Predicting Pressure Sensitivity to Luminophore Content and Paint Thickness of Pressure-Sensitive Paint Using Artificial Neural Network.

Mitsugu Hasegawa, Daiki Kurihara, Yasuhiro Egami, Hirotaka Sakaue, Aleksandar Jemcov

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Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 authors.

Mitsugu HasegawaDepartment of Aerospace and Mechanical Engineering, University of Notre Dame, Notre Dame, IN 46556, USA.ORCID 0000-0002-6742-4305

Daiki KuriharaDepartment of Aerospace and Mechanical Engineering, University of Notre Dame, Notre Dame, IN 46556, USA.

Yasuhiro EgamiDepartment of Mechanical Engineering, Aichi Institute of Technology, 1247 Yachigusa, Yakusa-cho, Toyota 470-0392, Aichi, Japan.ORCID 0000-0003-1519-3311

Hirotaka SakaueDepartment of Aerospace and Mechanical Engineering, University of Notre Dame, Notre Dame, IN 46556, USA.ORCID 0000-0002-5420-280X

Aleksandar JemcovDepartment of Aerospace and Mechanical Engineering, University of Notre Dame, Notre Dame, IN 46556, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

An artificial neural network (ANN) was constructed and trained for predicting pressure sensitivity using an experimental dataset consisting of luminophore content and paint thickness as chemical and physical inputs. A data augmentation technique was used to increase the number of data points based on the limited experimental observations. The prediction accuracy of the trained ANN was evaluated by using a metric, mean absolute percentage error. The ANN predicted pressure sensitivity to luminophore content and to paint thickness, within confidence intervals based on experimental errors. The present approach of applying ANN and the data augmentation has the potential to predict pressure-sensitive paint (PSP) characterizations that improve the performance of PSP for global surface pressure measurements.

Indexed as

Neural Networks, ComputerPaintHumansartificial neural networkdata augmentationmachine learningpressure-sensitive paint

Identifiers

PMID34372426

DOI10.3390/s21155188

PMCPMC8347181

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