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Aerosol jet printing is a promising technology for printing functional materials on a variety of substrates with high precision and resolution. This technology has the potential to revolutionize the manufacturing industry by providing a low‐cost, high‐resolution printing technique that can be used to produce additively printed electronics, sensors, and energy devices. However, the optimization of this process has traditionally relied on time‐consuming trial‐and‐error methods, hampering its efficiency and scalability. Machine learning (ML) models have the potential to overcome these challenges and improve the quality, speed, and efficiency of the printing process. In this paper, we propose an approach that leverages machine learning (ML) algorithms to streamline and enhance the aerosol jet printing optimization process. Our methodology involves data collection through systematic experimentation with various parameter settings. This dataset serves as the foundation for training different ML model capable of predicting printed line characteristics and optimal printing process parameter. We validate our approach by performing experiments on different inks, and we compare the results of our ML‐based optimization approach to those obtained using traditional trial‐and‐error methods. The results demonstrate that our approach offers significantly higher accuracy and efficiency. To enhance our approach's accessibility and ease of use, we incorporate AutoML techniques which automates the process of selecting the most suitable ML algorithms and hyperparameters, reducing the burden of manual configuration. Furthermore, we introduce a user‐friendly web‐based interface that facilitates the entire ML pipeline, from data preprocessing to prediction and batch processing. This interface empowers users to efficiently manage and manipulate their data, select appropriate ML algorithms, and execute predictions, ultimately improving accuracy and model performance.This article is protected by copyright. All rights reserved.
Aerosol jet printing is a promising technology for printing functional materials on a variety of substrates with high precision and resolution. This technology has the potential to revolutionize the manufacturing industry by providing a low‐cost, high‐resolution printing technique that can be used to produce additively printed electronics, sensors, and energy devices. However, the optimization of this process has traditionally relied on time‐consuming trial‐and‐error methods, hampering its efficiency and scalability. Machine learning (ML) models have the potential to overcome these challenges and improve the quality, speed, and efficiency of the printing process. In this paper, we propose an approach that leverages machine learning (ML) algorithms to streamline and enhance the aerosol jet printing optimization process. Our methodology involves data collection through systematic experimentation with various parameter settings. This dataset serves as the foundation for training different ML model capable of predicting printed line characteristics and optimal printing process parameter. We validate our approach by performing experiments on different inks, and we compare the results of our ML‐based optimization approach to those obtained using traditional trial‐and‐error methods. The results demonstrate that our approach offers significantly higher accuracy and efficiency. To enhance our approach's accessibility and ease of use, we incorporate AutoML techniques which automates the process of selecting the most suitable ML algorithms and hyperparameters, reducing the burden of manual configuration. Furthermore, we introduce a user‐friendly web‐based interface that facilitates the entire ML pipeline, from data preprocessing to prediction and batch processing. This interface empowers users to efficiently manage and manipulate their data, select appropriate ML algorithms, and execute predictions, ultimately improving accuracy and model performance.This article is protected by copyright. All rights reserved.
Recently, there has been interest in applying Additive Manufacturing (AM) to RF and Microwave applications, especially in packaging of microelectronic devices. Additive packaging offers advantages of expanded functionality in restricted volume, through miniature, low-SWaP-C sensors, allowing for non-traditional form factors. In this work, design, fabrication, and characterization of a non-planar multi-material MMIC structure is presented to demonstrate significant footprint reduction and circuit compaction. Performance of a non-planar passive RF device and details of planar to non-planar AM connections for characterization of the transitions will be demonstrated. The work will present details of the design and fabrication of the non-planar structure, optimization of the process parameters for the Optomec 5-axis Aerosol Jet Printer for multi-material printing, and the results of the characterization and testing. RF measurements were conducted to demonstrate the functionality of the developed non-planar circuit and compared with simulations which showed good agreement. The results of this work show that fully additive approach is feasible for non-planar circuits, which will allow for footprint reduction, weight reduction, and achievement of novel form factors that are critical for microwave applications.
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