Unsupervised learning for the droplet evolution prediction and process dynamics understanding in inkjet printing

Huang, Jida; Segura, Luis Javier; Wang, Tianjiao; Zhao, Guanglei; Sun, Hongyue; Zhou, Chi

doi:10.1016/j.addma.2020.101197

Cited by 46 publications

(31 citation statements)

References 42 publications

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“…Inkjet printing is a technology that is used mainly in pattern making. is method is widely accepted for variety of applications as it is highly environment friendly, cheap, and flexible [6][7][8][9] with all kinds of substrates such as paper, silicon, and mica sheets.…”

Section: Introductionmentioning

confidence: 99%

[Retracted] Development of Novel Nano‐Silver‐Based Antenna for Green Agriculture

Subitha

Vani

Raja

et al. 2021

Advances in Materials Science and Engineering

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The designed antenna is a monopole Z-shaped antenna operating in an unlicensed band of 2.4 GHz fabricated using low cost inkjet printing technology. The proposed inkjet printing technology is eco-friendly since the material used here is an ordinary “paper” that is suitable for the green technology. The conducting patch of silver nanoparticle (AgNP) ink has very high conductivity 35,700,000 s/m and instant curing property which helps in fabrication process without UV curing or oven heating. The printer used also is the cheap home printer HP DJ 2130 rather than the expensive Brother and Epson printers used in previous works. The printed antenna will be helpful in conditions to ascertain its performance in green agriculture in the form of RF-ID sensors, soil pH value sensor, and moisture sensor. The proposed antenna attains the gain of 2.5 dBi at the ISM band of 2.4 GHz with optimal VSWR value between 1 and 2 over the desired frequency band. The directivity and radiation efficiency of the proposed antenna are 2 dBi and 80%, respectively. The overall cost of the proposed antenna is much lesser in the order of 10 times than the recent low cost design.

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Section: Introductionmentioning

confidence: 99%

[Retracted] Development of Novel Nano‐Silver‐Based Antenna for Green Agriculture

Subitha

Vani

Raja

et al. 2021

Advances in Materials Science and Engineering

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show abstract

“…Unsupervised learning techniques are also developed and used with AM processes other than FFF, such as L-PBF and inkjet printing. 10,82 In one study, six representative anomalies, including recoater hopping, recoater streaking, debris, super-elevation, part failure, and incomplete spreading, were examined for the L-PBF process, with the image of each case 10 shown in Figure 4G. The model applied a filter bank to the input images and obtained a dictionary based on the clustering of the filter response.…”

Section: Real-time Anomaly Detection Using Novel Image-processing Metmentioning

confidence: 99%

Machine Learning for Advanced Additive Manufacturing

Jin

Zhang

Demir

et al. 2020

Matter

150

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“…In an example of inkjet printing, such a model serves as a powerful predictor of the ejected droplets' dynamics after deposition and its interactions with substrate, while requiring minimal human interventions. [149] In this study, unsupervised deep learning was used to examine droplet evolution from video data of the droplet ejection. Video data, as opposed to static image data, could be used due to the lower computational resource requirement and unsupervised nature of the method, which is a strong advantage as image data does not include temporal domain information.…”

Section: Tool Path Planning To Achieve Full Densitymentioning

confidence: 99%

Data‐Driven Approaches Toward Smarter Additive Manufacturing

Tian

Bustillos

et al. 2021

Advanced Intelligent Systems

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The latest industrial revolution, Industry 4.0, is driven by the emergence of digital manufacturing and, most notably, additive manufacturing (AM) technologies. The simultaneous material and structure forming in AM broadens the material and structural design space. This expanded design space holds a great potential in creating improved engineering materials and products that attract growing interests from both academia and industry. A major aspect of this growing interest is reflected in the increased adaptation of data‐driven tools that accelerate the exploration of the vast design space in AM. Herein, the integration of data‐driven tools in various aspects of AM is reviewed, from materials design in AM (i.e., homogeneous and composite material design) to structure design for AM (i.e., topology optimization). The optimization of AM tool path using machine learning for producing best‐quality AM products with optimal material and structure is also discussed. Finally, the perspectives on the future development of holistically integrated frameworks of AM and data‐driven methods are provided.

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Unsupervised learning for the droplet evolution prediction and process dynamics understanding in inkjet printing

Cited by 46 publications

References 42 publications

[Retracted] Development of Novel Nano‐Silver‐Based Antenna for Green Agriculture

[Retracted] Development of Novel Nano‐Silver‐Based Antenna for Green Agriculture

Machine Learning for Advanced Additive Manufacturing

Data‐Driven Approaches Toward Smarter Additive Manufacturing

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