Predicting particle properties in optical traps with machine learning

Prediction of Particle Properties in Plasma Spraying Based on Machine Learning

Journal of Thermal Spray Technology ◽

10.1007/s11666-021-01239-2 ◽

2021 ◽

Author(s):

K. Bobzin ◽

W. Wietheger ◽

H. Heinemann ◽

S. R. Dokhanchi ◽

M. Rom ◽

...

Keyword(s):

Machine Learning ◽

Plasma Spraying ◽

Process Parameters ◽

Gas Flow ◽

Cfd Simulation ◽

Thermal Spraying ◽

Latin Hypercube Sampling ◽

Electrical Current ◽

Support Vector ◽

Particle Properties

AbstractThermal spraying processes include complex nonlinear interdependencies among process parameters, in-flight particle properties and coating structure. Therefore, employing computer-aided methods is essential to quantify these complex relationships and subsequently enhance the process reproducibility. Typically, classic modeling approaches are pursued to understand these interactions. While these approaches are able to capture very complex systems, the increasingly sophisticated models have the drawback of requiring considerable calculation time. In this study, two different Machine Learning (ML) methods, Residual Neural Network (ResNet) and Support Vector Machine (SVM), were used to estimate the in-flight particle properties in plasma spraying in a much faster manner. To this end, data sets comprising the process parameters such as electrical current and gas flow as well as the in-flight particle velocities, temperatures and positions have been extracted from a CFD simulation of the plasma jet. Furthermore, two Design of Experiments (DOE) methods, Central Composite Design (CCD) and Latin Hypercube Sampling (LHS), have been employed to cover a set of representative process parameters for training the ML models. The results show that the developed ML models are able to estimate the trends of particle properties precisely and dramatically faster than the computation-intensive CFD simulations.

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Uncertainty in counting ice nucleating particles with continuous flow diffusion chambers

Atmospheric Chemistry and Physics ◽

10.5194/acp-17-10855-2017 ◽

2017 ◽

Vol 17 (17) ◽

pp. 10855-10864 ◽

Cited By ~ 21

Author(s):

Sarvesh Garimella ◽

Daniel A. Rothenberg ◽

Martin J. Wolf ◽

Robert O. David ◽

Zamin A. Kanji ◽

...

Keyword(s):

Machine Learning ◽

Continuous Flow ◽

Ice Nucleation ◽

Small Scale ◽

State Variables ◽

Detailed Measurement ◽

Diffusion Chambers ◽

Particle Properties ◽

Machine Learning Approach ◽

Flow Features

Abstract. This study investigates the measurement of ice nucleating particle (INP) concentrations and sizing of crystals using continuous flow diffusion chambers (CFDCs). CFDCs have been deployed for decades to measure the formation of INPs under controlled humidity and temperature conditions in laboratory studies and by ambient aerosol populations. These measurements have, in turn, been used to construct parameterizations for use in models by relating the formation of ice crystals to state variables such as temperature and humidity as well as aerosol particle properties such as composition and number. We show here that assumptions of ideal instrument behavior are not supported by measurements made with a commercially available CFDC, the SPectrometer for Ice Nucleation (SPIN), and the instrument on which it is based, the Zurich Ice Nucleation Chamber (ZINC). Non-ideal instrument behavior, which is likely inherent to varying degrees in all CFDCs, is caused by exposure of particles to different humidities and/or temperatures than predicated from instrument theory of operation. This can result in a systematic, and variable, underestimation of reported INP concentrations. We find here variable correction factors from 1.5 to 9.5, consistent with previous literature values. We use a machine learning approach to show that non-ideality is most likely due to small-scale flow features where the aerosols are combined with sheath flows. Machine learning is also used to minimize the uncertainty in measured INP concentrations. We suggest that detailed measurement, on an instrument-by-instrument basis, be performed to characterize this uncertainty.

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Illuminating the complex behaviour of particles in optical traps with machine learning

Emerging Topics in Artificial Intelligence 2020 ◽

10.1117/12.2566942 ◽

2020 ◽

Author(s):

Isaac C. Lenton ◽

Giovanni Volpe ◽

Alexander B. Stilgoe ◽

Timo A. Nieminen ◽

Halina Rubinsztein-Dunlop

Keyword(s):

Machine Learning ◽

Optical Traps ◽

Complex Behaviour

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Mind wandering as data augmentation: How mental travel supports abstraction

Behavioral and Brain Sciences ◽

10.1017/s0140525x1900311x ◽

2020 ◽

Vol 43 ◽

Author(s):

Myrthe Faber

Keyword(s):

Machine Learning ◽

Data Augmentation ◽

Mental Content ◽

Mind Wandering ◽

Theoretical Framework ◽

Important Addition

Abstract Gilead et al. state that abstraction supports mental travel, and that mental travel critically relies on abstraction. I propose an important addition to this theoretical framework, namely that mental travel might also support abstraction. Specifically, I argue that spontaneous mental travel (mind wandering), much like data augmentation in machine learning, provides variability in mental content and context necessary for abstraction.

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