Machine vision assisted micro-filament detection for real-time monitoring of electrohydrodynamic inkjet printing

Lies, Benjamin; Cai, Yi; Spahr, Eric; Lin, Kevin Y.; Qin, Hantang

doi:10.1016/j.promfg.2018.07.004

Cited by 14 publications

(5 citation statements)

References 31 publications

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“…However, because of the impact of many changing factors during printing, such as temperature fluctuations, solvent evaporation, and random variations, the developed stationary machine learning strategies for process optimization may not suffice for printing quality assurance. Therefore, it is necessary to further integrate the advantages of in situ monitoring − and closed-loop control , for in-process diagnosis and online optimization without manual intervention, which will be helpful to ensure process stability and optimization efficiency during printing. As shown in Figure , a systematic process optimization approach for noncontact direct ink writing consists of three main parts: (1) in situ monitoring, (2) online predictive modeling, and (3) closed-loop control.…”

Section: Discussionmentioning

confidence: 99%

“…Under such circumstances, as the droplet behaviors extensively define the final produced line morphology in IJP, machine vision systems have been employed to inspect the dynamic droplet dispensing behavior in real time, which is beneficial to the formed line morphology. For example, Lies et al developed an in situ inspection system for e-jet printing using a real-time image processing technology, but the proposed machine vision system was limited to detect whether a filament is printed or not. Moreover, a droplet location detection algorithm was coupled with an online monitoring system to ensure the process repeatability and reproducibility; however, the undetected droplet shape may influence the produced line morphology.…”

Section: Machine Learning Approaches For Process Optimizationmentioning

confidence: 99%

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Reviews on Machine Learning Approaches for Process Optimization in Noncontact Direct Ink Writing

Zhang

Moon

2021

ACS Appl. Mater. Interfaces

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Recently, machine learning has gained considerable attention in noncontact direct ink writing because of its novel process modeling and optimization techniques. Unlike conventional fabrication approaches, noncontact direct ink writing is an emerging 3D printing technology for directly fabricating low-cost and customized device applications. Despite possessing many advantages, the achieved electrical performance of produced microelectronics is still limited by the printing quality of the noncontact ink writing process. Therefore, there has been increasing interest in the machine learning for process optimization in the noncontact direct ink writing. Compared with traditional approaches, despite machine learning-based strategies having great potential for efficient process optimization, they are still limited to optimize a specific aspect of the printing process in the noncontact direct ink writing. Therefore, a systematic process optimization approach that integrates the advantages of state-of-the-art machine learning techniques is in demand to fully optimize the overall printing quality. In this paper, we systematically discuss the printing principles, key influencing factors, and main limitations of the noncontact direct ink writing technologies based on inkjet printing (IJP) and aerosol jet printing (AJP). The requirements for process optimization of the noncontact direct ink writing are classified into four main aspects. Then, traditional methods and the state-of-the-art machine learning-based strategies adopted in IJP and AJP for process optimization are reviewed and compared with pros and cons. Finally, to further develop a systematic machine learning approach for the process optimization, we highlight the major limitations, challenges, and future directions of the current machine learning applications.

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Section: Discussionmentioning

confidence: 99%

Section: Machine Learning Approaches For Process Optimizationmentioning

confidence: 99%

Reviews on Machine Learning Approaches for Process Optimization in Noncontact Direct Ink Writing

Zhang

Moon

2021

ACS Appl. Mater. Interfaces

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“…[ 146–148 ] An additional opportunity is the utilization of thermal cameras for monitoring the temperature to support extrusion of sensitive materials [ 149 ] or inkjet bioprinting. [ 150,151 ] All the proposed solutions focused on the inspection of the bioprinted construct (layer‐wise analysis) or the extruder conditions (layer‐height analysis) (both Figure ) to achieve first‐time‐right printing and possibly develop a digital platform implementing data mining to support monitoring (i.e., detection of unexpected events), feedback control (i.e., acting on process parameters to maintain process stability) or combining a virtual simulation of the process towards digital twin solutions (Figure 9).…”

Section: Online Monitoring Bioreactors and Remote Controlmentioning

confidence: 99%

3D Bioprinting in Microgravity: Opportunities, Challenges, and Possible Applications in Space

Ombergen

Chalupa-Gantner

Chansoria

et al. 2023

Adv Healthcare Materials

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Abstract3D bioprinting has developed tremendously in the last couple of years and enables the fabrication of simple, as well as complex, tissue models. The international space agencies have recognized the unique opportunities of these technologies for manufacturing cell and tissue models for basic research in space, in particular for investigating the effects of microgravity and cosmic radiation on different types of human tissues. In addition, bioprinting is capable of producing clinically applicable tissue grafts, and its implementation in space therefore can support the autonomous medical treatment options for astronauts in future long term and far‐distant space missions. The article discusses opportunities but also challenges of operating different types of bioprinters under space conditions, mainly in microgravity. While some process steps, most of which involving the handling of liquids, are challenging under microgravity, this environment can help overcome problems such as cell sedimentation in low viscous bioinks. Hopefully, this publication will motivate more researchers to engage in the topic, with publicly available bioprinting opportunities becoming available at the International Space Station (ISS) in the imminent future.

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“…The most widely used is the automated visual inspection (AVI). Several researchers documented their research that the AVI system was repeatable, and it could also be applied at a very high speed to evaluate the detected object [26,27]. The steps for applying Machine vision are divided into three stages, selecting a camera, selecting a lens, and selecting lighting [28].…”

Section: Machine Visionmentioning

confidence: 99%

Development of Machine Vision to Increase the Level of Automation in Indonesia Electronic Component Industry

Herlambang¹,

Purba²,

Jaqin³

2021

JESA

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Human involvement in the assembly part manufacturing process is still relatively high. However, automation solutions are not flexible enough to be applied to manufacturing systems. It is essential to evaluate each work activity so that automation can be implemented effectively. We developed an automatic vision inspection using machine vision. The level of automation (LoA) in the company increases, and the impact caused by process failures on manual systems can be eliminated during inspection activities. The automation level increase in the inspection area is described and analyzed using the Hierarchy Task Analysis (HTA). Inspection data process activity and quality data are collected to determine the CCD camera selection, lamp selection, and lens selection. Three quality objectives, such as geometric quality, surface quality, and structural quality, are identified automatically using machine vision. Furthermore, after applying machine vision, an analysis of current LoA conditions and future LoA conditions is carried out. The results showed that the application of machine vision could increase the Level of Automation in the product inspection activity by 81.8%. There is a strong correlation (R = 0.924) between manual measurements carried out by operators and machine vision.

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Machine vision assisted micro-filament detection for real-time monitoring of electrohydrodynamic inkjet printing

Cited by 14 publications

References 31 publications

Reviews on Machine Learning Approaches for Process Optimization in Noncontact Direct Ink Writing

Reviews on Machine Learning Approaches for Process Optimization in Noncontact Direct Ink Writing

3D Bioprinting in Microgravity: Opportunities, Challenges, and Possible Applications in Space

Development of Machine Vision to Increase the Level of Automation in Indonesia Electronic Component Industry

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