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

Abstract

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.

Application of machine learning in polymer additive manufacturing: A review

“…In contrast to parametric models, non-parametric models do not assume a predefined mapping or distribution for a given problem, which makes them more flexible, adapting to the characteristics of the data. Common non-parametric techniques used in polymer AM research include decision trees (DT), 135 K-nearest neighbors (KNN), 136 K-means clustering (KMC), 137 Gaussian process (GP), 138 principal component analysis (PCA), 139 and t-distributed stochastic neighbor embedding (t-SNE). 125 KMC, PCA, and t-SNE are unsupervised ML techniques, while DT and KNN are supervised ML techniques.…”

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

“…They pointed out the successful applications of response surface methods (RSMs), genetic algorithms (GAs), and artificial neural networks (ANNs) in industry. Zhang et al proposed a hybrid machine learning method based on deposited droplet morphology to analyze and optimize AJP processes. The technique included classical machine learning methods, such as space-filling-based experimental design, clustering, classification, regression, and multi-objective optimization.…”

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

“…At the same time, machine learning-based image processing can enable classification, object detection, and tracking in applications where real-time assessment is essential. − Convolutional neural networks (CNN) have made it possible to deal with image data with high efficiency, using shared weights and maintaining a structure with highly correlated local features between adjacent pixels. , Recently, this has been applied, inter alia, for classification and object detection on microfluidic systems. ,, Doing so has allowed nonexperts to evaluate microfluidics processes or sensor readouts for real-time assessments. In addition, there have been efforts for experts to optimize aerosol jet printing , and inkjet printing along with labeling of inkjet printed patterns − using machine learning-based methods. However, the aforementioned previous research lacks comprehensive assessments, from droplet watching to printout demonstration.…”