Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/144058
Title: A printing quality optimization framework for non-contact ink writing techniques
Authors: Zhang, Haining
Keywords: Engineering::Manufacturing::Flexible manufacturing systems
Engineering::Mechanical engineering
Issue Date: 2020
Publisher: Nanyang Technological University
Source: Zhang, H. (2020). A printing quality optimization framework for non-contact ink writing techniques. Doctoral thesis, Nanyang Technological University, Singapore.
Abstract: Non-contact ink writing techniques are a promising additive manufacturing (AM) technology to fabricate customized, low-cost and flexible electronic devices, while dramatically reducing chemical waste and lowering manufacturing costs. However, the printing quality of non-contact ink writing techniques is still highlighted as the main limitation significantly affecting the electrical performance of printed components. In this research, the author attempts to develop an optimization framework that helps designers improve the printing quality of the non-contact ink writing techniques. In the proposed optimization framework, the following methodologies have been proposed: 1) a novel hybrid machine learning method to determine an optimal operating process window of non-contact ink writing in various design spaces; 2) a statistical method to quantify the conflicting relationship between the printed line roughness and printed line thickness; 3) a Bayesian based approach to investigate the correlations between process parameters and printed line morphology; 4) a hybrid multi-objective optimization approach to optimize the conflicting relationship between the printed line edge roughness and line thickness in 2D and 3D design spaces, respectively; 5) a fast multi-objective optimization approach to optimize the overall printing quality, under the objective of customizing line width, and dual conflicting objectives of minimizing line edge roughness and maximizing line thickness; and 6) a knowledge transfer based method for rapid process modeling of non-contact ink writing techniques under varied operating conditions. To demonstrate the effectiveness of the developed methodology, an emerging and widely adopted non-contact ink writing technology - aerosol jet printing (AJP) is used as case studies throughout this thesis. The results of the case studies show that the proposed framework is beneficial to balance the complex relationship between different process parameters by the identified operating process windows of AJP, hence the lines can be fabricated with better edge definition and lower overspray in a design space. Following that, based on the determined operating process windows, the conflicting relationship between different printed line morphology is further optimized by the proposed optimization framework, thus improving the overall printed line quality. Additionally, rapid process modeling of AJP under varied operating conditions is achieved based on the proposed knowledge transfer method, which is more efficient than traditional modeling approaches in AJP. Despite the limitations of the proposed optimization framework, the implementation of the developed methodology demonstrates a significant improvement in printing process optimization of non-contact ink writing techniques. As the major contribution of this research, the proposed optimization framework provides designers with a guideline in developing customized and high-performance of electrical circuits and components. Moreover, considering its data-driven based characteristics, the proposed framework can be applicable to other process optimization researches in additive manufacturing technologies.
URI: https://hdl.handle.net/10356/144058
DOI: 10.32657/10356/144058
Rights: This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0).
Fulltext Permission: embargo_20251118
Fulltext Availability: With Fulltext
Appears in Collections:MAE Theses

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