Applications and prospects of machine learning for aerosol jet printing: A review

Abstract

Aerosol Jet Printing (AJP) is an additive manufacturing process that deposits ink-like materials suspended as an aerosol mist. AJP creates three-dimensional (3D) functional structures onto flat or conformal surfaces in complex shapes without the aid of additional tooling, enabling the manufacturing of extremely fine electrical interconnects with freeform structures. Due to the novelty and complexity of AJP, physical understanding is rather limited, hindering physics-based process modeling and analysis. Fortunately, the data resources from AJP applications, e.g., 3D Computer-Aided-Design data, Standard Triangle Language files, in-situ images of part, and nozzle motion records, provide an unparalleled opportunity for developing data-driven, Machine Learning (ML) methods to characterize AJP processes, support process control, and facilitate product improvement. To thoroughly identify the newfound opportunities, this study reviews state-of-the-art ML methods used in AJP applications, investigates open issues in AJP, and outlooks future development of ML-based research topics for AJP. It sheds light on how to maximize the value of ML on AJP data to develop scalable, generalizable decision-making methods. More future works along the direction will be motivated.

Keywords:

• Aerosol jet printing
• data-driven
• machine learning
• process control
• quality improvement

Data availability statement

Data sharing is not applicable to this article as no new data were created or analyzed in this study.

Additional information

Funding

This research is supported by Arizona State University startup funds, CC1379 PG14416 and CC1379 PG14421.

Notes on contributors

Shenghan Guo

Dr. Shenghan Guo is an assistant professor in The School of Manufacturing Systems and Networks at Arizona State University. Her research interests include statistical process monitoring, data mining, interpretable machine learning, and their applications in smart manufacturing. She is expertised with real data from manufacturing applications that possess complex properties, e.g., in-situ thermal video, multi-sensory data streams. Her recent projects focus on data-driven predictive analysis and knowledge mining in high-resolution 3D printing processes.

Hyunwoong Ko

Dr. Hyunwoong Ko is an assistant professor in the School of Manufacturing Systems and Networks of the Ira A. Fulton Schools of Engineering at Arizona State University. He completed his PhD in the School of Mechanical and Aerospace Engineering at Nanyang Technological University in September 2019 and worked at National Institute of Standards and Technology during his PhD and Postdoctoral studies as a research associate until September 2021. His research is focused on data science, manufacturing science, and design science as well as the intersections between these three areas. His research aims at building foundations of Artificial Intelligence (AI) and digitization for manufacturing and design.

Andi Wang

Dr. Andi Wang obtained his PhD from Georgia Institute of Technology and is currently an assistant professor at Arizona State University. His research focuses on the intersection of data science and manufacturing systems. His research involves data-driven root-cause diagnostics, monitoring, design optimization, prediction for complex, interconnected, and intelligent systems. His papers received five best paper awards or finalists from various conferences and one best paper award from IISE Transactions.