Hybrid machine learning method to determine the optimal operating process window in aerosol jet 3D printing

• Main Authors: Zhang, Haining, Moon, Seung Ki, Ngo, Teck Hui
• Other Authors: School of Mechanical and Aerospace Engineering
• Format: Article
• Language: English
• Published: 2021
• Subjects: Engineering::Mechanical engineering; Aerosol Jet Printing; Direct Writing
• Online Access: https://hdl.handle.net/10356/148676
• Institution: Nanyang Technological University

Aerosol jet printing (AJP) is a three-dimensional (3D) noncontact and direct printing technology for fabricating customized microelectronic devices on flexible substrates. Despite the capability of fine feature deposition, the complicated relationship between the main process parameters will affect the printing quality significantly in a design space. In this paper, a novel hybrid machine learning method is proposed to determine the optimal operating process window for the AJP process in various design spaces. The proposed method consists of classic machine learning methods, including experimental sampling, data clustering, classification, and knowledge transfer. In the proposed method, a two-dimensional design space is fully explored by a Latin hypercube sampling experimental design at a certain print speed. Then, the influence of the sheath gas flow rate (SHGFR) and the carrier gas flow rate (CGFR) on the printed line quality is analyzed by a K-means clustering approach, and an optimal operating process window is determined by a support vector machine. To efficiently identify more operating process windows at different print speeds, a transfer learning approach is applied to exploit relatedness between different operating process windows. Hence, at a new print speed, the number of line samples for identifying a new operating process window is greatly reduced. Finally, to balance the complex relationship among SHGFR, CGFR, and print speed, a 3D operating process window is determined by an incremental classification approach. Different from experiment-based approaches adopted in 3D printing technologies for quality optimization, the proposed method is developed based on the theory of knowledge discovery and data mining. Therefore, the knowledge in different design spaces can be fully explored and transferred for printed line quality optimization. Moreover, the data-driven-based characteristics can help the proposed method develop a guideline for quality optimization in other 3D printing technologies.