Deep-Learning-Based Microfluidic Droplet Classification for Multijet Monitoring

Self Cite

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.

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Choi,

et al. 2024

Self Cite

“…Deep learning techniques illustrate the overwhelming capability of solving complex problems in additive manufacturing processes. Choi et al 14 pioneered the introduction of the MobileNetV2 network to monitor the IJP process, enabling the classification of droplet Particular attention should still be spent on detecting defects in prints guided by optimal parameters. Object detection and IJP are critical for producing printed electronics with zero defects.…”

Section: Introductionmentioning

confidence: 99%

“…Deep learning techniques illustrate the overwhelming capability of solving complex problems in additive manufacturing processes. Choi et al pioneered the introduction of the MobileNetV2 network to monitor the IJP process, enabling the classification of droplet morphology for improved inkjet print quality. Mohamed et al reviewed the research on optimization techniques used in fused deposition modeling techniques.…”

Section: Introductionmentioning

confidence: 99%

Deep-Learning-Guided Evaluation Method for the High-Volume Preparation of Flexible Sensors Based on Inkjet Printing

Yao,

Wang,

Wang

et al. 2024

Flexible sensors for application in various industries, including biomedicine and wearable electronics, are frequently made using silver nanoparticle (AgNP) inks and inkjet printing (IJP) technology. Inkjet-printed flexible electronic devices are made up of many printed lines that run parallel to each other, and the surface morphology of the printed lines and the interline state directly impact the electrical conductivity of the electronic devices. This paper describes the experimental setup for IJP, the definition of print line characteristics, and common unavoidable defects. Conductivity and physical defects are considered in defining the print line quality assessment. In addition, two prediction models of flexible sensors before batch printing and a model for detecting defects after printing are provided. The predictive models can guide actions, leading to a print success rate of over 80%. We build the defect detection model using a neural architecture search because manually fine-tuning neural networks for reference is challenging. Finally, a target detection model with a mAP@0.5 of 81.2% is built in just 0.77 graphics processing unit (GPU) days. The model takes only 4.6 ms to detect an image, satisfying the real-time monitoring needs. At the same time, an accuracy of 95.5% can be achieved in the test data set. This work provides a new idea for the high-volume preparation of flexible sensors.

“…Therefore, it has the potential for efficient process optimization in various additive manufacturing (AM) systems. , Specifically, machine learning usually establishes cause–effect correlations between process parameters and the printing quality of AM in a data-driven manner to achieve effective process modeling and optimization. For example, based on different machine learning methods, the influence of main inkjet printing − /extrusion printing − process parameters on droplet/line behaviors was investigated and optimized, which will be beneficial to the wide application of inkjet printing/extrusion printing technology in the field of printed electronics and bioprinting. In recent years, some representative machine learning methods have been introduced into AJP to optimize printing quality.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Enables Process Optimization of Aerosol Jet 3D Printing Based on the Droplet Morphology

Zhang

Hong

Chen

et al. 2023

Aerosol jet printing (AJP) is a promising noncontact direct ink writing technology that enables flexible and conformal electronic devices to be fabricated onto planar and nonplanar substrates with higher resolution and less waste. Despite possessing many advantages, the limited electrical performance of microelectronic devices caused by the poor printing quality is still the greatest hurdle to overcome for AJP technology. With the motivation to improve the printing quality, a novel hybrid machine learning method is proposed to analyze and optimize the AJP process based on the deposited droplet morphology in this study. The proposed method consists of classic machine learning approaches, including space-filling-based experimental design, clustering, classification, regression, and multiobjective optimization. In the proposed method, a two-dimensional (2D) design space is fully explored using a Latin hypercube sampling approach for experimental design, and a K-means clustering approach is employed to reveal the cause–effect relationship between the deposited droplet morphology and printed line characteristics. Following that, an optimal operating window with respect to the deposited droplet morphology is identified using a support vector machine to ensure the printing quality in a design space. Finally, to achieve high-controllability and sufficient-thickness droplets, Gaussian process regression is adopted to develop the process model of droplet geometrical properties, and the deposited droplet morphology is optimized under dual conflicting objectives of customizing the droplet diameter and maximizing droplet thickness. Different from previous printing quality optimization approaches, the proposed method enables a systemic investigation on the formation mechanisms of printed line characteristics, and the printing quality is fundamentally optimized based on the deposited droplet morphology. Moreover, data-driven-based characteristics can help the proposed approach serve as a guideline for printing quality optimization in other noncontact direct ink writing technologies.