Effect of pulse overlapping on temperature field and physical characteristics in pulsed laser sintering of inkjet-printed silver nanoparticles

Hussain, Arif; Lee, Hee-Lak; Moon, Yoon-Jae; Hwang, Jong Yeon; Moon, Seung-Jae

doi:10.1016/j.ijheatmasstransfer.2022.123678

Cited by 8 publications

(4 citation statements)

References 49 publications

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“…65 The viscosity (η) and surface tension (γ) of the ink were 10 cP and 35 mN/m, respectively. 66 Two nozzles were activated with varying pulse amplitudes from 22 to 40 V by increments of 0.5 V to induce drop and satellite in a jetting. 10,127 multijet frames were labeled frame by frame using Roboflow and split into 85% for training, 10% for validation, and 5% for test sets.…”

Section: ■ Methodsmentioning

confidence: 99%

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Deep Learning-Based Inkjet Droplet Detection for Jetting Characterizations and Multijet Synchronization

Choi,

et al. 2024

ACS Appl. Mater. Interfaces

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Inkjet printing is a powerful direct material writing process. It can be used to deposit microfluidic droplets in designated patterns at submicrometer resolution, which reduces materials usage. Nonetheless, predicting jetting characterizations is not easy because of the intrinsic complexity of the ink−nozzle−air interactions. Thus, inkjet processes are monitored by skilled engineers to ensure process reliability. This is a bottleneck in industry, resulting in high labor costs for multiple nozzles. To address this, we present a deep learning-based method for jetting characterizations. Inkjet printing is recorded by an in situ CCD camera and each droplet is detected by YOLOv5, a 1-stage detector using a convolutional neural network (CNN). The precision, recall, and mean average precision (mAP) at a 0.5 intersection over the union (IoU) threshold of the trained model were 0.86, 0.89, and 0.90, respectively. Each regression result for a detected droplet is accumulated in chronological order for each class of droplet and nozzle. The quantified information includes velocity, diameter, length, and translation, which can be used to synchronize multinozzle jetting and, eventually, the printed patterns. This demonstrates the feasibility of autonomous real-time process testing for large-scale electronics manufacturing, such as the high-resolution patterning of biosensor electrodes and QD display pixels while exploiting big data obtained from jetting characterizations.

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Section: ■ Methodsmentioning

confidence: 99%

“…The density (ρ) of AgNP ink was 5.4 g/mL . The viscosity (η) and surface tension (γ) of the ink were 10 cP and 35 mN/m, respectively . Two nozzles were activated with varying pulse amplitudes from 22 to 40 V by increments of 0.5 V to induce drop and satellite in a jetting.…”

Section: Methodsmentioning

confidence: 99%

Deep Learning-Based Inkjet Droplet Detection for Jetting Characterizations and Multijet Synchronization

Choi,

et al. 2024

ACS Appl. Mater. Interfaces

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“…The term "overlapping" in pulsed laser processing pertains to the extent to which consecutive laser pulses coincide on the specific area of the surface undergoing processing [50]. The value for this parameter can be determined using Equation (1) [51]:…”

Section: Laser Processing and Parametersmentioning

confidence: 99%

Investigating the Effect of Nanosecond Laser Surface Texturing on Microstructure and Mechanical Properties of AISI 301LN

Rezayat,

Besharatloo,

Mateo

2023

Metals

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This study explores pulsed Nd:YLF laser surface modification (LSM) effects on AISI 301LN stainless steel. Laser-treated surfaces underwent SEM characterization, revealing patterns and irregularities. Higher heat input surfaces showed significant microstructural changes, while lower heat input surfaces experienced less alteration. Increased laser spot overlap led to larger exposed areas and higher heat input, influencing groove width, depth, and surface roughness. Three-dimensional reconstructions illustrated the correlation between laser parameters and surface characteristics. XRD (X-ray diffraction analysis) and EBSD (Electron backscatter diffraction) analyses revealed a transformation from austenite to martensite, with an increase in the α’-martensite phase, particularly in patterns with high laser power, attributed to rapid cooling during laser modification. Grain size analysis indicated a 42% reduction post-treatment, enhancing the surface fraction of fine grains. Hardness measurements demonstrated an overall increase in laser-treated samples, linked to fine-grained microstructure formation, induced residual stresses, and the α’-martensitic phase.

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“…Many studies have been conducted with different sintering methods such as furnace [ 10 ], hotplate [ 11 , 12 , 13 ], laser [ 14 ], electrical [ 15 ], plasma [ 16 , 17 ], microwave sintering [ 18 , 19 ], etc. Studies have investigated the effect of sintering conditions, i.e., sintering temperature [ 20 ], heating time [ 21 , 22 ], parameter configurations [ 22 , 23 , 24 ], etc., on the sintering results. The size of the NP has also been studied as an influential factor on the characteristics of the sintered NPs [ 25 ].…”

Section: Introductionmentioning

confidence: 99%

Physical Characteristics of Sintered Silver Nanoparticle Inks with Different Sizes during Furnace Sintering

Park,

Ryu,

Lee

et al. 2024

Materials

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The influence of nanoparticle (NP) size on the physical characteristics of sintered silver NP ink was studied using four different types of inks. The Ag NP inks were spin-coated on glass substrates with an average thickness of 300 nm. Each sample was sintered for 30 min, with temperatures from 50 °C to 400 °C by an interval of 50 °C. After sintering, the specific resistance of each case was obtained using the resistance and surface profile measurements. The minimum specific resistance obtained by the experiment was 2.6 μΩ·cm in the case in which 50 nm-sized Ag NP ink was sintered at 350 °C. The transformed surface morphology and grain size of each case were observed using scanning electron microscopy and atomic force microscopy. The results of this study can be a reference for future manufacturers in selecting the Ag NP size and the sintering temperature.

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Effect of pulse overlapping on temperature field and physical characteristics in pulsed laser sintering of inkjet-printed silver nanoparticles

Cited by 8 publications

References 49 publications

Deep Learning-Based Inkjet Droplet Detection for Jetting Characterizations and Multijet Synchronization

Deep Learning-Based Inkjet Droplet Detection for Jetting Characterizations and Multijet Synchronization

Investigating the Effect of Nanosecond Laser Surface Texturing on Microstructure and Mechanical Properties of AISI 301LN

Physical Characteristics of Sintered Silver Nanoparticle Inks with Different Sizes during Furnace Sintering

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