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Comparison and Identification of Optimal Machine Learning Model for Rapid Optimization of Printed Line Characteristics of Aerosol Jet Printing Technology

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Abstract

Among the various direct-write (DW) techniques, aerosol jet printing (AJP) has the advantages of high resolution (~ 10 μm) and flexible working distance (2-5 mm). On this basis, it has emerged as a promising DW technology to precisely customize complex electrical functional devices. However, the micro-electronic devices fabricated using AJP suffer from low electrical performance because of inferior printed line geometric characteristics. Specifically, high edge roughness lines are detrimental to the uniformity of the formed electrical functional devices. In addition, the low controllability of the printed line width may induce overlap of narrowly spaced circuits or unnecessary intertrack voids, which will hinder the wide application of AJP technology in advanced electronic manufacturing industry. Therefore, ensuring high precision of the line width and low edge roughness is of primary importance for AJP technology. In this research, a machine learning framework is proposed for rapid optimization of printed line characteristics. In the proposed framework, SHGFR and CGFR were considered as input variables, and line width and line roughness were taken as the target responses. Three representative machine learning algorithms, tree-based random forest regression, kernel-based support vector machine, and Bayesian-based Gaussian process regression, were then adopted for model development. Subsequently, the identified optimal machine learning model was integrated with a NSGA-III for rapid optimization of printed line characteristics, and experiments validated the effectiveness of the adopted approach.

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Data is made available on request.

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Acknowledgements

This research work was conducted in the Intelligent Manufacturing Laboratory with funding support from Suzhou University (No. 2021XJPT51, No. 2021yzd08, No. 2021BSK023, No. 2019xjzdxk1). This research was also supported by the Nano & Material Technology Development Program through the National Research Foundation of Korea (NRF) funded by Ministry of Science and ICT (No. 2021M3D1A2047721) and the Basic Research Program funded by the Korea Institute of Machinery & Materials (KIMM) (No. NK242J).

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Authors and Affiliations

  1. School of Information Engineering, Suzhou University, Suzhou, 234000, China

    Mingdong Li & Haining Zhang

  2. School of Mechanical and Aerospace, Nanyang Technological University, Singapore, 639798, Singapore

    Zhixin Liu, Shuai Yin & Haining Zhang

  3. Department of 3D Printing, Korea Institute of Machinery & Materials, Daejeon, 34103, Republic of Korea

    Joon Phil Choi

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  1. Mingdong Li
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Contributions

ML: Methodology, Investigation, Writing—original draft. HZ: Methodology, Investigation. JPC: Methodology, Writing—review & editing. ZL: Conceptualization, Methodology, Writing—original draft. SY: Writing—original draft, Writing—review & editing.

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Correspondence to Joon Phil Choi or Haining Zhang.

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Appendix A

Appendix A

The implementation of LHS and the corresponding experimental points are described in the following appendix.

If the design space is a unit hypercube \({\left[\mathrm{0,1}\right]}^{\mathrm{K}}\), the experimental points based on LHS can be directly obtained by implementing the following Matlab function

$${\text{X }} = {\text{ lhsdesign}}\left( {{\text{N}},{\text{ K}}} \right)$$
(A1)

where N is the number of designed experimental points, K is the dimension of the sampled unit hypercube, X is a returned N×K matrix containing the designed experimental points.

In this research, as the design space is not a unit hypercube, normalization is required before the LHS experimental design can be implemented, followed by projecting the obtained points back to the original design space. The table below shows the designed experimental points using LHS in this study (Table A1).

Table A1 The designed experiments based on LHS
Full size table

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Li, M., Liu, Z., Yin, S. et al. Comparison and Identification of Optimal Machine Learning Model for Rapid Optimization of Printed Line Characteristics of Aerosol Jet Printing Technology. Int. J. of Precis. Eng. and Manuf.-Green Tech. 11, 71–87 (2024). https://doi.org/10.1007/s40684-023-00528-1

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