Scientific Reports (Feb 2022)

Sample-efficient parameter exploration of the powder film drying process using experiment-based Bayesian optimization

  • Kohei Nagai,
  • Takayuki Osa,
  • Gen Inoue,
  • Takuya Tsujiguchi,
  • Takuto Araki,
  • Yoshiyuki Kuroda,
  • Morio Tomizawa,
  • Keisuke Nagato

DOI
https://doi.org/10.1038/s41598-022-05784-w
Journal volume & issue
Vol. 12, no. 1
pp. 1 – 11

Abstract

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Abstract Parameter optimization is a long-standing challenge in various production processes. Particularly, powder film forming processes entail multiscale and multiphysical phenomena, each of which is usually controlled by a combination of several parameters. Therefore, it is difficult to optimize the parameters either by numerical-model-based analysis or by “brute force” experiment-based exploration. In this study, we focus on a Bayesian optimization method that has led to breakthroughs in materials informatics. Specifically, we apply this method to exploration of production-process-parameter for the powder film forming process. To this end, a slurry containing a powder, polymer, and solvent was dropped, the drying temperature and time were controlled as parameters to be explored, and the uniformity of the fabricated film was evaluated. Using this experiment-based Bayesian optimization system, we searched for the optimal parameters among 32,768 (85) parameter sets to minimize defects. This optimization converged at 40 experiments, which is a substantially smaller number than that observed in brute-force exploration and traditional design-of-experiments methods. Furthermore, we inferred the mechanism corresponding to the unknown drying conditions discovered in the parameter exploration that resulted in uniform film formation. This demonstrates that a data-driven approach leads to high-throughput exploration and the discovery of novel parameters, which inspire further research.