization of cross-talk for an inkjet printhead using MIMO ILC

Cited by 6 publications

References 12 publications

A review on droplet-based 3D printing with piezoelectric micro-jet device

A review on droplet-based 3D printing with piezoelectric micro-jet device

Using Iterative Learning Control in a Walking Beam Motion System with a Flexible Coupled Robotic Mechanism

Experimental Techniques for Retrieving Flow Information from within Inkjet Nozzles

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