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Deep learning-based optimization using a limited number of experiments for roll-to-roll UV embossing

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Abstract

Optoelectronic components are becoming more complex and functional, but they must be miniaturized to achieve portability. The roll-to-roll UV embossing process is used to produce a light guide film to replace the light guide plate in a backlight module. To improve replicability, this study uses a deep learning-based optimization (DLBO) system. Deep learning requires a large amount of experimental data, but it is impractical for manufacturing. The DLBO system comprises a data generator, a model generator, and an optimal factor generator. The data generator generates massive labeled data by using a limited number of Taguchi experiments to predict the quality characteristic for each combination. The model generator produces an optimal neural network model that conforms to the input data. The optimal factor generator uses the model that is produced by the model generator as the objective function to determine the optimal factors. Verification experiments show that a DLBO system can successfully predict the height of a microstructure. This system also determines the optimal process conditions. The measured height of the microstructure is 1.27 μm higher than the result with Taguchi optimization, and the replicability is 90%. For the optimal conditions using the DLBO system, the roll-to-roll UV embossed film has the best light guiding properties. The results show that the optimal conditions that are determined using the DLBO system produce light guide films with better optical qualities than the Taguchi method.

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Funding

This work was supported by the Ministry of Science and Technology of Taiwan (MOST 107–2221-E-992–048).

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Authors and Affiliations

  1. Department of Computer Science and Engineering, National Sun Yat-Sen University, Kaohsiung, 804, Taiwan

    Meng-Ting Wu

  2. Department of Mold and Die Engineering, National Kaohsiung University of Science and Technology, Kaohsiung, 807, Taiwan

    Ming-You Wu & Cheng-Hsien Wu

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  1. Meng-Ting Wu
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  2. Ming-You Wu
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Contributions

Conceptualization: Meng-Ting Wu and Cheng-Hsien Wu; methodology: Meng-Ting Wu, Ming-You Wu, and Cheng-Hsien Wu; optimization: Meng-Ting Wu and Ming-You Wu; experiment: Ming-You Wu and Cheng-Hsien Wu; resources: Cheng-Hsien Wu; writing. Meng-Ting Wu.

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Correspondence to Cheng-Hsien Wu.

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Wu, MT., Wu, MY. & Wu, CH. Deep learning-based optimization using a limited number of experiments for roll-to-roll UV embossing. Int J Adv Manuf Technol 120, 5955–5967 (2022). https://doi.org/10.1007/s00170-022-09115-1

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  • DOI: https://doi.org/10.1007/s00170-022-09115-1

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