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dc.contributor.authorZhang, Hainingen_US
dc.identifier.citationZhang, H. (2020). A printing quality optimization framework for non-contact ink writing techniques. Doctoral thesis, Nanyang Technological University, Singapore.en_US
dc.description.abstractNon-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.en_US
dc.publisherNanyang Technological Universityen_US
dc.rightsThis work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0).en_US
dc.subjectEngineering::Manufacturing::Flexible manufacturing systemsen_US
dc.subjectEngineering::Mechanical engineeringen_US
dc.titleA printing quality optimization framework for non-contact ink writing techniquesen_US
dc.typeThesis-Doctor of Philosophyen_US
dc.contributor.supervisorMoon Seung Kien_US
dc.contributor.schoolSchool of Mechanical and Aerospace Engineeringen_US
dc.description.degreeDoctor of Philosophyen_US
dc.contributor.organizationNational Research Foundation (NRF), SMRTen_US
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